Linux Parallel Processing HOWTO
Hank Dietz,
[email protected]
v980105, 5 January 1998
Parallel Processing refers to the concept of speeding-up the execution
of a program by dividing the program into multiple fragments that can
execute simultaneously, each on its own processor. A program being
executed across N processors might execute N times faster than it
would using a single processor. This document discusses the four
basic approaches to parallel processing that are available to Linux
users: SMP Linux systems, clusters of networked Linux systems, paral-
lel execution using multimedia instructions (i.e., MMX), and attached
(parallel) processors hosted by a Linux system.
______________________________________________________________________
Table of Contents
1. Introduction
1.1 Is Parallel Processing What I Want?
1.2 Terminology
1.3 Example Algorithm
1.4 Organization Of This Document
2. SMP Linux
2.1 SMP Hardware
2.1.1 Does each processor have its own L2 cache?
2.1.2 Bus configuration?
2.1.3 Memory interleaving and DRAM technologies?
2.2 Introduction To Shared Memory Programming
2.2.1 Shared Everything Vs. Shared Something
2.2.1.1 Shared Everything
2.2.1.2 Shared Something
2.2.2 Atomicity And Ordering
2.2.3 Volatility
2.2.4 Locks
2.2.5 Cache Line Size
2.2.6 Linux Scheduler Issues
2.3 bb_threads
2.4 LinuxThreads
2.5 System V Shared Memory
2.6 Memory Map Call
3. Clusters Of Linux Systems
3.1 Why A Cluster?
3.2 Network Hardware
3.2.1 ArcNet
3.2.2 ATM
3.2.3 CAPERS
3.2.4 Ethernet
3.2.5 Ethernet (Fast Ethernet)
3.2.6 Ethernet (Gigabit Ethernet)
3.2.7 FC (Fibre Channel)
3.2.8 FireWire (IEEE 1394)
3.2.9 HiPPI And Serial HiPPI
3.2.10 IrDA (Infrared Data Association)
3.2.11 Myrinet
3.2.12 Parastation
3.2.13 PLIP
3.2.14 SCI
3.2.15 SCSI
3.2.16 ServerNet
3.2.17 SHRIMP
3.2.18 SLIP
3.2.19 TTL_PAPERS
3.2.20 USB (Universal Serial Bus)
3.2.21 WAPERS
3.3 Network Software Interface
3.3.1 Sockets
3.3.1.1 UDP Protocol (SOCK_DGRAM)
3.3.1.2 TCP Protocol (SOCK_STREAM)
3.3.2 Device Drivers
3.3.3 User-Level Libraries
3.4 PVM (Parallel Virtual Machine)
3.5 MPI (Message Passing Interface)
3.6 AFAPI (Aggregate Function API)
3.7 Other Cluster Support Libraries
3.7.1 Condor (process migration support)
3.7.2 DFN-RPC (German Research Network - Remote Procedure Call)
3.7.3 DQS (Distributed Queueing System)
3.8 General Cluster References
3.8.1 Beowulf
3.8.2 Linux/AP+
3.8.3 Locust
3.8.4 Midway DSM (Distributed Shared Memory)
3.8.5 Mosix
3.8.6 NOW (Network Of Workstations)
3.8.7 Parallel Processing Using Linux
3.8.8 Pentium Pro Cluster Workshop
3.8.9 TreadMarks DSM (Distributed Shared Memory)
3.8.10 U-Net (User-level NETwork interface architecture)
3.8.11 WWT (Wisconsin Wind Tunnel)
4. SIMD Within A Register (e.g., using MMX)
4.1 SWAR: What Is It Good For?
4.2 Introduction To SWAR Programming
4.2.1 Polymorphic Operations
4.2.2 Partitioned Operations
4.2.2.1 Partitioned Instructions
4.2.2.2 Unpartitioned Operations With Correction Code
4.2.2.3 Controlling Field Values
4.2.3 Communication & Type Conversion Operations
4.2.4 Recurrence Operations (Reductions, Scans, etc.)
4.3 MMX SWAR Under Linux
5. Linux-Hosted Attached Processors
5.1 A Linux PC Is A Good Host
5.2 Did You DSP That?
5.3 FPGAs And Reconfigurable Logic Computing
6. Of General Interest
6.1 Programming Languages And Compilers
6.1.1 Fortran 66/77/PCF/90/HPF/95
6.1.2 GLU (Granular Lucid)
6.1.3 Jade And SAM
6.1.4 Mentat And Legion
6.1.5 MPL (MasPar Programming Language)
6.1.6 PAMS (Parallel Application Management System)
6.1.7 Parallaxis-III
6.1.8 pC++/Sage++
6.1.9 SR (Synchronizing Resources)
6.1.10 ZPL And IronMan
6.2 Performance Issues
6.3 Conclusion - It's Out There
______________________________________________________________________
1. Introduction
Parallel Processing refers to the concept of speeding-up the execution
of a program by dividing the program into multiple fragments that can
execute simultaneously, each on its own processor. A program being
executed across n processors might execute n times faster than it
would using a single processor.
Traditionally, multiple processors were provided within a specially
designed "parallel computer"; along these lines, Linux now supports
SMP systems (often sold as "servers") in which multiple processors
share a single memory and bus interface within a single computer. It
is also possible for a group of computers (for example, a group of PCs
each running Linux) to be interconnected by a network to form a
parallel-processing cluster. The third alternative for parallel
computing using Linux is to use the multimedia instruction extensions
(i.e., MMX) to operate in parallel on vectors of integer data.
Finally, it is also possible to use a Linux system as a "host" for a
specialized attached parallel processing compute engine. All these
approaches are discussed in detail in this document.
1.1. Is Parallel Processing What I Want?
Although use of multiple processors can speed-up many operations, most
applications cannot yet benefit from parallel processing. Basically,
parallel processing is appropriate only if:
o Your application has enough parallelism to make good use of
multiple processors. In part, this is a matter of identifying
portions of the program that can execute independently and
simultaneously on separate processors, but you will also find that
some things that could execute in parallel might actually slow
execution if executed in parallel using a particular system. For
example, a program that takes four seconds to execute within a
single machine might be able to execute in only one second of
processor time on each of four machines, but no speedup would be
achieved if it took three seconds or more for these machines to
coordinate their actions.
o Either the particular application program you are interested in
already has been parallelized (rewritten to take advantage of
parallel processing) or you are willing to do at least some new
coding to take advantage of parallel processing.
o You are interested in researching, or at least becoming familiar
with, issues involving parallel processing. Parallel processing
using Linux systems isn't necessarily difficult, but it is not
familiar to most computer users, and there isn't any book called
"Parallel Processing for Dummies"... at least not yet. This HOWTO
is a good starting point, not all you need to know.
The good news is that if all the above are true, you'll find that
parallel processing using Linux can yield supercomputer performance
for some programs that perform complex computations or operate on
large data sets. What's more, it can do that using cheap hardware...
which you might already own. As an added bonus, it is also easy to
use a parallel Linux system for other things when it is not busy
executing a parallel job.
If parallel processing is not what you want, but you would like to
achieve at least a modest improvement in performance, there are still
things you can do. For example, you can improve performance of
sequential programs by moving to a faster processor, adding memory,
replacing an IDE disk with fast wide SCSI, etc. If that's all you are
interested in, jump to section 6.2; otherwise, read on.
1.2. Terminology
Although parallel processing has been used for many years in many
systems, it is still somewhat unfamiliar to most computer users.
Thus, before discussing the various alternatives, it is important to
become familiar with a few commonly used terms.
SIMD:
SIMD (Single Instruction stream, Multiple Data stream) refers to
a parallel execution model in which all processors execute the
same operation at the same time, but each processor is allowed
to operate upon its own data. This model naturally fits the
concept of performing the same operation on every element of an
array, and is thus often associated with vector or array
manipulation. Because all operations are inherently
synchronized, interactions among SIMD processors tend to be
easily and efficiently implemented.
MIMD:
MIMD (Multiple Instruction stream, Multiple Data stream) refers
to a parallel execution model in which each processor is
essentially acting independently. This model most naturally
fits the concept of decomposing a program for parallel execution
on a functional basis; for example, one processor might update a
database file while another processor generates a graphic
display of the new entry. This is a more flexible model than
SIMD execution, but it is achieved at the risk of debugging
nightmares called race conditions, in which a program may
intermittently fail due to timing variations reordering the
operations of one processor relative to those of another.
SPMD:
SPMD (Single Program, Multiple Data) is a restricted version of
MIMD in which all processors are running the same program.
Unlike SIMD, each processor executing SPMD code may take a
different control flow path through the program.
Communication Bandwidth:
The bandwidth of a communication system is the maximum amount of
data that can be transmitted in a unit of time... once data
transmission has begun. Bandwidth for serial connections is
often measured in baud or bits/second (b/s), which generally
correspond to 1/10 to 1/8 that many Bytes/second (B/s). For
example, a 1,200 baud modem transfers about 120 B/s, whereas a
155 Mb/s ATM network connection is nearly 130,000 times faster,
transferring about about 17 MB/s. High bandwidth allows large
blocks of data to be transferred efficiently between processors.
Communication Latency:
The latency of a communication system is the minimum time taken
to transmit one object, including any send and receive software
overhead. Latency is very important in parallel processing
because it determines the minimum useful grain size, the minimum
run time for a segment of code to yield speed-up through
parallel execution. Basically, if a segment of code runs for
less time than it takes to transmit its result value (i.e.,
latency), executing that code segment serially on the processor
that needed the result value would be faster than parallel
execution; serial execution would avoid the communication
overhead.
Message Passing:
Message passing is a model for interactions between processors
within a parallel system. In general, a message is constructed
by software on one processor and is sent through an
interconnection network to another processor, which then must
accept and act upon the message contents. Although the overhead
in handling each message (latency) may be high, there are
typically few restrictions on how much information each message
may contain. Thus, message passing can yield high bandwidth
making it a very effective way to transmit a large block of data
from one processor to another. However, to minimize the need
for expensive message passing operations, data structures within
a parallel program must be spread across the processors so that
most data referenced by each processor is in its local memory...
this task is known as data layout.
Shared Memory:
Shared memory is a model for interactions between processors
within a parallel system. Systems like the multi-processor
Pentium machines running Linux physically share a single memory
among their processors, so that a value written to shared memory
by one processor can be directly accessed by any processor.
Alternatively, logically shared memory can be implemented for
systems in which each processor has it own memory by converting
each non-local memory reference into an appropriate inter-
processor communication. Either implementation of shared memory
is generally considered easier to use than message passing.
Physically shared memory can have both high bandwidth and low
latency, but only when multiple processors do not try to access
the bus simultaneously; thus, data layout still can seriously
impact performance, and cache effects, etc., can make it
difficult to determine what the best layout is.
Aggregate Functions:
In both the message passing and shared memory models, a
communication is initiated by a single processor; in contrast,
aggregate function communication is an inherently parallel
communication model in which an entire group of processors act
together. The simplest such action is a barrier
synchronization, in which each individual processor waits until
every processor in the group has arrived at the barrier. By
having each processor output a datum as a side-effect of
reaching a barrier, it is possible to have the communication
hardware return a value to each processor which is an arbitrary
function of the values collected from all processors. For
example, the return value might be the answer to the question
"did any processor find a solution?" or it might be the sum of
one value from each processor. Latency can be very low, but
bandwidth per processor also tends to be low. Traditionally,
this model is used primarily to control parallel execution
rather than to distribute data values.
Collective Communication:
This is another name for aggregate functions, most often used
when referring to aggregate functions that are constructed using
multiple message-passing operations.
SMP:
SMP (Symmetric Multi-Processor) refers to the operating system
concept of a group of processors working together as peers, so
that any piece of work could be done equally well by any
processor. Typically, SMP implies the combination of MIMD and
shared memory. In the IA32 world, SMP generally means compliant
with MPS (the Intel MultiProcessor Specification); in the
future, it may mean "Slot 2"....
SWAR:
SWAR (SIMD Within A Register) is a generic term for the concept
of partitioning a register into multiple integer fields and
using register-width operations to perform SIMD-parallel
computations across those fields. Given a machine with k-bit
registers, data paths, and function units, it has long been
known that ordinary register operations can function as SIMD
parallel operations on as many as n, k/n-bit, field values.
Although this type of parallelism can be implemented using
ordinary integer registers and instructions, many high-end
microprocessors have recently added specialized instructions to
enhance the performance of this technique for multimedia-
oriented tasks. In addition to the Intel/AMD/Cyrix MMX
(MultiMedia eXtensions), there are: Digital Alpha MAX
(MultimediA eXtensions), Hewlett-Packard PA-RISC MAX (Multimedia
Acceleration eXtensions), MIPS MDMX (Digital Media eXtension,
pronounced "Mad Max"), and Sun SPARC V9 VIS (Visual Instruction
Set). Aside from the three vendors who have agreed on MMX, all
of these instruction set extensions are roughly comparable, but
mutually incompatible.
Attached Processors:
Attached processors are essentially special-purpose computers
that are connected to a host system to accelerate specific types
of computation. For example, many video and audio cards for PCs
contain attached processors designed, respectively, to
accelerate common graphics operations and audio DSP (Digital
Signal Processing). There is also a wide range of attached
array processors, so called because they are designed to
accelerate arithmetic operations on arrays. In fact, many
commercial supercomputers are really attached processors with
workstation hosts.
RAID:
RAID (Redundant Array of Inexpensive Disks) is a simple
technology for increasing both the bandwidth and reliability of
disk I/O. Although there are many different variations, all
have two key concepts in common. First, each data block is
striped across a group of n+k disk drives such that each drive
only has to read or write 1/n of the data... yielding n times
the bandwidth of one drive. Second, redundant data is written
so that data can be recovered if a disk drive fails; this is
important because otherwise if any one of the n+k drives were to
fail, the entire file system could be lost. A good overview of
RAID in general is given at <
http://www.dpt.com/uraiddoc.html>,
and information about RAID options for Linux systems is at
<
http://linas.org/linux/raid.html>. Aside from specialized RAID
hardware support, Linux also supports software RAID 0, 1, 4, and
5 across multiple disks hosted by a single Linux system; see the
Software RAID mini-HOWTO and the Multi-Disk System Tuning mini-
HOWTO for details. RAID across disk drives on multiple machines
in a cluster is not directly supported.
IA32:
IA32 (Intel Architecture, 32-bit) really has nothing to do with
parallel processing, but rather refers to the class of
processors whose instruction sets are generally compatible with
that of the Intel 386. Basically, any Intel x86 processor after
the 286 is compatible with the 32-bit flat memory model that
characterizes IA32. AMD and Cyrix also make a multitude of
IA32-compatible processors. Because Linux evolved primarily on
IA32 processors and that is where the commodity market is
centered, it is convenient to use IA32 to distinguish any of
these processors from the PowerPC, Alpha, PA-RISC, MIPS, SPARC,
etc. The upcoming IA64 (64-bit with EPIC, Explicitly Parallel
Instruction Computing) will certainly complicate matters, but
Merced, the first IA64 processor, is not scheduled for
production until 1999.
COTS:
Since the demise of many parallel supercomputer companies, COTS
(Commercial Off-The-Shelf) is commonly discussed as a
requirement for parallel computing systems. Being fanatically
pure, the only COTS parallel processing techniques using PCs are
things like SMP Windows NT servers and various MMX Windows
applications; it really doesn't pay to be that fanatical. The
underlying concept of COTS is really minimization of development
time and cost. Thus, a more useful, more common, meaning of
COTS is that at least most subsystems benefit from commodity
marketing, but other technologies are used where they are
effective. Most often, COTS parallel processing refers to a
cluster in which the nodes are commodity PCs, but the network
interface and software are somewhat customized... typically
running Linux and applications codes that are freely available
(e.g., copyleft or public domain), but not literally COTS.
1.3. Example Algorithm
In order to better understand the use of the various parallel
programming approaches outlined in this HOWTO, it is useful to have an
example problem. Although just about any simple parallel algorithm
would do, by selecting an algorithm that has been used to demonstrate
various other parallel programming systems, it becomes a bit easier to
compare and contrast approaches. M. J. Quinn's book, Parallel
Computing Theory And Practice, second edition, McGraw Hill, New York,
1994, uses a parallel algorithm that computes the value of Pi to
demonstrate a variety of different parallel supercomputer programming
environments (e.g., nCUBE message passing, Sequent shared memory). In
this HOWTO, we use the same basic algorithm.
The algorithm computes the approximate value of Pi by summing the area
under x squared. As a purely sequential C program, the algorithm
looks like:
______________________________________________________________________
#include <stdlib.h>;
#include <stdio.h>;
main(int argc, char **argv)
{
register double width, sum;
register int intervals, i;
/* get the number of intervals */
intervals = atoi(argv[1]);
width = 1.0 / intervals;
/* do the computation */
sum = 0;
for (i=0; i<intervals; ++i) {
register double x = (i + 0.5) * width;
sum += 4.0 / (1.0 + x * x);
}
sum *= width;
printf("Estimation of pi is %f\n", sum);
return(0);
}
______________________________________________________________________
However, this sequential algorithm easily yields an "embarrassingly
parallel" implementation. The area is subdivided into intervals, and
any number of processors can each independently sum the intervals
assigned to it, with no need for interaction between processors. Once
the local sums have been computed, they are added together to create a
global sum; this step requires some level of coordination and
communication between processors. Finally, this global sum is printed
by one processor as the approximate value of Pi.
In this HOWTO, the various parallel implementations of this algorithm
appear where each of the different programming methods is discussed.
1.4. Organization Of This Document
The remainder of this document is divided into five parts. Sections
2, 3, 4, and 5 correspond to the three different types of hardware
configurations supporting parallel processing using Linux:
o Section 2 discusses SMP Linux systems. These directly support MIMD
execution using shared memory, although message passing also is
implemented easily. Although Linux supports SMP configurations up
to 16 processors, most SMP PC systems have either two or four
identical processors.
o Section 3 discusses clusters of networked machines, each running
Linux. A cluster can be used as a parallel processing system that
directly supports MIMD execution and message passing, perhaps also
providing logically shared memory. Simulated SIMD execution and
aggregate function communication also can be supported, depending
on the networking method used. The number of processors in a
cluster can range from two to thousands, primarily limited by the
physical wiring constraints of the network. In some cases, various
types of machines can be mixed within a cluster; for example, a
network combining DEC Alpha and Pentium Linux systems would be a
heterogeneous cluster.
o Section 4 discusses SWAR, SIMD Within A Register. This is a very
restrictive type of parallel execution model, but on the other
hand, it is a built-in capability of ordinary processors.
Recently, MMX (and other) instruction set extensions to modern
processors have made this approach even more effective.
o Section 5 discusses the use of Linux PCs as hosts for simple
parallel computing systems. Either as an add-in card or as an
external box, attached processors can provide a Linux system with
formidable processing power for specific types of applications.
For example, inexpensive ISA cards are available that provide
multiple DSP processors offering hundreds of MFLOPS for compute-
bound problems. However, these add-in boards are just processors;
they generally do not run an OS, have disk or console I/O
capability, etc. To make such systems useful, the Linux "host"
must provide these functions.
The final section of this document covers aspects that are of general
interest for parallel processing using Linux, not specific to a
particular one of the approaches listed above.
As you read this document, keep in mind that we haven't tested
everything, and a lot of stuff reported here "still has a research
character" (a nice way to say "doesn't quite work like it should" ;-).
However, parallel processing using Linux is useful now, and an
increasingly large group is working to make it better.
The author of this HOWTO is Hank Dietz, Ph.D., currently Associate
Professor of Electrical and Computer Engineering at Purdue University,
in West Lafayette, IN, 47907-1285. Dietz retains rights to this
document as per the Linux Documentation Project guidelines. Although
an effort has been made to ensure the correctness and fairness of this
presentation, neither Dietz nor Purdue University can be held
responsible for any problems or errors, and Purdue University does not
endorse any of the work/products discussed.
2. SMP Linux
This document gives a brief overview of how to use SMP Linux
<
http://www.uk.linux.org/SMP/title.html> systems for parallel
processing. The most up-to-date information on SMP Linux is probably
available via the SMP Linux project mailing list; send email to
[email protected] with the text subscribe linux-smp to join
the list.
Does SMP Linux really work? In June 1996, I purchased a brand new
(well, new off-brand ;-) two-processor 100MHz Pentium system. The
fully assembled system, including both processors, Asus motherboard,
256K cache, 32M RAM, 1.6G disk, 6X CDROM, Stealth 64, and 15" Acer
monitor, cost a total of $1,800. This was just a few hundred dollars
more than a comparable uniprocessor system. Getting SMP Linux running
was simply a matter of installing the "stock" uniprocessor Linux,
recompiling the kernel with the SMP=1 line in the makefile uncommented
(although I find setting SMP to 1 a bit ironic ;-), and informing lilo
about the new kernel. This system performs well enough, and has been
stable enough, to serve as my primary workstation ever since. In
summary, SMP Linux really does work.
The next question is how much high-level support is available for
writing and executing shared memory parallel programs under SMP Linux.
Through early 1996, there wasn't much. Things have changed. For
example, there is now a very complete POSIX threads library.
Although performance may be lower than for native shared-memory
mechanisms, an SMP Linux system also can use most parallel processing
software that was originally developed for a workstation cluster using
socket communication. Sockets (see section 3.3) work within an SMP
Linux system, and even for multiple SMPs networked as a cluster.
However, sockets imply a lot of unnecessary overhead for an SMP. Much
of that overhead is within the kernel or interrupt handlers; this
worsens the problem because SMP Linux generally allows only one
processor to be in the kernel at a time and the interrupt controller
is set so that only the boot processor can process interrupts.
Despite this, typical SMP communication hardware is so much better
than most cluster networks that cluster software will often run better
on an SMP than on the cluster for which it was designed.
The remainder of this section discusses SMP hardware, reviews the
basic Linux mechanisms for sharing memory across the processes of a
parallel program, makes a few observations about atomicity,
volatility, locks, and cache lines, and finally gives some pointers to
other shared memory parallel processing resources.
2.1. SMP Hardware
Although SMP systems have been around for many years, until very
recently, each such machine tended to implement basic functions
differently enough so that operating system support was not portable.
The thing that has changed this situation is Intel's Multiprocessor
Specification, often referred to as simply MPS. The MPS 1.4
specification is currently available as a PDF file at
<
http://www.intel.com/design/pro/datashts/242016.htm>, and there is a
brief overview of MPS 1.1 at
<
http://support.intel.com/oem_developer/ial/support/9300.HTM>, but be
aware that Intel does re-arrange their WWW site often. A wide range
of vendors <
http://www.uruk.org/~erich/mps-hw.html> are building MPS-
compliant systems supporting up to four processors, but MPS
theoretically allows many more processors.
The only non-MPS, non-IA32, systems supported by SMP Linux are Sun4m
multiprocessor SPARC machines. SMP Linux supports most Intel MPS
version 1.1 or 1.4 compliant machines with up to sixteen 486DX,
Pentium, Pentium MMX, Pentium Pro, or Pentium II processors.
Unsupported IA32 processors include the Intel 386, Intel 486SX/SLC
processors (the lack of floating point hardware interferes with the
SMP mechanisms), and AMD & Cyrix processors (they require different
SMP support chips that do not seem to be available at this writing).
It is important to understand that the performance of MPS-compliant
systems can vary widely. As expected, one cause for performance
differences is processor speed: faster clock speeds tend to yield
faster systems, and a Pentium Pro processor is faster than a Pentium.
However, MPS does not really specify how hardware implements shared
memory, but only how that implementation must function from a software
point of view; this means that performance is also a function of how
the shared memory implementation interacts with the characteristics of
SMP Linux and your particular programs.
The primary way in which systems that comply with MPS differ is in how
they implement access to physically shared memory.
2.1.1. Does each processor have its own L2 cache?
Some MPS Pentium systems, and all MPS Pentium Pro and Pentium II
systems, have independent L2 caches. (The L2 cache is packaged within
the Pentium Pro or Pentium II modules.) Separate L2 caches are
generally viewed as maximizing compute performance, but things are not
quite so obvious under Linux. The primary complication is that the
current SMP Linux scheduler does not attempt to keep each process on
the same processor, a concept known as processor affinity. This may
change soon; there has recently been some discussion about this in the
SMP Linux development community under the title "processor binding."
Without processor affinity, having separate L2 caches may introduce
significant overhead when a process is given a timeslice on a
processor other than the one that was executing it last.
Many relatively inexpensive systems are organized so that two Pentium
processors share a single L2 cache. The bad news is that this causes
contention for the cache, seriously degrading performance when running
multiple independent sequential programs. The good news is that many
parallel programs might actually benefit from the shared cache because
if both processors will want to access the same line from shared
memory, only one had to fetch it into cache and contention for the bus
is averted. The lack of processor affinity also causes less damage
with a shared L2 cache. Thus, for parallel programs, it isn't really
clear that sharing L2 cache is as harmful as one might expect.
Experience with our dual Pentium shared 256K cache system shows quite
a wide range of performance depending on the level of kernel activity
required. At worst, we see only about 1.2x speedup. However, we also
have seen up to 2.1x speedup, which suggests that compute-intensive
SPMD-style code really does profit from the "shared fetch" effect.
2.1.2. Bus configuration?
The first thing to say is that most modern systems connect the
processors to one or more PCI buses that in turn are "bridged" to one
or more ISA/EISA buses. These bridges add latency, and both EISA and
ISA generally offer lower bandwidth than PCI (ISA being the lowest),
so disk drives, video cards, and other high-performance devices
generally should be connected via a PCI bus interface.
Although an MPS system can achieve good speed-up for many compute-
intensive parallel programs even if there is only one PCI bus, I/O
operations occur at no better than uniprocessor performance... and
probably a little worse due to bus contention from the processors.
Thus, if you are looking to speed-up I/O, make sure that you get an
MPS system with multiple independent PCI busses and I/O controllers
(e.g., multiple SCSI chains). You will need to be careful to make
sure SMP Linux supports what you get. Also keep in mind that the
current SMP Linux essentially allows only one processor in the kernel
at any time, so you should choose your I/O controllers carefully to
pick ones that minimize the kernel time required for each I/O
operation. For really high performance, you might even consider doing
raw device I/O directly from user processes, without a system call...
this isn't necessarily as hard as it sounds, and need not compromise
security (see section 3.3 for a description of the basic techniques).
It is important to note that the relationship between bus speed and
processor clock rate has become very fuzzy over the past few years.
Although most systems now use the same PCI clock rate, it is not
uncommon to find a faster processor clock paired with a slower bus
clock. The classic example of this was that the Pentium 133 generally
used a faster bus than a Pentium 150, with appropriately strange-
looking performance on various benchmarks. These effects are
amplified in SMP systems; it is even more important to have a faster
bus clock.
2.1.3. Memory interleaving and DRAM technologies?
Memory interleaving actually has nothing whatsoever to do with MPS,
but you will often see it mentioned for MPS systems because these
systems are typically more demanding of memory bandwidth. Basically,
two-way or four-way interleaving organizes RAM so that a block access
is accomplished using multiple banks of RAM rather than just one.
This provides higher memory access bandwidth, particularly for cache
line loads and stores.
The waters are a bit muddied about this, however, because EDO DRAM and
various other memory technologies tend to improve similar kinds of
operations. An excellent overview of DRAM technologies is given in
<
http://www.pcguide.com/ref/ram/tech.htm>.
So, for example, is it better to have 2-way interleaved EDO DRAM or
non-interleaved SDRAM? That is a very good question with no simple
answer, because both interleaving and exotic DRAM technologies tend to
be expensive. The same dollar investment in more ordinary memory
configurations generally will give you a significantly larger main
memory. Even the slowest DRAM is still a heck of a lot faster than
using disk-based virtual memory....
2.2. Introduction To Shared Memory Programming
Ok, so you have decided that parallel processing on an SMP is a great
thing to do... how do you get started? Well, the first step is to
learn a little bit about how shared memory communication really works.
It sounds like you simply have one processor store a value into memory
and another processor load it; unfortunately, it isn't quite that
simple. For example, the relationship between processes and
processors is very blurry; however, if we have no more active
processes than there are processors, the terms are roughly
interchangeable. The remainder of this section briefly summarizes the
key issues that could cause serious problems, if you were not aware of
them: the two different models used to determine what is shared,
atomicity issues, the concept of volatility, hardware lock
instructions, cache line effects, and Linux scheduler issues.
2.2.1. Shared Everything Vs. Shared Something
There are two fundamentally different models commonly used for shared
memory programming: shared everything and shared something. Both of
these models allow processors to communicate by loads and stores
from/into shared memory; the distinction comes in the fact that shared
everything places all data structures in shared memory, while shared
something requires the user to explicitly indicate which data
structures are potentially shared and which are private to a single
processor.
Which shared memory model should you use? That is mostly a question
of religion. A lot of people like the shared everything model because
they do not really need to identify which data structures should be
shared at the time they are declared... you simply put locks around
potentially-conflicting accesses to shared objects to ensure that only
one process(or) has access at any moment. Then again, that really
isn't all that simple... so many people prefer the relative safety of
shared something.
2.2.1.1. Shared Everything
The nice thing about sharing everything is that you can easily take an
existing sequential program and incrementally convert it into a shared
everything parallel program. You do not have to first determine which
data need to be accessible by other processors.
Put simply, the primary problem with sharing everything is that any
action taken by one processor could affect the other processors. This
problem surfaces in two ways:
o Many libraries use data structures that simply are not sharable.
For example, the UNIX convention is that most functions can return
an error code in a variable called errno; if two shared everything
processes perform various calls, they would interfere with each
other because they share the same errno. Although there is now a
library version that fixes the errno problem, similar problems
still exist in most libraries. For example, unless special
precautions are taken, the X library will not work if calls are
made from multiple shared everything processes.
o Normally, the worst-case behavior for a program with a bad pointer
or array subscript is that the process that contains the offending
code dies. It might even generate a core file that clues you in to
what happened. In shared everything parallel processing, it is
very likely that the stray accesses will bring the demise of a
process other than the one at fault, making it nearly impossible to
localize and correct the error.
Neither of these types of problems is common when shared something is
used, because only the explicitly-marked data structures are shared.
It also is fairly obvious that shared everything only works if all
processors are executing the exact same memory image; you cannot use
shared everything across multiple different code images (i.e., can use
only SPMD, not general MIMD).
The most common type of shared everything programming support is a
threads library. Threads
<
http://liinwww.ira.uka.de/bibliography/Os/threads.html> are
essentially "light-weight" processes that might not be scheduled in
the same way as regular UNIX processes and, most importantly, share
access to a single memory map. The POSIX Pthreads
<
http://www.mit.edu:8001/people/proven/pthreads.html> package has been
the focus of a number of porting efforts; the big question is whether
any of these ports actually run the threads of a program in parallel
under SMP Linux (ideally, with a processor for each thread). The
POSIX API doesn't require it, and versions like
<
http://www.aa.net/~mtp/PCthreads.html> apparently do not implement
parallel thread execution - all the threads of a program are kept
within a single Linux process.
The first threads library that supported SMP Linux parallelism was the
now somewhat obsolete bb_threads library,
<
ftp://caliban.physics.utoronto.ca/pub/linux/>, a very small library
that used the Linux clone() call to fork new, independently scheduled,
Linux processes all sharing a single address space. SMP Linux
machines can run multiple of these "threads" in parallel because each
"thread" is a full Linux process; the trade-off is that you do not get
the same "light-weight" scheduling control provided by some thread
libraries under other operating systems. The library used a bit of C-
wrapped assembly code to install a new chunk of memory as each
thread's stack and to provide atomic access functions for an array of
locks (mutex objects). Documentation consisted of a README and a
short sample program.
More recently, a version of POSIX threads using clone() has been
developed. This library, LinuxThreads
<
http://pauillac.inria.fr/~xleroy/linuxthreads/>, is clearly the
preferred shared everything library for use under SMP Linux. POSIX
threads are well documented, and the LinuxThreads README
<
http://pauillac.inria.fr/~xleroy/linuxthreads/README> and
LinuxThreads FAQ
<
http://pauillac.inria.fr/~xleroy/linuxthreads/faq.html> are very well
done. The primary problem now is simply that POSIX threads have a lot
of details to get right and LinuxThreads is still a work in progress.
There is also the problem that the POSIX thread standard has evolved
through the standardization process, so you need to be a bit careful
not to program for obsolete early versions of the standard.
2.2.1.2. Shared Something
Shared something is really "only share what needs to be shared." This
approach can work for general MIMD (not just SPMD) provided that care
is taken for the shared objects to be allocated at the same places in
each processor's memory map. More importantly, shared something makes
it easier to predict and tune performance, debug code, etc. The only
problems are:
o It can be hard to know beforehand what really needs to be shared.
o The actual allocation of objects in shared memory may be awkward,
especially for what would have been stack-allocated objects. For
example, it may be necessary to explicitly allocate shared objects
in a separate memory segment, requiring separate memory allocation
routines and introducing extra pointer indirections in each
reference.
Currently, there are two very similar mechanisms that allow groups of
Linux processes to have independent memory spaces, all sharing only a
relatively small memory segment. Assuming that you didn't foolishly
exclude "System V IPC" when you configured your Linux system, Linux
supports a very portable mechanism that has generally become known as
"System V Shared Memory." The other alternative is a memory mapping
facility whose implementation varies widely across different UNIX
systems: the mmap() system call. You can, and should, learn about
these calls from the manual pages... but a brief overview of each is
given in sections 2.5 and 2.6 to help get you started.
2.2.2. Atomicity And Ordering
No matter which of the above two models you use, the result is pretty
much the same: you get a pointer to a chunk of read/write memory that
is accessible by all processes within your parallel program. Does
that mean I can just have my parallel program access shared memory
objects as though they were in ordinary local memory? Well, not
quite....
Atomicity refers to the concept that an operation on an object is
accomplished as an indivisible, uninterruptible, sequence.
Unfortunately, sharing memory access does not imply that all
operations on data in shared memory occur atomically. Unless special
precautions are taken, only simple load or store operations that occur
within a single bus transaction (i.e., aligned 8, 16, or 32-bit
operations, but not misaligned nor 64-bit operations) are atomic.
Worse still, "smart" compilers like GCC will often perform
optimizations that could eliminate the memory operations needed to
ensure that other processors can see what this processor has done.
Fortunately, both these problems can be remedied... leaving only the
relationship between access efficiency and cache line size for us to
worry about.
However, before discussing these issues, it is useful to point-out
that all of this assumes that memory references for each processor
happen in the order in which they were coded. The Pentium does this,
but also notes that future Intel processors might not. So, for future
processors, keep in mind that it may be necessary to surround some
shared memory accesses with instructions that cause all pending memory
accesses to complete, thus providing memory access ordering. The
CPUID instruction apparently is reserved to have this side-effect.
2.2.3. Volatility
To prevent GCC's optimizer from buffering values of shared memory
objects in registers, all objects in shared memory should be declared
as having types with the volatile attribute. If this is done, all
shared object reads and writes that require just one word access will
occur atomically. For example, suppose that p is a pointer to an
integer, where both the pointer and the integer it will point at are
in shared memory; the ANSI C declaration might be:
______________________________________________________________________
volatile int * volatile p;
______________________________________________________________________
In this code, the first volatile refers to the int that p will
eventually point at; the second volatile refers to the pointer itself.
Yes, it is annoying, but it is the price one pays for enabling GCC to
perform some very powerful optimizations. At least in theory, the
-traditional option to GCC might suffice to produce correct code at
the expense of some optimization, because pre-ANSI K&R C essentially
claimed that all variables were volatile unless explicitly declared as
register. Still, if your typical GCC compile looks like cc -O6 ...,
you really will want to explicitly mark things as volatile only where
necessary.
There has been a rumor to the effect that using assembly-language
locks that are marked as modifying all processor registers will cause
GCC to appropriately flush all variables, thus avoiding the
"inefficient" compiled code associated with things declared as
volatile. This hack appears to work for statically allocated global
variables using version 2.7.0 of GCC... however, that behavior is not
required by the ANSI C standard. Still worse, other processes that
are making only read accesses can buffer the values in registers
forever, thus never noticing that the shared memory value has actually
changed. In summary, do what you want, but only variables accessed
through volatile are guaranteed to work correctly.
Note that you can cause a volatile access to an ordinary variable by
using a type cast that imposes the volatile attribute. For example,
the ordinary int i; can be referenced as a volatile by *((volatile int
*) &i); thus, you can explicitly invoke the "overhead" of volatility
only where it is critical.
2.2.4. Locks
If you thought that ++i; would always work to add one to a variable i
in shared memory, you've got a nasty little surprise coming: even if
coded as a single instruction, the load and store of the result are
separate memory transactions, and other processors could access i
between these two transactions. For example, having two processes
both perform ++i; might only increment i by one, rather than by two.
According to the Intel Pentium "Architecture and Programming Manual,"
the LOCK prefix can be used to ensure that any of the following
instructions is atomic relative to the data memory location it
accesses:
______________________________________________________________________
BTS, BTR, BTC mem, reg/imm
XCHG reg, mem
XCHG mem, reg
ADD, OR, ADC, SBB, AND, SUB, XOR mem, reg/imm
NOT, NEG, INC, DEC mem
CMPXCHG, XADD
______________________________________________________________________
However, it probably is not a good idea to use all these operations.
For example, XADD did not even exist for the 386, so coding it may
cause portability problems.
The XCHG instruction always asserts a lock, even without the LOCK
prefix, and thus is clearly the preferred atomic operation from which
to build higher-level atomic constructs such as semaphores and shared
queues. Of course, you can't get GCC to generate this instruction
just by writing C code... instead, you must use a bit of in-line
assembly code. Given a word-size volatile object obj and a word-size
register value reg, the GCC in-line assembly code is:
______________________________________________________________________
__asm__ __volatile__ ("xchgl %1,%0"
:"=r" (reg), "=m" (obj)
:"r" (reg), "m" (obj));
______________________________________________________________________
Examples of GCC in-line assembly code using bit operations for locking
are given in the source code for the bb_threads library
<
ftp://caliban.physics.utoronto.ca/pub/linux/>.
It is important to remember, however, that there is a cost associated
with making memory transactions atomic. A locking operation carries a
fair amount of overhead and may delay memory activity from other
processors, whereas ordinary references may use local cache. The best
performance results when locking operations are used as infrequently
as possible. Further, these IA32 atomic instructions obviously are
not portable to other systems.
There are many alternative approaches that allow ordinary instructions
to be used to implement various synchronizations, including mutual
exclusion - ensuring that at most one processor is updating a given
shared object at any moment. Most OS textbooks discuss at least one
of these techniques. There is a fairly good discussion in the Fourth
Edition of Operating System Concepts, by Abraham Silberschatz and
Peter B. Galvin, ISBN 0-201-50480-4.
2.2.5. Cache Line Size
One more fundamental atomicity concern can have a dramatic impact on
SMP performance: cache line size. Although the MPS standard requires
references to be coherent no matter what caching is used, the fact is
that when one processor writes to a particular line of memory, every
cached copy of the old line must be invalidated or updated. This
implies that if two or more processors are both writing data to
different portions of the same line a lot of cache and bus traffic may
result, effectively to pass the line from cache to cache. This
problem is known as false sharing. The solution is simply to try to
organize data so that what is accessed in parallel tends to come from
a different cache line for each process.
You might be thinking that false sharing is not a problem using a
system with a shared L2 cache, but remember that there are still
separate L1 caches. Cache organization and number of separate levels
can both vary, but the Pentium L1 cache line size is 32 bytes and
typical external cache line sizes are around 256 bytes. Suppose that
the addresses (physical or virtual) of two items are a and b and that
the largest per-processor cache line size is c, which we assume to be
a power of two. To be very precise, if ((int) a) & ~(c - 1) is equal
to ((int) b) & ~(c - 1), then both references are in the same cache
line. A simpler rule is that if shared objects being referenced in
parallel are at least c bytes apart, they should map to different
cache lines.
2.2.6. Linux Scheduler Issues
Although the whole point of using shared memory for parallel
processing is to avoid OS overhead, OS overhead can come from things
other than communication per se. We have already said that the number
of processes that should be constructed is less than or equal to the
number of processors in the machine. But how do you decide exactly
how many processes to make?
For best performance, the number of processes in your parallel program
should be equal to the expected number of your program's processes
that simultaneously can be running on different processors. For
example, if a four-processor SMP typically has one process actively
running for some other purpose (e.g., a WWW server), then your
parallel program should use only three processes. You can get a rough
idea of how many other processes are active on your system by looking
at the "load average" quoted by the uptime command.
Alternatively, you could boost the priority of the processes in your
parallel program using, for example, the renice command or nice()
system call. You must be privileged to increase priority. The idea
is simply to force the other processes out of processors so that your
program can run simultaneously across all processors. This can be
accomplished somewhat more explicitly using the prototype version of
SMP Linux at <
http://luz.cs.nmt.edu/~rtlinux/>, which offers real-
time schedulers.
If you are not the only user treating your SMP system as a parallel
machine, you may also have conflicts between the two or more parallel
programs trying to execute simultaneously. This standard solution is
gang scheduling - i.e., manipulating scheduling priority so that at
any given moment, only the processes of a single parallel program are
running. It is useful to recall, however, that using more parallelism
tends to have diminishing returns and scheduler activity adds
overhead. Thus, for example, it is probably better for a four-
processor machine to run two programs with two processes each rather
than gang scheduling between two programs with four processes each.
There is one more twist to this. Suppose that you are developing a
program on a machine that is heavily used all day, but will be fully
available for parallel execution at night. You need to write and test
your code for correctness with the full number of processes, even
though you know that your daytime test runs will be slow. Well, they
will be very slow if you have processes busy waiting for shared memory
values to be changed by other processes that are not currently running
(on other processors). The same problem occurs if you develop and
test your code on a single-processor system.
The solution is to embed calls in your code, wherever it may loop
awaiting an action from another processor, so that Linux will give
another process a chance to run. I use a C macro, call it IDLE_ME, to
do this: for a test run, compile with cc -DIDLE_ME=usleep(1); ...;
for a "production" run, compile with cc -DIDLE_ME={} .... The
usleep(1) call requests a 1 microsecond sleep, which has the effect of
allowing the Linux scheduler to select a different process to run on
that processor. If the number of processes is more than twice the
number of processors available, it is not unusual for codes to run ten
times faster with usleep(1) calls than without them.
2.3. bb_threads
The bb_threads ("Bare Bones" threads) library,
<
ftp://caliban.physics.utoronto.ca/pub/linux/>, is a remarkably simple
library that demonstrates use of the Linux clone() call. The gzip tar
file is only 7K bytes! Although this library is essentially made
obsolete by the LinuxThreads library discussed in section 2.4,
bb_threads is still usable, and it is small and simple enough to serve
well as an introduction to use of Linux thread support. Certainly, it
is far less daunting to read this source code than to browse the
source code for LinuxThreads. In summary, the bb_threads library is a
good starting point, but is not really suitable for coding large
projects.
The basic program structure for using the bb_threads library is:
1. Start the program running as a single process.
2. You will need to estimate the maximum stack space that will be
required for each thread. Guessing large is relatively harmless
(that is what virtual memory is for ;-), but remember that all the
stacks are coming from a single virtual address space, so guessing
huge is not a great idea. The demo suggests 64K. This size is set
to b bytes by bb_threads_stacksize(b).
3. The next step is to initialize any locks that you will need. The
lock mechanism built-into this library numbers locks from 0 to
MAX_MUTEXES, and initializes lock i by bb_threads_mutexcreate(i).
4. Spawning a new thread is done by calling a library routine that
takes arguments specifying what function the new thread should
execute and what arguments should be transmitted to it. To start a
new thread executing the void-returning function f with the single
argument arg, you do something like bb_threads_newthread(f, &arg),
where f should be declared something like void f(void *arg, size_t
dummy). If you need to pass more than one argument, pass a pointer
to a structure initialized to hold the argument values.
5. Run parallel code, being careful to use bb_threads_lock(n) and
bb_threads_unlock(n) where n is an integer identifying which lock
to use. Note that the lock and unlock operations in this library
are very basic spin locks using atomic bus-locking instructions,
which can cause excessive memory-reference interference and do not
make any attempt to ensure fairness.
The demo program packaged with bb_threads did not correctly use
locks to prevent printf() from being executed simultaneously from
within the functions fnn and main... and because of this, the demo
does not always work. I'm not saying this to knock the demo, but
rather to emphasize that this stuff is very tricky; also, it is
only slightly easier using LinuxThreads.
6. When a thread executes a return, it actually destroys the
process... but the local stack memory is not automatically
deallocated. To be precise, Linux doesn't support deallocation,
but the memory space is not automatically added back to the
malloc() free list. Thus, the parent process should reclaim the
space for each dead child by bb_threads_cleanup(wait(NULL)).
The following C program uses the algorithm discussed in section 1.3 to
compute the approximate value of Pi using two bb_threads threads.
______________________________________________________________________
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <sys/types.h>
#include <sys/wait.h>
#include "bb_threads.h"
volatile double pi = 0.0;
volatile int intervals;
volatile int pids[2]; /* Unix PIDs of threads */
void
do_pi(void *data, size_t len)
{
register double width, localsum;
register int i;
register int iproc = (getpid() != pids[0]);
/* set width */
width = 1.0 / intervals;
/* do the local computations */
localsum = 0;
for (i=iproc; i<intervals; i+=2) {
register double x = (i + 0.5) * width;
localsum += 4.0 / (1.0 + x * x);
}
localsum *= width;
/* get permission, update pi, and unlock */
bb_threads_lock(0);
pi += localsum;
bb_threads_unlock(0);
}
int
main(int argc, char **argv)
{
/* get the number of intervals */
intervals = atoi(argv[1]);
/* set stack size and create lock... */
bb_threads_stacksize(65536);
bb_threads_mutexcreate(0);
/* make two threads... */
pids[0] = bb_threads_newthread(do_pi, NULL);
pids[1] = bb_threads_newthread(do_pi, NULL);
/* cleanup after two threads (really a barrier sync) */
bb_threads_cleanup(wait(NULL));
bb_threads_cleanup(wait(NULL));
/* print the result */
printf("Estimation of pi is %f\n", pi);
/* check-out */
exit(0);
}
______________________________________________________________________
2.4. LinuxThreads
LinuxThreads <
http://pauillac.inria.fr/~xleroy/linuxthreads/> is a
fairly complete and solid implementation of "shared everything" as per
the POSIX 1003.1c threads standard. Unlike other POSIX threads ports,
LinuxThreads uses the same Linux kernel threads facility (clone())
that is used by bb_threads. POSIX compatibility means that it is
relatively easy to port quite a few threaded applications from other
systems and various tutorial materials are available. In short, this
is definitely the threads package to use under Linux for developing
large-scale threaded programs.
The basic program structure for using the LinuxThreads library is:
1. Start the program running as a single process.
2. The next step is to initialize any locks that you will need.
Unlike bb_threads locks, which are identified by numbers, POSIX
locks are declared as variables of type pthread_mutex_t lock. Use
pthread_mutex_init(&lock,val) to initialize each one you will need
to use.
3. As with bb_threads, spawning a new thread is done by calling a
library routine that takes arguments specifying what function the
new thread should execute and what arguments should be transmitted
to it. However, POSIX requires the user to declare a variable of
type pthread_t to identify each thread. To create a thread
pthread_t thread running f(), one calls
pthread_create(&thread,NULL,f,&arg).
4. Run parallel code, being careful to use pthread_mutex_lock(&lock)
and pthread_mutex_unlock(&lock) as appropriate.
5. Use pthread_join(thread,&retval) to clean-up after each thread.
6. Use -D_REENTRANT when compiling your C code.
An example parallel computation of Pi using LinuxThreads follows. The
algorithm of section 1.3 is used and, as for the bb_threads example,
two threads execute in parallel.
______________________________________________________________________
#include <stdio.h>
#include <stdlib.h>
#include "pthread.h"
volatile double pi = 0.0; /* Approximation to pi (shared) */
pthread_mutex_t pi_lock; /* Lock for above */
volatile double intervals; /* How many intervals? */
void *
process(void *arg)
{
register double width, localsum;
register int i;
register int iproc = (*((char *) arg) - '0');
/* Set width */
width = 1.0 / intervals;
/* Do the local computations */
localsum = 0;
for (i=iproc; i<intervals; i+=2) {
register double x = (i + 0.5) * width;
localsum += 4.0 / (1.0 + x * x);
}
localsum *= width;
/* Lock pi for update, update it, and unlock */
pthread_mutex_lock(&pi_lock);
pi += localsum;
pthread_mutex_unlock(&pi_lock);
return(NULL);
}
int
main(int argc, char **argv)
{
pthread_t thread0, thread1;
void * retval;
/* Get the number of intervals */
intervals = atoi(argv[1]);
/* Initialize the lock on pi */
pthread_mutex_init(&pi_lock, NULL);
/* Make the two threads */
if (pthread_create(&thread0, NULL, process, "0") ||
pthread_create(&thread1, NULL, process, "1")) {
fprintf(stderr, "%s: cannot make thread\n", argv[0]);
exit(1);
}
/* Join (collapse) the two threads */
if (pthread_join(thread0, &retval) ||
pthread_join(thread1, &retval)) {
fprintf(stderr, "%s: thread join failed\n", argv[0]);
exit(1);
}
/* Print the result */
printf("Estimation of pi is %f\n", pi);
/* Check-out */
exit(0);
}
______________________________________________________________________
2.5. System V Shared Memory
The System V IPC (Inter-Process Communication) support consists of a
number of system calls providing message queues, semaphores, and a
shared memory mechanism. Of course, these mechanisms were originally
intended to be used for multiple processes to communicate within a
uniprocessor system. However, that implies that it also should work
to communicate between processes under SMP Linux, no matter which
processors they run on.
Before going into how these calls are used, it is important to
understand that although System V IPC calls exist for things like
semaphores and message transmission, you probably should not use them.
Why not? These functions are generally slow and serialized under SMP
Linux. Enough said.
The basic procedure for creating a group of processes sharing access
to a shared memory segment is:
1. Start the program running as a single process.
2. Typically, you will want each run of a parallel program to have its
own shared memory segment, so you will need to call shmget() to
create a new segment of the desired size. Alternatively, this call
can be used to get the ID of a pre-existing shared memory segment.
In either case, the return value is either the shared memory
segment ID or -1 for error. For example, to create a shared memory
segment of b bytes, the call might be shmid = shmget(IPC_PRIVATE,
b, (IPC_CREAT | 0666)).
3. The next step is to attach this shared memory segment to this
process, literally adding it to the virtual memory map of this
process. Although the shmat() call allows the programmer to
specify the virtual address at which the segment should appear, the
address selected must be aligned on a page boundary (i.e., be a
multiple of the page size returned by getpagesize(), which is
usually 4096 bytes), and will override the mapping of any memory
formerly at that address. Thus, we instead prefer to let the
system pick the address. In either case, the return value is a
pointer to the base virtual address of the segment just mapped.
The code is shmptr = shmat(shmid, 0, 0).
Notice that you can allocate all your static shared variables into
this shared memory segment by simply declaring all shared variables
as members of a struct type, and declaring shmptr to be a pointer
to that type. Using this technique, shared variable x would be
accessed as shmptr->x.
4. Since this shared memory segment should be destroyed when the last
process with access to it terminates or detaches from it, we need
to call shmctl() to set-up this default action. The code is
something like shmctl(shmid, IPC_RMID, 0).
5. Use the standard Linux fork() call to make the desired number of
processes... each will inherit the shared memory segment.
6. When a process is done using a shared memory segment, it really
should detach from that shared memory segment. This is done by
shmdt(shmptr).
Although the above set-up does require a few system calls, once the
shared memory segment has been established, any change made by one
processor to a value in that memory will automatically be visible to
all processes. Most importantly, each communication operation will
occur without the overhead of a system call.
An example C program using System V shared memory segments follows.
It computes Pi, using the same algorithm given in section 1.3.
______________________________________________________________________
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <fcntl.h>
#include <sys/ipc.h>
#include <sys/shm.h>
volatile struct shared { double pi; int lock; } *shared;
inline extern int xchg(register int reg,
volatile int * volatile obj)
{
/* Atomic exchange instruction */
__asm__ __volatile__ ("xchgl %1,%0"
:"=r" (reg), "=m" (*obj)
:"r" (reg), "m" (*obj));
return(reg);
}
main(int argc, char **argv)
{
register double width, localsum;
register int intervals, i;
register int shmid;
register int iproc = 0;;
/* Allocate System V shared memory */
shmid = shmget(IPC_PRIVATE,
sizeof(struct shared),
(IPC_CREAT | 0600));
shared = ((volatile struct shared *) shmat(shmid, 0, 0));
shmctl(shmid, IPC_RMID, 0);
/* Initialize... */
shared->pi = 0.0;
shared->lock = 0;
/* Fork a child */
if (!fork()) ++iproc;
/* get the number of intervals */
intervals = atoi(argv[1]);
width = 1.0 / intervals;
/* do the local computations */
localsum = 0;
for (i=iproc; i<intervals; i+=2) {
register double x = (i + 0.5) * width;
localsum += 4.0 / (1.0 + x * x);
}
localsum *= width;
/* Atomic spin lock, add, unlock... */
while (xchg((iproc + 1), &(shared->lock))) ;
shared->pi += localsum;
shared->lock = 0;
/* Terminate child (barrier sync) */
if (iproc == 0) {
wait(NULL);
printf("Estimation of pi is %f\n", shared->pi);
}
/* Check out */
return(0);
}
______________________________________________________________________
In this example, I have used the IA32 atomic exchange instruction to
implement locking. For better performance and portability, substitute
a synchronization technique that avoids atomic bus-locking
instructions (discussed in section 2.2).
When debugging your code, it is useful to remember that the ipcs
command will report the status of the System V IPC facilities
currently in use.
2.6. Memory Map Call
Using system calls for file I/O can be very expensive; in fact, that
is why there is a user-buffered file I/O library (getchar(), fwrite(),
etc.). But user buffers don't work if multiple processes are
accessing the same writeable file, and the user buffer management
overhead is significant. The BSD UNIX fix for this was the addition
of a system call that allows a portion of a file to be mapped into
user memory, essentially using virtual memory paging mechanisms to
cause updates. This same mechanism also has been used in systems from
Sequent for many years as the basis for their shared memory parallel
processing support. Despite some very negative comments in the (quite
old) man page, Linux seems to correctly perform at least some of the
basic functions, and it supports the degenerate use of this system
call to map an anonymous segment of memory that can be shared across
multiple processes.
In essence, the Linux implementation of mmap() is a plug-in
replacement for steps 2, 3, and 4 in the System V shared memory scheme
outlined in section 2.5. To create an anonymous shared memory
segment:
______________________________________________________________________
shmptr =
mmap(0, /* system assigns address */
b, /* size of shared memory segment */
(PROT_READ | PROT_WRITE), /* access rights, can be rwx */
(MAP_ANON | MAP_SHARED), /* anonymous, shared */
0, /* file descriptor (not used) */
0); /* file offset (not used) */
______________________________________________________________________
The equivalent to the System V shared memory shmdt() call is munmap():
______________________________________________________________________
munmap(shmptr, b);
______________________________________________________________________
In my opinion, there is no real benefit in using mmap() instead of the
System V shared memory support.
3. Clusters Of Linux Systems
This section attempts to give an overview of cluster parallel
processing using Linux. Clusters are currently both the most popular
and the most varied approach, ranging from a conventional network of
workstations (NOW) to essentially custom parallel machines that just
happen to use Linux PCs as processor nodes. There is also quite a lot
of software support for parallel processing using clusters of Linux
machines.
3.1. Why A Cluster?
Cluster parallel processing offers several important advantages:
o Each of the machines in a cluster can be a complete system, usable
for a wide range of other computing applications. This leads many
people to suggest that cluster parallel computing can simply claim
all the "wasted cycles" of workstations sitting idle on people's
desks. It is not really so easy to salvage those cycles, and it
will probably slow your co-worker's screen saver, but it can be
done.
o The current explosion in networked systems means that most of the
hardware for building a cluster is being sold in high volume, with
correspondingly low "commodity" prices as the result. Further
savings come from the fact that only one video card, monitor, and
keyboard are needed for each cluster (although you may need to swap
these into each machine to perform the initial installation of
Linux, once running, a typical Linux PC does not need a "console").
In comparison, SMP and attached processors are much smaller
markets, tending toward somewhat higher price per unit performance.
o Cluster computing can scale to very large systems. While it is
currently hard to find a Linux-compatible SMP with many more than
four processors, most commonly available network hardware easily
builds a cluster with up to 16 machines. With a little work,
hundreds or even thousands of machines can be networked. In fact,
the entire Internet can be viewed as one truly huge cluster.
o The fact that replacing a "bad machine" within a cluster is trivial
compared to fixing a partly faulty SMP yields much higher
availability for carefully designed cluster configurations. This
becomes important not only for particular applications that cannot
tolerate significant service interruptions, but also for general
use of systems containing enough processors so that single-machine
failures are fairly common. (For example, even though the average
time to failure of a PC might be two years, in a cluster with 32
machines, the probability that at least one will fail within 6
months is quite high.)
OK, so clusters are free or cheap and can be very large and highly
available... why doesn't everyone use a cluster? Well, there are
problems too:
o With a few exceptions, network hardware is not designed for
parallel processing. Typically latency is very high and bandwidth
relatively low compared to SMP and attached processors. For
example, SMP latency is generally no more than a few microseconds,
but is commonly hundreds or thousands of microseconds for a
cluster. SMP communication bandwidth is often more than 100
MBytes/second; although the fastest network hardware (e.g.,
"Gigabit Ethernet") offers comparable speed, the most commonly used
networks are between 10 and 1000 times slower.
The performance of network hardware is poor enough as an isolated
cluster network. If the network is not isolated from other
traffic, as is often the case using "machines that happen to be
networked" rather than a system designed as a cluster, performance
can be substantially worse.
o There is very little software support for treating a cluster as a
single system. For example, the ps command only reports the
processes running on one Linux system, not all processes running
across a cluster of Linux systems.
Thus, the basic story is that clusters offer great potential, but that
potential may be very difficult to achieve for most applications. The
good news is that there is quite a lot of software support that will
help you achieve good performance for programs that are well suited to
this environment, and there are also networks designed specifically to
widen the range of programs that can achieve good performance.
3.2. Network Hardware
Computer networking is an exploding field... but you already knew
that. An ever-increasing range of networking technologies and
products are being developed, and most are available in forms that
could be applied to make a parallel-processing cluster out of a group
of machines (i.e., PCs each running Linux).
Unfortunately, no one network technology solves all problems best; in
fact, the range of approach, cost, and performance is at first hard to
believe. For example, using standard commercially-available hardware,
the cost per machine networked ranges from less than $5 to over
$4,000. The delivered bandwidth and latency each also vary over four
orders of magnitude.
Before trying to learn about specific networks, it is important to
recognize that these things change like the wind (see
<
http://www.uk.linux.org/NetNews.html> for Linux networking news), and
it is very difficult to get accurate data about some networks.
Where I was particularly uncertain, I've placed a ?. I have spent a
lot of time researching this topic, but I'm sure my summary is full of
errors and has omitted many important things. If you have any
corrections or additions, please send email to
[email protected].
Summaries like the LAN Technology Scorecard at
<
http://web.syr.edu/~jmwobus/comfaqs/lan-technology.html> give some
characteristics of many different types of networks and LAN standards.
However, the summary in this HOWTO centers on the network properties
that are most relevant to construction of Linux clusters. The section
discussing each network begins with a short list of characteristics.
The following defines what these entries mean.
Linux support:
If the answer is no, the meaning is pretty clear. Other answers
try to describe the basic program interface that is used to
access the network. Most network hardware is interfaced via a
kernel driver, typically supporting TCP/UDP communication. Some
other networks use more direct (e.g., library) interfaces to
reduce latency by bypassing the kernel.
Years ago, it used to be considered perfectly acceptable to
access a floating point unit via an OS call, but that is now
clearly ludicrous; in my opinion, it is just as awkward for each
communication between processors executing a parallel program to
require an OS call. The problem is that computers haven't yet
integrated these communication mechanisms, so non-kernel
approaches tend to have portability problems. You are going to
hear a lot more about this in the near future, mostly in the
form of the new Virtual Interface (VI) Architecture,
<
http://www.viarch.org/>, which is a standardized method for
most network interface operations to bypass the usual OS call
layers. The VI standard is backed by Compaq, Intel, and
Microsoft, and is sure to have a strong impact on SAN (System
Area Network) designs over the next few years.
Maximum bandwidth:
This is the number everybody cares about. I have generally used
the theoretical best case numbers; your mileage will vary.
Minimum latency:
In my opinion, this is the number everybody should care about
even more than bandwidth. Again, I have used the unrealistic
best-case numbers, but at least these numbers do include all
sources of latency, both hardware and software. In most cases,
the network latency is just a few microseconds; the much larger
numbers reflect layers of inefficient hardware and software
interfaces.
Available as:
Simply put, this describes how you get this type of network
hardware. Commodity stuff is widely available from many
vendors, with price as the primary distinguishing factor.
Multiple-vendor things are available from more than one
competing vendor, but there are significant differences and
potential interoperability problems. Single-vendor networks
leave you at the mercy of that supplier (however benevolent it
may be). Public domain designs mean that even if you cannot
find somebody to sell you one, you or anybody else can buy parts
and make one. Research prototypes are just that; they are
generally neither ready for external users nor available to
them.
Interface port/bus used:
How does one hook-up this network? The highest performance and
most common now is a PCI bus interface card. There are also
EISA, VESA local bus (VL bus), and ISA bus cards. ISA was there
first, and is still commonly used for low-performance cards.
EISA is still around as the second bus in a lot of PCI machines,
so there are a few cards. These days, you don't see much VL
stuff (although <
http://www.vesa.org/> would beg to differ).
Of course, any interface that you can use without having to open
your PC's case has more than a little appeal. IrDA and USB
interfaces are appearing with increasing frequency. The
Standard Parallel Port (SPP) used to be what your printer was
plugged into, but it has seen a lot of use lately as an external
extension of the ISA bus; this new functionality is enhanced by
the IEEE 1284 standard, which specifies EPP and ECP
improvements. There is also the old, reliable, slow RS232
serial port. I don't know of anybody connecting machines using
VGA video connectors, keyboard, mouse, or game ports... so
that's about it.
Network structure:
A bus is a wire, set of wires, or fiber. A hub is a little box
that knows how to connect different wires/fibers plugged into
it; switched hubs allow multiple connections to be actively
transmitting data simultaneously.
Cost per machine connected:
Here's how to use these numbers. Suppose that, not counting the
network connection, it costs $2,000 to purchase a PC for use as
a node in your cluster. Adding a Fast Ethernet brings the per
node cost to about $2,400; adding a Myrinet instead brings the
cost to about $3,800. If you have about $20,000 to spend, that
means you could have either 8 machines connected by Fast
Ethernet or 5 machines connected by Myrinet. It also can be
very reasonable to have multiple networks; e.g., $20,000 could
buy 8 machines connected by both Fast Ethernet and TTL_PAPERS.
Pick the network, or set of networks, that is most likely to
yield a cluster that will run your application fastest.
By the time you read this, these numbers will be wrong... heck,
they're probably wrong already. There may also be quantity
discounts, special deals, etc. Still, the prices quoted here
aren't likely to be wrong enough to lead you to a totally
inappropriate choice. It doesn't take a PhD (although I do have
one ;-) to see that expensive networks only make sense if your
application needs their special properties or if the PCs being
clustered are relatively expensive.
Now that you have the disclaimers, on with the show....
3.2.1. ArcNet
o Linux support: kernel drivers
o Maximum bandwidth: 2.5 Mb/s
o Minimum latency: 1,000 microseconds?
o Available as: multiple-vendor hardware
o Interface port/bus used: ISA
o Network structure: unswitched hub or bus (logical ring)
o Cost per machine connected: $200
ARCNET is a local area network that is primarily intended for use in
embedded real-time control systems. Like Ethernet, the network is
physically organized either as taps on a bus or one or more hubs,
however, unlike Ethernet, it uses a token-based protocol logically
structuring the network as a ring. Packet headers are small (3 or 4
bytes) and messages can carry as little as a single byte of data.
Thus, ARCNET yields more consistent performance than Ethernet, with
bounded delays, etc. Unfortunately, it is slower than Ethernet and
less popular, making it more expensive. More information is available
from the ARCNET Trade Association at <
http://www.arcnet.com/>.
3.2.2. ATM
o Linux support: kernel driver, AAL* library
o Maximum bandwidth: 155 Mb/s (soon, 1,200 Mb/s)
o Minimum latency: 120 microseconds
o Available as: multiple-vendor hardware
o Interface port/bus used: PCI
o Network structure: switched hubs
o Cost per machine connected: $3,000
Unless you've been in a coma for the past few years, you have probably
heard a lot about how ATM (Asynchronous Transfer Mode) is the
future... well, sort-of. ATM is cheaper than HiPPI and faster than
Fast Ethernet, and it can be used over the very long distances that
the phone companies care about. The ATM network protocol is also
designed to provide a lower-overhead software interface and to more
efficiently manage small messages and real-time communications (e.g.,
digital audio and video). It is also one of the highest-bandwidth
networks that Linux currently supports. The bad news is that ATM
isn't cheap, and there are still some compatibility problems across
vendors. An overview of Linux ATM development is available at
<
http://lrcwww.epfl.ch/linux-atm/>.
3.2.3. CAPERS
o Linux support: AFAPI library
o Maximum bandwidth: 1.2 Mb/s
o Minimum latency: 3 microseconds
o Available as: commodity hardware
o Interface port/bus used: SPP
o Network structure: cable between 2 machines
o Cost per machine connected: $2
CAPERS (Cable Adapter for Parallel Execution and Rapid
Synchronization) is a spin-off of the PAPERS project,
<
http://garage.ecn.purdue.edu/~papers/>, at the Purdue University
School of Electrical and Computer Engineering. In essence, it defines
a software protocol for using an ordinary "LapLink" SPP-to-SPP cable
to implement the PAPERS library for two Linux PCs. The idea doesn't
scale, but you can't beat the price. As with TTL_PAPERS, to improve
system security, there is a minor kernel patch recommended, but not
required: <
http://garage.ecn.purdue.edu/~papers/giveioperm.html>.
3.2.4. Ethernet
o Linux support: kernel drivers
o Maximum bandwidth: 10 Mb/s
o Minimum latency: 100 microseconds
o Available as: commodity hardware
o Interface port/bus used: PCI
o Network structure: switched or unswitched hubs, or hubless bus
o Cost per machine connected: $100 (hubless, $50)
For some years now, 10 Mbits/s Ethernet has been the standard network
technology. Good Ethernet interface cards can be purchased for well
under $50, and a fair number of PCs now have an Ethernet controller
built-into the motherboard. For lightly-used networks, Ethernet
connections can be organized as a multi-tap bus without a hub; such
configurations can serve up to 200 machines with minimal cost, but are
not appropriate for parallel processing. Adding an unswitched hub
does not really help performance. However, switched hubs that can
provide full bandwidth to simultaneous connections cost only about
$100 per port. Linux supports an amazing range of Ethernet
interfaces, but it is important to keep in mind that variations in the
interface hardware can yield significant performance differences. See
the Hardware Compatibility HOWTO for comments on which are supported
and how well they work; also see
<
http://cesdis1.gsfc.nasa.gov/linux/drivers/>.
An interesting way to improve performance is offered by the 16-machine
Linux cluster work done in the Beowulf project,
<
http://cesdis.gsfc.nasa.gov/linux/beowulf/beowulf.html>, at NASA
CESDIS. There, Donald Becker, who is the author of many Ethernet card
drivers, has developed support for load sharing across multiple
Ethernet networks that shadow each other (i.e., share the same network
addresses). This load sharing is built-into the standard Linux
distribution, and is done invisibly below the socket operation level.
Because hub cost is significant, having each machine connected to two
or more hubless or unswitched hub Ethernet networks can be a very
cost-effective way to improve performance. In fact, in situations
where one machine is the network performance bottleneck, load sharing
using shadow networks works much better than using a single switched
hub network.
3.2.5. Ethernet (Fast Ethernet)
o Linux support: kernel drivers
o Maximum bandwidth: 100 Mb/s
o Minimum latency: 80 microseconds
o Available as: commodity hardware
o Interface port/bus used: PCI
o Network structure: switched or unswitched hubs
o Cost per machine connected: $400?
Although there are really quite a few different technologies calling
themselves "Fast Ethernet," this term most often refers to a hub-based
100 Mbits/s Ethernet that is somewhat compatible with older "10 BaseT"
10 Mbits/s devices and cables. As might be expected, anything called
Ethernet is generally priced for a volume market, and these interfaces
are generally a small fraction of the price of 155 Mbits/s ATM cards.
The catch is that having a bunch of machines dividing the bandwidth of
a single 100 Mbits/s "bus" (using an unswitched hub) yields
performance that might not even be as good on average as using 10
Mbits/s Ethernet with a switched hub that can give each machine's
connection a full 10 Mbits/s.
Switched hubs that can provide 100 Mbits/s for each machine
simultaneously are expensive, but prices are dropping every day, and
these switches do yield much higher total network bandwidth than
unswitched hubs. The thing that makes ATM switches so expensive is
that they must switch for each (relatively short) ATM cell; some Fast
Ethernet switches take advantage of the expected lower switching
frequency by using techniques that may have low latency through the
switch, but take multiple milliseconds to change the switch path...
if your routing pattern changes frequently, avoid those switches. See
<
http://cesdis1.gsfc.nasa.gov/linux/drivers/> for information about
the various cards and drivers.
Also note that, as described for Ethernet, the Beowulf project,
<
http://cesdis.gsfc.nasa.gov/linux/beowulf/beowulf.html>, at NASA has
been developing support that offers improved performance by load
sharing across multiple Fast Ethernets.
3.2.6. Ethernet (Gigabit Ethernet)
o Linux support: kernel drivers
o Maximum bandwidth: 1,000 Mb/s
o Minimum latency: 300 microseconds?
o Available as: multiple-vendor hardware
o Interface port/bus used: PCI
o Network structure: switched hubs or FDRs
o Cost per machine connected: $2,500?
I'm not sure that Gigabit Ethernet, <
http://www.gigabit-
ethernet.org/>, has a good technological reason to be called
Ethernet... but the name does accurately reflect the fact that this
is intended to be a cheap, mass-market, computer network technology
with native support for IP. However, current pricing reflects the
fact that Gb/s hardware is still a tricky thing to build.
Unlike other Ethernet technologies, Gigabit Ethernet provides for a
level of flow control that should make it a more reliable network.
FDRs, or Full-Duplex Repeaters, simply multiplex lines, using
buffering and localized flow control to improve performance. Most
switched hubs are being built as new interface modules for existing
gigabit-capable switch fabrics. Switch/FDR products have been shipped
or announced by at least <
http://www.acacianet.com/>,
<
http://www.baynetworks.com/>, <
http://www.cabletron.com/>,
<
http://www.networks.digital.com/>,
<
http://www.extremenetworks.com/>, <
http://www.foundrynet.com/>,
<
http://www.gigalabs.com/>, <
http://www.packetengines.com/>.
<
http://www.plaintree.com/>, <
http://www.prominet.com/>,
<
http://www.sun.com/>, and <
http://www.xlnt.com/>.
There is a Linux driver,
<
http://cesdis.gsfc.nasa.gov/linux/drivers/yellowfin.html>, for the
Packet Engines "Yellowfin" G-NIC, <
http://www.packetengines.com/>.
Early tests under Linux achieved about 2.5x higher bandwidth than
could be achieved with the best 100 Mb/s Fast Ethernet; with gigabit
networks, careful tuning of PCI bus use is a critical factor. There
is little doubt that driver improvements, and Linux drivers for other
NICs, will follow.
3.2.7. FC (Fibre Channel)
o Linux support: no
o Maximum bandwidth: 1,062 Mb/s
o Minimum latency: ?
o Available as: multiple-vendor hardware
o Interface port/bus used: PCI?
o Network structure: ?
o Cost per machine connected: ?
The goal of FC (Fibre Channel) is to provide high-performance block
I/O (an FC frame carries a 2,048 byte data payload), particularly for
sharing disks and other storage devices that can be directly connected
to the FC rather than connected through a computer. Bandwidth-wise,
FC is specified to be relatively fast, running anywhere between 133
and 1,062 Mbits/s. If FC becomes popular as a high-end SCSI
replacement, it may quickly become a cheap technology; for now, it is
not cheap and is not supported by Linux. A good collection of FC
references is maintained by the Fibre Channel Association at
<
http://www.amdahl.com/ext/CARP/FCA/FCA.html>
3.2.8. FireWire (IEEE 1394)
o Linux support: no
o Maximum bandwidth: 196.608 Mb/s (soon, 393.216 Mb/s)
o Minimum latency: ?
o Available as: multiple-vendor hardware
o Interface port/bus used: PCI
o Network structure: random without cycles (self-configuring)
o Cost per machine connected: $600
FireWire, <
http://www.firewire.org/>, the IEEE 1394-1995 standard, is
destined to be the low-cost high-speed digital network for consumer
electronics. The showcase application is connecting DV digital video
camcorders to computers, but FireWire is intended to be used for
applications ranging from being a SCSI replacement to interconnecting
the components of your home theater. It allows up to 64K devices to
be connected in any topology using busses and bridges that does not
create a cycle, and automatically detects the configuration when
components are added or removed. Short (four-byte "quadlet") low-
latency messages are supported as well as ATM-like isochronous
transmission (used to keep multimedia messages synchronized). Adaptec
has FireWire products that allow up to 63 devices to be connected to a
single PCI interface card, and also has good general FireWire
information at <
http://www.adaptec.com/serialio/>.
Although FireWire will not be the highest bandwidth network available,
the consumer-level market (which should drive prices very low) and low
latency support might make this one of the best Linux PC cluster
message-passing network technologies within the next year or so.
3.2.9. HiPPI And Serial HiPPI
o Linux support: no
o Maximum bandwidth: 1,600 Mb/s (serial is 1,200 Mb/s)
o Minimum latency: ?
o Available as: multiple-vendor hardware
o Interface port/bus used: EISA, PCI
o Network structure: switched hubs
o Cost per machine connected: $3,500 (serial is $4,500)
HiPPI (High Performance Parallel Interface) was originally intended to
provide very high bandwidth for transfer of huge data sets between a
supercomputer and another machine (a supercomputer, frame buffer, disk
array, etc.), and has become the dominant standard for supercomputers.
Although it is an oxymoron, Serial HiPPI is also becoming popular,
typically using a fiber optic cable instead of the 32-bit wide
standard (parallel) HiPPI cables. Over the past few years, HiPPI
crossbar switches have become common and prices have dropped sharply;
unfortunately, serial HiPPI is still pricey, and that is what PCI bus
interface cards generally support. Worse still, Linux doesn't yet
support HiPPI. A good overview of HiPPI is maintained by CERN at
<
http://www.cern.ch/HSI/hippi/>; they also maintain a rather long list
of HiPPI vendors at
<
http://www.cern.ch/HSI/hippi/procintf/manufact.htm>.
3.2.10. IrDA (Infrared Data Association)
o Linux support: no?
o Maximum bandwidth: 1.15 Mb/s and 4 Mb/s
o Minimum latency: ?
o Available as: multiple-vendor hardware
o Interface port/bus used: IrDA
o Network structure: thin air ;-)
o Cost per machine connected: $0
IrDA (Infrared Data Association, <
http://www.irda.org/>) is that
little infrared device on the side of a lot of laptop PCs. It is
inherently difficult to connect more than two machines using this
interface, so it is unlikely to be used for clustering. Don Becker
did some preliminary work with IrDA.
3.2.11. Myrinet
o Linux support: library
o Maximum bandwidth: 1,280 Mb/s
o Minimum latency: 9 microseconds
o Available as: single-vendor hardware
o Interface port/bus used: PCI
o Network structure: switched hubs
o Cost per machine connected: $1,800
Myrinet <
http://www.myri.com/> is a local area network (LAN) designed
to also serve as a "system area network" (SAN), i.e., the network
within a cabinet full of machines connected as a parallel system. The
LAN and SAN versions use different physical media and have somewhat
different characteristics; generally, the SAN version would be used
within a cluster.
Myrinet is fairly conventional in structure, but has a reputation for
being particularly well-implemented. The drivers for Linux are said
to perform very well, although shockingly large performance variations
have been reported with different PCI bus implementations for the host
computers.
Currently, Myrinet is clearly the favorite network of cluster groups
that are not too severely "budgetarily challenged." If your idea of a
Linux PC is a high-end Pentium Pro or Pentium II with at least 256 MB
RAM and a SCSI RAID, the cost of Myrinet is quite reasonable.
However, using more ordinary PC configurations, you may find that your
choice is between N machines linked by Myrinet or 2N linked by
multiple Fast Ethernets and TTL_PAPERS. It really depends on what
your budget is and what types of computations you care about most.
3.2.12. Parastation
o Linux support: HAL or socket library
o Maximum bandwidth: 125 Mb/s
o Minimum latency: 2 microseconds
o Available as: single-vendor hardware
o Interface port/bus used: PCI
o Network structure: hubless mesh
o Cost per machine connected: > $1,000
The ParaStation project <
http://wwwipd.ira.uka.de/parastation> at
University of Karlsruhe Department of Informatics is building a PVM-
compatible custom low-latency network. They first constructed a two-
processor ParaPC prototype using a custom EISA card interface and PCs
running BSD UNIX, and then built larger clusters using DEC Alphas.
Since January 1997, ParaStation has been available for Linux. The PCI
cards are being made in cooperation with a company called Hitex (see
<
http://www.hitex.com:80/parastation/>). Parastation hardware
implements both fast, reliable, message transmission and simple
barrier synchronization.
3.2.13. PLIP
o Linux support: kernel driver
o Maximum bandwidth: 1.2 Mb/s
o Minimum latency: 1,000 microseconds?
o Available as: commodity hardware
o Interface port/bus used: SPP
o Network structure: cable between 2 machines
o Cost per machine connected: $2
For just the cost of a "LapLink" cable, PLIP (Parallel Line Interface
Protocol) allows two Linux machines to communicate through standard
parallel ports using standard socket-based software. In terms of
bandwidth, latency, and scalability, this is not a very serious
network technology; however, the near-zero cost and the software
compatibility are useful. The driver is part of the standard Linux
kernel distributions.
3.2.14. SCI
o Linux support: no
o Maximum bandwidth: 4,000 Mb/s
o Minimum latency: 2.7 microseconds
o Available as: multiple-vendor hardware
o Interface port/bus used: PCI, proprietary
o Network structure: ?
o Cost per machine connected: > $1,000
The goal of SCI (Scalable Coherent Interconnect, ANSI/IEEE 1596-1992)
is essentially to provide a high performance mechanism that can
support coherent shared memory access across large numbers of
machines, as well various types of block message transfers. It is
fairly safe to say that the designed bandwidth and latency of SCI are
both "awesome" in comparison to most other network technologies. The
catch is that SCI is not widely available as cheap production units,
and there isn't any Linux support.
SCI primarily is used in various proprietary designs for logically-
shared physically-distributed memory machines, such as the HP/Convex
Exemplar SPP and the Sequent NUMA-Q 2000 (see
<
http://www.sequent.com/>). However, SCI is available as a PCI
interface card and 4-way switches (up to 16 machines can be connected
by cascading four 4-way switches) from Dolphin,
<
http://www.dolphinics.com/>, as their CluStar product line. A good
set of links overviewing SCI is maintained by CERN at
<
http://www.cern.ch/HSI/sci/sci.html>.
3.2.15. SCSI
o Linux support: kernel drivers
o Maximum bandwidth: 5 Mb/s to over 20 Mb/s
o Minimum latency: ?
o Available as: multiple-vendor hardware
o Interface port/bus used: PCI, EISA, ISA card
o Network structure: inter-machine bus sharing SCSI devices
o Cost per machine connected: ?
SCSI (Small Computer Systems Interconnect) is essentially an I/O bus
that is used for disk drives, CD ROMS, image scanners, etc. There are
three separate standards SCSI-1, SCSI-2, and SCSI-3; Fast and Ultra
speeds; and data path widths of 8, 16, or 32 bits (with FireWire
compatibility also mentioned in SCSI-3). It is all pretty confusing,
but we all know a good SCSI is somewhat faster than EIDE and can
handle more devices more efficiently.
What many people do not realize is that it is fairly simple for two
computers to share a single SCSI bus. This type of configuration is
very useful for sharing disk drives between machines and implementing
fail-over - having one machine take over database requests when the
other machine fails. Currently, this is the only mechanism supported
by Microsoft's PC cluster product, WolfPack. However, the inability
to scale to larger systems renders shared SCSI uninteresting for
parallel processing in general.
3.2.16. ServerNet
o Linux support: no
o Maximum bandwidth: 400 Mb/s
o Minimum latency: 3 microseconds
o Available as: single-vendor hardware
o Interface port/bus used: PCI
o Network structure: hexagonal tree/tetrahedral lattice of hubs
o Cost per machine connected: ?
ServerNet is the high-performance network hardware from Tandem,
<
http://www.tandem.com>. Especially in the online transation
processing (OLTP) world, Tandem is well known as a leading producer of
high-reliability systems, so it is not surprising that their network
claims not just high performance, but also "high data integrity and
reliability." Another interesting aspect of ServerNet is that it
claims to be able to transfer data from any device directly to any
device; not just between processors, but also disk drives, etc., in a
one-sided style similar to that suggested by the MPI remote memory
access mechanisms described in section 3.5. One last comment about
ServerNet: although there is just a single vendor, that vendor is
powerful enough to potentially establish ServerNet as a major
standard... Tandem is owned by Compaq.
3.2.17. SHRIMP
o Linux support: user-level memory mapped interface
o Maximum bandwidth: 180 Mb/s
o Minimum latency: 5 microseconds
o Available as: research prototype
o Interface port/bus used: EISA
o Network structure: mesh backplane (as in Intel Paragon)
o Cost per machine connected: ?
The SHRIMP project, <
http://www.CS.Princeton.EDU/shrimp/>, at the
Princeton University Computer Science Department is building a
parallel computer using PCs running Linux as the processing elements.
The first SHRIMP (Scalable, High-Performance, Really Inexpensive
Multi-Processor) was a simple two-processor prototype using a dual-
ported RAM on a custom EISA card interface. There is now a prototype
that will scale to larger configurations using a custom interface card
to connect to a "hub" that is essentially the same mesh routing
network used in the Intel Paragon (see
<
http://www.ssd.intel.com/paragon.html>). Considerable effort has
gone into developing low-overhead "virtual memory mapped
communication" hardware and support software.
3.2.18. SLIP
o Linux support: kernel drivers
o Maximum bandwidth: 0.1 Mb/s
o Minimum latency: 1,000 microseconds?
o Available as: commodity hardware
o Interface port/bus used: RS232C
o Network structure: cable between 2 machines
o Cost per machine connected: $2
Although SLIP (Serial Line Interface Protocol) is firmly planted at
the low end of the performance spectrum, SLIP (or CSLIP or PPP) allows
two machines to perform socket communication via ordinary RS232 serial
ports. The RS232 ports can be connected using a null-modem RS232
serial cable, or they can even be connected via dial-up through a
modem. In any case, latency is high and bandwidth is low, so SLIP
should be used only when no other alternatives are available. It is
worth noting, however, that most PCs have two RS232 ports, so it would
be possible to network a group of machines simply by connecting the
machines as a linear array or as a ring. There is even load sharing
software called EQL.
3.2.19. TTL_PAPERS
o Linux support: AFAPI library
o Maximum bandwidth: 1.6 Mb/s
o Minimum latency: 3 microseconds
o Available as: public-domain design, single-vendor hardware
o Interface port/bus used: SPP
o Network structure: tree of hubs
o Cost per machine connected: $100
The PAPERS (Purdue's Adapter for Parallel Execution and Rapid
Synchronization) project, <
http://garage.ecn.purdue.edu/~papers/>, at
the Purdue University School of Electrical and Computer Engineering is
building scalable, low-latency, aggregate function communication
hardware and software that allows a parallel supercomputer to be built
using unmodified PCs/workstations as nodes.
There have been over a dozen different types of PAPERS hardware built
that connect to PCs/workstations via the SPP (Standard Parallel Port),
roughly following two development lines. The versions called "PAPERS"
target higher performance, using whatever technologies are
appropriate; current work uses FPGAs, and high bandwidth PCI bus
interface designs are also under development. In contrast, the
versions called "TTL_PAPERS" are designed to be easily reproduced
outside Purdue, and are remarkably simple public domain designs that
can be built using ordinary TTL logic. One such design is produced
commercially, <
http://chelsea.ios.com:80/~hgdietz/sbm4.html>.
Unlike the custom hardware designs from other universities, TTL_PAPERS
clusters have been assembled at many universities from the USA to
South Korea. Bandwidth is severely limited by the SPP connections,
but PAPERS implements very low latency aggregate function
communications; even the fastest message-oriented systems cannot
provide comparable performance on those aggregate functions. Thus,
PAPERS is particularly good for synchronizing the displays of a video
wall (to be discussed further in the upcoming Video Wall HOWTO),
scheduling accesses to a high-bandwidth network, evaluating global
fitness in genetic searches, etc. Although PAPERS clusters have been
built using IBM PowerPC AIX, DEC Alpha OSF/1, and HP PA-RISC HP-UX
machines, Linux-based PCs are the platforms best supported.
User programs using TTL_PAPERS AFAPI directly access the SPP hardware
port registers under Linux, without an OS call for each access. To do
this, AFAPI first gets port permission using either iopl() or
ioperm(). The problem with these calls is that both require the user
program to be privileged, yielding a potential security hole. The
solution is an optional kernel patch,
<
http://garage.ecn.purdue.edu/~papers/giveioperm.html>, that allows a
privileged process to control port permission for any process.
3.2.20. USB (Universal Serial Bus)
o Linux support: kernel driver
o Maximum bandwidth: 12 Mb/s
o Minimum latency: ?
o Available as: commodity hardware
o Interface port/bus used: USB
o Network structure: bus
o Cost per machine connected: $5?
USB (Universal Serial Bus, <
http://www.usb.org/>) is a hot-pluggable
conventional-Ethernet-speed, bus for up to 127 peripherals ranging
from keyboards to video conferencing cameras. It isn't really clear
how multiple computers get connected to each other using USB. In any
case, USB ports are quickly becoming as standard on PC motherboards as
RS232 and SPP, so don't be surprised if one or two USB ports are
lurking on the back of the next PC you buy. Development of a Linux
driver is discussed at <
http://peloncho.fis.ucm.es/~inaky/USB.html>.
In some ways, USB is almost the low-performance, zero-cost, version of
FireWire that you can purchase today.
3.2.21. WAPERS
o Linux support: AFAPI library
o Maximum bandwidth: 0.4 Mb/s
o Minimum latency: 3 microseconds
o Available as: public-domain design
o Interface port/bus used: SPP
o Network structure: wiring pattern between 2-64 machines
o Cost per machine connected: $5
WAPERS (Wired-AND Adapter for Parallel Execution and Rapid
Synchronization) is a spin-off of the PAPERS project,
<
http://garage.ecn.purdue.edu/~papers/>, at the Purdue University
School of Electrical and Computer Engineering. If implemented
properly, the SPP has four bits of open-collector output that can be
wired together across machines to implement a 4-bit wide wired AND.
This wired-AND is electrically touchy, and the maximum number of
machines that can be connected in this way critically depends on the
analog properties of the ports (maximum sink current and pull-up
resistor value); typically, up to 7 or 8 machines can be networked by
WAPERS. Although cost and latency are very low, so is bandwidth;
WAPERS is much better as a second network for aggregate operations
than as the only network in a cluster. As with TTL_PAPERS, to improve
system security, there is a minor kernel patch recommended, but not
required: <
http://garage.ecn.purdue.edu/~papers/giveioperm.html>.
3.3. Network Software Interface
Before moving on to discuss the software support for parallel
applications, it is useful to first briefly cover the basics of low-
level software interface to the network hardware. There are really
only three basic choices: sockets, device drivers, and user-level
libraries.
3.3.1. Sockets
By far the most common low-level network interface is a socket
interface. Sockets have been a part of unix for over a decade, and
most standard network hardware is designed to support at least two
types of socket protocols: UDP and TCP. Both types of socket allow
you to send arbitrary size blocks of data from one machine to another,
but there are several important differences. Typically, both yield a
minimum latency of around 1,000 microseconds, although performance can
be far worse depending on network traffic.
These socket types are the basic network software interface for most
of the portable, higher-level, parallel processing software; for
example, PVM uses a combination of UDP and TCP, so knowing the
difference will help you tune performance. For even better
performance, you can also use these mechanisms directly in your
program. The following is just a simple overview of UDP and TCP; see
the manual pages and a good network programming book for details.
3.3.1.1. UDP Protocol (SOCK_DGRAM)
UDP is the User Datagram Protocol, but you more easily can remember
the properties of UDP as Unreliable Datagram Processing. In other
words, UDP allows each block to be sent as an individual message, but
a message might be lost in transmission. In fact, depending on
network traffic, UDP messages can be lost, can arrive multiple times,
or can arrive in an order different from that in which they were sent.
The sender of a UDP message does not automatically get an
acknowledgment, so it is up to user-written code to detect and
compensate for these problems. Fortunately, UDP does ensure that if a
message arrives, the message contents are intact (i.e., you never get
just part of a UDP message).
The nice thing about UDP is that it tends to be the fastest socket
protocol. Further, UDP is "connectionless," which means that each
message is essentially independent of all others. A good analogy is
that each message is like a letter to be mailed; you might send
multiple letters to the same address, but each one is independent of
the others and there is no limit on how many people you can send
letters to.
3.3.1.2. TCP Protocol (SOCK_STREAM)
Unlike UDP, TCP is a reliable, connection-based, protocol. Each block
sent is not seen as a message, but as a block of data within an
apparently continuous stream of bytes being transmitted through a
connection between sender and receiver. This is very different from
UDP messaging because each block is simply part of the byte stream and
it is up to the user code to figure-out how to extract each block from
the byte stream; there are no markings separating messages. Further,
the connections are more fragile with respect to network problems, and
only a limited number of connections can exist simultaneously for each
process. Because it is reliable, TCP generally implies significantly
more overhead than UDP.
There are, however, a few pleasant surprises about TCP. One is that,
if multiple messages are sent through a connection, TCP is able to
pack them together in a buffer to better match network hardware packet
sizes, potentially yielding better-than-UDP performance for groups of
short or oddly-sized messages. The other bonus is that networks
constructed using reliable direct physical links between machines can
easily and efficiently simulate TCP connections. For example, this
was done for the ParaStation's "Socket Library" interface software,
which provides TCP semantics using user-level calls that differ from
the standard TCP OS calls only by the addition of the prefix PSS to
each function name.
3.3.2. Device Drivers
When it comes to actually pushing data onto the network or pulling
data off the network, the standard unix software interface is a part
of the unix kernel called a device driver. UDP and TCP don't just
transport data, they also imply a fair amount of overhead for socket
management. For example, something has to manage the fact that
multiple TCP connections can share a single physical network
interface. In contrast, a device driver for a dedicated network
interface only needs to implement a few simple data transport
functions. These device driver functions can then be invoked by user
programs by using open() to identify the proper device and then using
system calls like read() and write() on the open "file." Thus, each
such operation could transport a block of data with little more than
the overhead of a system call, which might be as fast as tens of
microseconds.
Writing a device driver to be used with Linux is not hard... if you
know precisely how the device hardware works. If you are not sure how
it works, don't guess. Debugging device drivers isn't fun and
mistakes can fry hardware. However, if that hasn't scared you off, it
may be possible to write a device driver to, for example, use
dedicated Ethernet cards as dumb but fast direct machine-to-machine
connections without the usual Ethernet protocol overhead. In fact,
that's pretty much what some early Intel supercomputers did.... Look
at the Device Driver HOWTO for more information.
3.3.3. User-Level Libraries
If you've taken an OS course, user-level access to hardware device
registers is exactly what you have been taught never to do, because
one of the primary purposes of an OS is to control device access.
However, an OS call is at least tens of microseconds of overhead. For
custom network hardware like TTL_PAPERS, which can perform a basic
network operation in just 3 microseconds, such OS call overhead is
intolerable. The only way to avoid that overhead is to have user-
level code - a user-level library - directly access hardware device
registers. Thus, the question becomes one of how a user-level library
can access hardware directly, yet not compromise the OS control of
device access rights.
On a typical system, the only way for a user-level library to directly
access hardware device registers is to:
1. At user program start-up, use an OS call to map the page of memory
address space containing the device registers into the user process
virtual memory map. For some systems, the mmap() call (first
mentioned in section 2.6) can be used to map a special file which
represents the physical memory page addresses of the I/O devices.
Alternatively, it is relatively simple to write a device driver to
perform this function. Further, this device driver can control
access by only mapping the page(s) containing the specific device
registers needed, thereby maintaining OS access control.
2. Access device registers without an OS call by simply loading or
storing to the mapped addresses. For example, *((char *) 0x1234) =
5; would store the byte value 5 into memory location 1234
(hexadecimal).
Fortunately, it happens that Linux for the Intel 386 (and compatible
processors) offers an even better solution:
1. Using the ioperm() OS call from a privileged process, get
permission to access the precise I/O port addresses that correspond
to the device registers. Alternatively, permission can be managed
by an independent privileged user process (i.e., a "meta OS") using
the giveioperm() OS call
<
http://garage.ecn.purdue.edu/~papers/giveioperm.html> patch for
Linux.
2. Access device registers without an OS call by using 386 port I/O
instructions.
This second solution is preferable because it is common that multiple
I/O devices have their registers within a single page, in which case
the first technique would not provide protection against accessing
other device registers that happened to reside in the same page as the
ones intended. Of course, the down side is that 386 port I/O
instructions cannot be coded in C - instead, you will need to use a
bit of assembly code. The GCC-wrapped (usable in C programs) inline
assembly code function for a port input of a byte value is:
______________________________________________________________________
extern inline unsigned char
inb(unsigned short port)
{
unsigned char _v;
__asm__ __volatile__ ("inb %w1,%b0"
:"=a" (_v)
:"d" (port), "0" (0));
return _v;
}
______________________________________________________________________
Similarly, the GCC-wrapped code for a byte port output is:
______________________________________________________________________
extern inline void
outb(unsigned char value,
unsigned short port)
{
__asm__ __volatile__ ("outb %b0,%w1"
:/* no outputs */
:"a" (value), "d" (port));
}
______________________________________________________________________
3.4. PVM (Parallel Virtual Machine)
PVM (Parallel Virtual Machine) is a freely-available, portable,
message-passing library generally implemented on top of sockets. It
is clearly established as the de-facto standard for message-passing
cluster parallel computing.
PVM supports single-processor and SMP Linux machines, as well as
clusters of Linux machines linked by socket-capable networks (e.g.,
SLIP, PLIP, Ethernet, ATM). In fact, PVM will even work across groups
of machines in which a variety of different types of processors,
configurations, and physical networks are used - Heterogeneous
Clusters - even to the scale of treating machines linked by the
Internet as a parallel cluster. PVM also provides facilities for
parallel job control across a cluster. Best of all, PVM has long been
freely available (currently from
<
http://www.epm.ornl.gov/pvm/pvm_home.html>), which has led to many
programming language compilers, application libraries, programming and
debugging tools, etc., using it as their "portable message-passing
target library." There is also a network newsgroup,
comp.parallel.pvm.
It is important to note, however, that PVM message-passing calls
generally add significant overhead to standard socket operations,
which already had high latency. Further, the message handling calls
themselves do not constitute a particularly "friendly" programming
model.
Using the same Pi computation example first described in section 1.3,
the version using C with PVM library calls is:
______________________________________________________________________
#include <stdlib.h>
#include <stdio.h>
#include <pvm3.h>
#define NPROC 4
main(int argc, char **argv)
{
register double lsum, width;
double sum;
register int intervals, i;
int mytid, iproc, msgtag = 4;
int tids[NPROC]; /* array of task ids */
/* enroll in pvm */
mytid = pvm_mytid();
/* Join a group and, if I am the first instance,
iproc=0, spawn more copies of myself
*/
iproc = pvm_joingroup("pi");
if (iproc == 0) {
tids[0] = pvm_mytid();
pvm_spawn("pvm_pi", &argv[1], 0, NULL, NPROC-1, &tids[1]);
}
/* make sure all processes are here */
pvm_barrier("pi", NPROC);
/* get the number of intervals */
intervals = atoi(argv[1]);
width = 1.0 / intervals;
lsum = 0.0;
for (i = iproc; i<intervals; i+=NPROC) {
register double x = (i + 0.5) * width;
lsum += 4.0 / (1.0 + x * x);
}
/* sum across the local results & scale by width */
sum = lsum * width;
pvm_reduce(PvmSum, &sum, 1, PVM_DOUBLE, msgtag, "pi", 0);
/* have only the console PE print the result */
if (iproc == 0) {
printf("Estimation of pi is %f\n", sum);
}
/* Check program finished, leave group, exit pvm */
pvm_barrier("pi", NPROC);
pvm_lvgroup("pi");
pvm_exit();
return(0);
}
______________________________________________________________________
3.5. MPI (Message Passing Interface)
Although PVM is the de-facto standard message-passing library, MPI
(Message Passing Interface) is the relatively new official standard.
The home page for the MPI standard is
<
http://www.mcs.anl.gov:80/mpi/> and the newsgroup is
comp.parallel.mpi.
However, before discussing MPI, I feel compelled to say a little bit
about the PVM vs. MPI religious war that has been going on for the
past few years. I'm not really on either side. Here's my attempt at
a relatively unbiased summary of the differences:
Execution control environment.
Put simply, PVM has one and MPI doesn't specify how/if one is
implemented. Thus, things like starting a PVM program executing
are done identically everywhere, while for MPI it depends on
which implementation is being used.
Support for heterogeneous clusters.
PVM grew-up in the workstation cycle-scavenging world, and thus
directly manages heterogeneous mixes of machines and operating
systems. In contrast, MPI largely assumes that the target is an
MPP (Massively Parallel Processor) or a dedicated cluster of
nearly identical workstations.
Kitchen sink syndrome.
PVM evidences a unity of purpose that MPI 2.0 doesn't. The new
MPI 2.0 standard includes a lot of features that go way beyond
the basic message passing model - things like RMA (Remote Memory
Access) and parallel file I/O. Are these things useful? Of
course they are... but learning MPI 2.0 is a lot like learning
a complete new programming language.
User interface design.
MPI was designed after PVM, and clearly learned from it. MPI
offers simpler, more efficient, buffer handling and higher-level
abstractions allowing user-defined data structures to be
transmitted in messages.
The force of law.
By my count, there are still significantly more things designed
to use PVM than there are to use MPI; however, porting them to
MPI is easy, and the fact that MPI is backed by a widely-
supported formal standard means that using MPI is, for many
institutions, a matter of policy.
Conclusion? Well, there are at least three independently developed,
freely available, versions of MPI that can run on clusters of Linux
systems (and I wrote one of them):
o LAM (Local Area Multicomputer) is a full implementation of the MPI
1.1 standard. It allows MPI programs to be executed within an
individual Linux system or across a cluster of Linux systems using
UDP/TCP socket communication. The system includes simple execution
control facilities, as well as a variety of program development and
debugging aids. It is freely available from
<
http://www.osc.edu/lam.html>.
o MPICH (MPI CHameleon) is designed as a highly portable full
implementation of the MPI 1.1 standard. Like LAM, it allows MPI
programs to be executed within an individual Linux system or across
a cluster of Linux systems using UDP/TCP socket communication.
However, the emphasis is definitely on promoting MPI by providing
an efficient, easily retargetable, implementation. To port this
MPI implementation, one implements either the five functions of the
"channel interface" or, for better performance, the full MPICH ADI
(Abstract Device Interface). MPICH, and lots of information about
it and porting, are available from
<
http://www.mcs.anl.gov/mpi/mpich/>.
o AFMPI (Aggregate Function MPI) is a subset implementation of the
MPI 2.0 standard. This is the one that I wrote. Built on top of
the AFAPI, it is designed to showcase low-latency collective
communication functions and RMAs, and thus provides only minimal
support for MPI data types, communicators, etc. It allows C
programs using MPI to run on an individual Linux system or across a
cluster connected by AFAPI-capable network hardware. It is freely
available from <
http://garage.ecn.purdue.edu/~papers/>.
No matter which of these (or other) MPI implementations one uses, it
is fairly simple to perform the most common types of communications.
However, MPI 2.0 incorporates several communication paradigms that are
fundamentally different enough so that a programmer using one of them
might not even recognize the other coding styles as MPI. Thus, rather
than giving a single example program, it is useful to have an example
of each of the fundamentally different communication paradigms that
MPI supports. All three programs implement the same basic algorithm
(from section 1.3) that is used throughout this HOWTO to compute the
value of Pi.
The first MPI program uses basic MPI message-passing calls for each
processor to send its partial sum to processor 0, which sums and
prints the result:
______________________________________________________________________
#include <stdlib.h>
#include <stdio.h>
#include <mpi.h>
main(int argc, char **argv)
{
register double width;
double sum, lsum;
register int intervals, i;
int nproc, iproc;
MPI_Status status;
if (MPI_Init(&argc, &argv) != MPI_SUCCESS) exit(1);
MPI_Comm_size(MPI_COMM_WORLD, &nproc);
MPI_Comm_rank(MPI_COMM_WORLD, &iproc);
intervals = atoi(argv[1]);
width = 1.0 / intervals;
lsum = 0;
for (i=iproc; i<intervals; i+=nproc) {
register double x = (i + 0.5) * width;
lsum += 4.0 / (1.0 + x * x);
}
lsum *= width;
if (iproc != 0) {
MPI_Send(&lbuf, 1, MPI_DOUBLE, 0, 0, MPI_COMM_WORLD);
} else {
sum = lsum;
for (i=1; i<nproc; ++i) {
MPI_Recv(&lbuf, 1, MPI_DOUBLE, MPI_ANY_SOURCE,
MPI_ANY_TAG, MPI_COMM_WORLD, &status);
sum += lsum;
}
printf("Estimation of pi is %f\n", sum);
}
MPI_Finalize();
return(0);
}
______________________________________________________________________
The second MPI version uses collective communication (which, for this
particular application, is clearly the most appropriate):
______________________________________________________________________
#include <stdlib.h>
#include <stdio.h>
#include <mpi.h>
main(int argc, char **argv)
{
register double width;
double sum, lsum;
register int intervals, i;
int nproc, iproc;
if (MPI_Init(&argc, &argv) != MPI_SUCCESS) exit(1);
MPI_Comm_size(MPI_COMM_WORLD, &nproc);
MPI_Comm_rank(MPI_COMM_WORLD, &iproc);
intervals = atoi(argv[1]);
width = 1.0 / intervals;
lsum = 0;
for (i=iproc; i<intervals; i+=nproc) {
register double x = (i + 0.5) * width;
lsum += 4.0 / (1.0 + x * x);
}
lsum *= width;
MPI_Reduce(&lsum, &sum, 1, MPI_DOUBLE,
MPI_SUM, 0, MPI_COMM_WORLD);
if (iproc == 0) {
printf("Estimation of pi is %f\n", sum);
}
MPI_Finalize();
return(0);
}
______________________________________________________________________
The third MPI version uses the MPI 2.0 RMA mechanism for each
processor to add its local lsum into sum on processor 0:
______________________________________________________________________
#include <stdlib.h>
#include <stdio.h>
#include <mpi.h>
main(int argc, char **argv)
{
register double width;
double sum = 0, lsum;
register int intervals, i;
int nproc, iproc;
MPI_Win sum_win;
if (MPI_Init(&argc, &argv) != MPI_SUCCESS) exit(1);
MPI_Comm_size(MPI_COMM_WORLD, &nproc);
MPI_Comm_rank(MPI_COMM_WORLD, &iproc);
MPI_Win_create(&sum, sizeof(sum), sizeof(sum),
0, MPI_COMM_WORLD, &sum_win);
MPI_Win_fence(0, sum_win);
intervals = atoi(argv[1]);
width = 1.0 / intervals;
lsum = 0;
for (i=iproc; i<intervals; i+=nproc) {
register double x = (i + 0.5) * width;
lsum += 4.0 / (1.0 + x * x);
}
lsum *= width;
MPI_Accumulate(&lsum, 1, MPI_DOUBLE, 0, 0,
1, MPI_DOUBLE, MPI_SUM, sum_win);
MPI_Win_fence(0, sum_win);
if (iproc == 0) {
printf("Estimation of pi is %f\n", sum);
}
MPI_Finalize();
return(0);
}
______________________________________________________________________
It is useful to note that the MPI 2.0 RMA mechanism very neatly
overcomes any potential problems with the corresponding data structure
on various processors residing at different memory locations. This is
done by referencing a "window" that implies the base address,
protection against out-of-bound accesses, and even address scaling.
Efficient implementation is aided by the fact that RMA processing may
be delayed until the next MPI_Win_fence. In summary, the RMA
mechanism may be a strange cross between distributed shared memory and
message passing, but it is a very clean interface that potentially
generates very efficient communication.
3.6. AFAPI (Aggregate Function API)
Unlike PVM, MPI, etc., the AFAPI (Aggregate Function Application
Program Interface) did not start life as an attempt to build a
portable abstract interface layered on top of existing network
hardware and software. Rather, AFAPI began as the very hardware-
specific low-level support library for PAPERS (Purdue's Adapter for
Parallel Execution and Rapid Synchronization; see
<
http://garage.ecn.purdue.edu/~papers/>).
PAPERS was discussed briefly in section 3.2; it is a public domain
design custom aggregate function network that delivers latencies as
low as a few microseconds. However, the important thing about PAPERS
is that it was developed as an attempt to build a supercomputer that
would be a better target for compiler technology than existing
supercomputers. This is qualitatively different from most Linux
cluster efforts and PVM/MPI, which generally focus on trying to use
standard networks for the relatively few sufficiently coarse-grain
parallel applications. The fact that Linux PCs are used as components
of PAPERS systems is simply an artifact of implementing prototypes in
the most cost-effective way possible.
The need for a common low-level software interface across more than a
dozen different prototype implementations was what made the PAPERS
library become standardized as AFAPI. However, the model used by
AFAPI is inherently simpler and better suited for the finer-grain
interactions typical of code compiled by parallelizing compilers or
written for SIMD architectures. The simplicity of the model not only
makes PAPERS hardware easy to build, but also yields surprisingly
efficient AFAPI ports for a variety of other hardware systems, such as
SMPs.
AFAPI currently runs on Linux clusters connected using TTL_PAPERS,
CAPERS, or WAPERS. It also runs (without OS calls or even bus-lock
instructions, see section 2.2) on SMP systems using a System V Shared
Memory library called SHMAPERS. A version that runs across Linux
clusters using UDP broadcasts on conventional networks (e.g.,
Ethernet) is under development. All released versions are available
from <
http://garage.ecn.purdue.edu/~papers/>. All versions of the
AFAPI are designed to be called from C or C++.
The following example program is the AFAPI version of the Pi
computation described in section 1.3.
______________________________________________________________________
#include <stdlib.h>
#include <stdio.h>
#include "afapi.h"
main(int argc, char **argv)
{
register double width, sum;
register int intervals, i;
if (p_init()) exit(1);
intervals = atoi(argv[1]);
width = 1.0 / intervals;
sum = 0;
for (i=IPROC; i<intervals; i+=NPROC) {
register double x = (i + 0.5) * width;
sum += 4.0 / (1.0 + x * x);
}
sum = p_reduceAdd64f(sum) * width;
if (IPROC == CPROC) {
printf("Estimation of pi is %f\n", sum);
}
p_exit();
return(0);
}
______________________________________________________________________
3.7. Other Cluster Support Libraries
In addition to PVM, MPI, and AFAPI, the following libraries offer
features that may be useful in parallel computing using a cluster of
Linux systems. These systems are given a lighter treatment in this
document simply because, unlike PVM, MPI, and AFAPI, I have little or
no direct experience with the use of these systems on Linux clusters.
If you find any of these or other libraries to be especially useful,
please send email to me at
[email protected] describing what
you've found, and I will consider adding an expanded section on that
library.
3.7.1. Condor (process migration support)
Condor is a distributed resource management system that can manage
large heterogeneous clusters of workstations. Its design has been
motivated by the needs of users who would like to use the unutilized
capacity of such clusters for their long-running, computation-
intensive jobs. Condor preserves a large measure of the originating
machine's environment on the execution machine, even if the
originating and execution machines do not share a common file system
and/or password mechanisms. Condor jobs that consist of a single
process are automatically checkpointed and migrated between
workstations as needed to ensure eventual completion.
Condor is available at <
http://www.cs.wisc.edu/condor/>. A Linux
port exists; more information is available at
<
http://www.cs.wisc.edu/condor/linux/linux.html>. Contact condor-
[email protected] for details.
3.7.2. DFN-RPC (German Research Network - Remote Procedure Call)
The DFN-RPC, a (German Research Network Remote Procedure Call) tool,
was developed to distribute and parallelize scientific-technical
application programs between a workstation and a compute server or a
cluster. The interface is optimized for applications written in
fortran, but the DFN-RPC can also be used in a C environment. It has
been ported to Linux. More information is at <
ftp://ftp.uni-
stuttgart.de/pub/rus/dfn_rpc/README_dfnrpc.html>.
3.7.3. DQS (Distributed Queueing System)
Not exactly a library, DQS 3.0 (Distributed Queueing System) is a job
queueing system that has been developed and tested under Linux. It is
designed to allow both use and administration of a heterogeneous
cluster as a single entity. It is available from
<
http://www.scri.fsu.edu/~pasko/dqs.html>.
There is also a commercial version called CODINE 4.1.1 (COmputing in
DIstributed Network Environments). Information on it is available
from <
http://www.genias.de/genias_welcome.html>.
3.8. General Cluster References
Because clusters can be constructed and used in so many different
ways, there are quite a few groups that have made interesting
contributions. The following are references to various cluster-
related projects that may be of general interest. This includes a mix
of Linux-specific and generic cluster references. The list is given
in alphabetical order.
3.8.1. Beowulf
The Beowulf project, <
http://cesdis1.gsfc.nasa.gov/beowulf/>, centers
on production of software for using off-the-shelf clustered
workstations based on commodity PC-class hardware, a high-bandwidth
cluster-internal network, and the Linux operating system.
Thomas Sterling has been the driving force behind Beowulf, and
continues to be an eloquent and outspoken proponent of Linux
clustering for scientific computing in general. In fact, many groups
now refer to their clusters as "Beowulf class" systems - even if the
cluster isn't really all that similar to the official Beowulf design.
Don Becker, working in support of the Beowulf project, has produced
many of the network drivers used by Linux in general. Many of these
drivers have even been adapted for use in BSD. Don also is
responsible for many of these Linux network drivers allowing load-
sharing across multiple parallel connections to achieve higher
bandwidth without expensive switched hubs. This type of load sharing
was the original distinguishing feature of the Beowulf cluster.
3.8.2. Linux/AP+
The Linux/AP+ project, <
http://cap.anu.edu.au/cap/projects/linux/>,
is not exactly about Linux clustering, but centers on porting Linux to
the Fujitsu AP1000+ and adding appropriate parallel processing
enhancements. The AP1000+ is a commercially available SPARC-based
parallel machine that uses a custom network with a torus topology, 25
MB/s bandwidth, and 10 microsecond latency... in short, it looks a
lot like a SPARC Linux cluster.
3.8.3. Locust
The Locust project, <
http://www.ecsl.cs.sunysb.edu/~manish/locust/>,
is building a distributed virtual shared memory system that uses
compile-time information to hide message-latency and to reduce network
traffic at run time. Pupa is the underlying communication subsystem
of Locust, and is implemented using Ethernet to connect 486 PCs under
FreeBSD. Linux?
3.8.4. Midway DSM (Distributed Shared Memory)
Midway,
<
http://www.cs.cmu.edu/afs/cs.cmu.edu/project/midway/WWW/HomePage.html>,
is a software-based DSM (Distributed Shared Memory) system, not unlike
TreadMarks. The good news is that it uses compile-time aids rather
than relatively slow page-fault mechanisms, and it is free. The bad
news is that it doesn't run on Linux clusters.
3.8.5. Mosix
MOSIX modifies the BSDI BSD/OS to provide dynamic load balancing and
preemptive process migration across a networked group of PCs. This is
nice stuff not just for parallel processing, but for generally using a
cluster much like a scalable SMP. Will there be a Linux version?
Look at <
http://www.cs.huji.ac.il/mosix/> for more information.
3.8.6. NOW (Network Of Workstations)
The Berkeley NOW (Network Of Workstations) project,
<
http://now.cs.berkeley.edu/>, has led much of the push toward
parallel computing using networks of workstations. There is a lot
work going on here, all aimed toward "demonstrating a practical 100
processor system in the next few years." Alas, they don't use Linux.
3.8.7. Parallel Processing Using Linux
The parallel processing using Linux WWW site,
<
http://yara.ecn.purdue.edu/~pplinux/>, is the home of this HOWTO and
many related documents including online slides for a full-day
tutorial. Aside from the work on the PAPERS project, the Purdue
University School of Electrical and Computer Engineering generally has
been a leader in parallel processing; this site was established to
help others apply Linux PCs for parallel processing.
Since Purdue's first cluster of Linux PCs was assembled in February
1994, there have been many Linux PC clusters assembled at Purdue,
including several with video walls. Although these clusters used 386,
486, and Pentium systems (no Pentium Pro systems), Intel recently
awarded Purdue a donation which will allow it to construct multiple
large clusters of Pentium II systems (with as many as 165 machines
planned for a single cluster). Although these clusters all have/will
have PAPERS networks, most also have conventional networks.
3.8.8. Pentium Pro Cluster Workshop
In Des Moines, Iowa, April 10-11, 1997, AMES Laboratory held the
Pentium Pro Cluster Workshop. The WWW site from this workshop,
<
http://www.scl.ameslab.gov/workshops/PPCworkshop.html>, contains a
wealth of PC cluster information gathered from all the attendees.
3.8.9. TreadMarks DSM (Distributed Shared Memory)
DSM (Distributed Shared Memory) is a technique whereby a message-
passing system can appear to behave as an SMP. There are quite a few
such systems, most of which use the OS page-fault mechanism to trigger
message transmissions. TreadMarks,
<
http://www.cs.rice.edu/~willy/TreadMarks/overview.html>, is one of
the more efficient of such systems and does run on Linux clusters.
The bad news is "TreadMarks is being distributed at a small cost to
universities and nonprofit institutions." For more information about
the software, contact
[email protected].
3.8.10. U-Net (User-level NETwork interface architecture)
The U-Net (User-level NETwork interface architecture) project at
Cornell, <
http://www2.cs.cornell.edu/U-Net/Default.html>, attempts to
provide low-latency and high-bandwidth using commodity network
hardware by by virtualizing the network interface so that applications
can send and receive messages without operating system intervention.
U-Net runs on Linux PCs using a DECchip DC21140 based Fast Ethernet
card or a Fore Systems PCA-200 (not PCA-200E) ATM card.
3.8.11. WWT (Wisconsin Wind Tunnel)
There is really quite a lot of cluster-related work at Wisconsin. The
WWT (Wisconsin Wind Tunnel) project, <
http://www.cs.wisc.edu/~wwt/>,
is doing all sorts of work toward developing a "standard" interface
between compilers and the underlying parallel hardware. There is the
Wisconsin COW (Cluster Of Workstations), Cooperative Shared Memory and
Tempest, the Paradyn Parallel Performance Tools, etc. Unfortunately,
there is not much about Linux.
4. SIMD Within A Register (e.g., using MMX)
SIMD (Single Instruction stream, Multiple Data stream) Within A
Register (SWAR) isn't a new idea. Given a machine with k-bit
registers, data paths, and function units, it has long been known that
ordinary register operations can function as SIMD parallel operations
on n, k/n-bit, integer field values. However, it is only with the
recent push for multimedia that the 2x to 8x speedup offered by SWAR
techniques has become a concern for mainstream computing. The 1997
versions of most microprocessors incorporate hardware support for
SWAR:
o AMD K6 MMX (MultiMedia eXtensions)
o Cyrix M2 MMX (MultiMedia eXtensions)
o Digital Alpha MAX (MultimediA eXtensions)
o Hewlett-Packard PA-RISC MAX (Multimedia Acceleration eXtensions)
o Intel Pentium II & Pentium with MMX (MultiMedia eXtensions)
o Microunity Mediaprocessor SIGD (Single Instruction on Groups of
Data)
o MIPS Digital Media eXtension (MDMX, pronounced Mad Max)
o Sun SPARC V9 VIS (Visual Instruction Set)
There are a few holes in the hardware support provided by the new
microprocessors, quirks like only supporting some operations for some
field sizes. It is important to remember, however, that you don't
need any hardware support for many SWAR operations to be efficient.
For example, bitwise operations are not affected by the logical
partitioning of a register.
4.1. SWAR: What Is It Good For?
Although every modern processor is capable of executing with at least
some SWAR parallelism, the sad fact is that even the best SWAR-
enhanced instruction sets do not support very general-purpose
parallelism. In fact, many people have noticed that the performance
difference between Pentium and "Pentium with MMX technology" is often
due to things like the larger L1 cache that coincided with appearance
of MMX. So, realistically, what is SWAR (or MMX) good for?
o Integers only, the smaller the better. Two 32-bit values fit in a
64-bit MMX register, but so do eight one-byte characters or even an
entire chess board worth of one-bit values.
Note: there will be a floating-point version of MMX, although very
little has been said about it at this writing. Cyrix has posted a
set of slides, <
ftp://ftp.cyrix.com/developr/mpf97rm.pdf>, that
includes a few comments about MMFP. Apparently, MMFP will support
two 32-bit floating-point numbers to be packed into a 64-bit MMX
register; combining this with two MMFP pipelines will yield four
single-precision FLOPs per clock.
o SIMD or vector-style parallelism. The same operation is applied to
all fields simultaneously. There are ways to nullify the effects
on selected fields (i.e., equivalent to SIMD enable masking), but
they complicate coding and hurt performance.
o Localized, regular (preferably packed), memory reference patterns.
SWAR in general, and MMX in particular, are terrible at randomly-
ordered accesses; gathering a vector x[y] (where y is an index
array) is prohibitively expensive.
These are serious restrictions, but this type of parallelism occurs in
many parallel algorithms - not just multimedia applications. For the
right type of algorithm, SWAR is more effective than SMP or cluster
parallelism... and it doesn't cost anything to use it.
4.2. Introduction To SWAR Programming
The basic concept of SWAR, SIMD Within A Register, is that operations
on word-length registers can be used to speed-up computations by
performing SIMD parallel operations on n k/n-bit field values.
However, making use of SWAR technology can be awkward, and some SWAR
operations are actually more expensive than the corresponding
sequences of serial operations because they require additional
instructions to enforce the field partitioning.
To illustrate this point, let's consider a greatly simplified SWAR
mechanism that manages four 8-bit fields within each 32-bit register.
The values in two registers might be represented as:
______________________________________________________________________
PE3 PE2 PE1 PE0
+-------+-------+-------+-------+
Reg0 | D 7:0 | C 7:0 | B 7:0 | A 7:0 |
+-------+-------+-------+-------+
Reg1 | H 7:0 | G 7:0 | F 7:0 | E 7:0 |
+-------+-------+-------+-------+
______________________________________________________________________
This simply indicates that each register is viewed as essentially a
vector of four independent 8-bit integer values. Alternatively, think
of A and E as values in Reg0 and Reg1 of processing element 0 (PE0), B
and F as values in PE1's registers, and so forth.
The remainder of this document briefly reviews the basic classes of
SIMD parallel operations on these integer vectors and how these
functions can be implemented.
4.2.1. Polymorphic Operations
Some SWAR operations can be performed trivially using ordinary 32-bit
integer operations, without concern for the fact that the operation is
really intended to operate independently in parallel on these 8-bit
fields. We call any such SWAR operation polymorphic, since the
function is unaffected by the field types (sizes).
Testing if any field is non-zero is polymorphic, as are all bitwise
logic operations. For example, an ordinary bitwise-and operation (C's
& operator) performs a bitwise and no matter what the field sizes are.
A simple bitwise and of the above registers yields:
______________________________________________________________________
PE3 PE2 PE1 PE0
+---------+---------+---------+---------+
Reg2 | D&H 7:0 | C&G 7:0 | B&F 7:0 | A&E 7:0 |
+---------+---------+---------+---------+
______________________________________________________________________
Because the bitwise and operation always has the value of result bit k
affected only by the values of the operand bit k values, all field
sizes are supported using the same single instruction.
4.2.2. Partitioned Operations
Unfortunately, lots of important SWAR operations are not polymorphic.
Arithmetic operations such as add, subtract, multiply, and divide are
all subject to carry/borrow interactions between fields. We call such
SWAR operations partitioned, because each such operation must
effectively partition the operands and result to prevent interactions
between fields. However, there are actually three different methods
that can be used to achieve this effect.
4.2.2.1. Partitioned Instructions
Perhaps the most obvious approach to implementing partitioned
operations is to provide hardware support for "partitioned parallel
instructions" that cut the carry/borrow logic between fields. This
approach can yield the highest performance, but it requires a change
to the processor's instruction set and generally places many
restrictions on field size (e.g., 8-bit fields might be supported, but
not 12-bit fields).
The AMD/Cyrix/Intel MMX, Digital MAX, HP MAX, and Sun VIS all
implement restricted versions of partitioned instructions.
Unfortunately, these different instruction set extensions have
significantly different restrictions, making algorithms somewhat non-
portable between them. For example, consider the following sampling
of partitioned operations:
______________________________________________________________________
Instruction AMD/Cyrix/Intel MMX DEC MAX HP MAX Sun VIS
+---------------------+---------------------+---------+--------+---------+
| Absolute Difference | | 8 | | 8 |
+---------------------+---------------------+---------+--------+---------+
| Merge Maximum | | 8, 16 | | |
+---------------------+---------------------+---------+--------+---------+
| Compare | 8, 16, 32 | | | 16, 32 |
+---------------------+---------------------+---------+--------+---------+
| Multiply | 16 | | | 8x16 |
+---------------------+---------------------+---------+--------+---------+
| Add | 8, 16, 32 | | 16 | 16, 32 |
+---------------------+---------------------+---------+--------+---------+
______________________________________________________________________
In the table, the numbers indicate the field sizes, in bits, for which
each operation is supported. Even though the table omits many
instructions including all the more exotic ones, it is clear that
there are many differences. The direct result is that high-level
languages (HLLs) really are not very effective as programming models,
and portability is generally poor.
4.2.2.2. Unpartitioned Operations With Correction Code
Implementing partitioned operations using partitioned instructions can
certainly be efficient, but what do you do if the partitioned
operation you need is not supported by the hardware? The answer is
that you use a series of ordinary instructions to perform the
operation with carry/borrow across fields, and then correct for the
undesired field interactions.
This is a purely software approach, and the corrections do introduce
overhead, but it works with fully general field partitioning. This
approach is also fully general in that it can be used either to fill
gaps in the hardware support for partitioned instructions, or it can
be used to provide full functionality for target machines that have no
hardware support at all. In fact, by expressing the code sequences in
a language like C, this approach allows SWAR programs to be fully
portable.
The question immediately arises: precisely how inefficient is it to
simulate SWAR partitioned operations using unpartitioned operations
with correction code? Well, that is certainly the $64k question...
but many operations are not as difficult as one might expect.
Consider implementing a four-element 8-bit integer vector add of two
source vectors, x+y, using ordinary 32-bit operations.
An ordinary 32-bit add might actually yield the correct result, but
not if any 8-bit field carries into the next field. Thus, our goal is
simply to ensure that such a carry does not occur. Because adding two
k-bit fields generates an at most k+1 bit result, we can ensure that
no carry occurs by simply "masking out" the most significant bit of
each field. This is done by bitwise anding each operand with
0x7f7f7f7f and then performing an ordinary 32-bit add.
______________________________________________________________________
t = ((x & 0x7f7f7f7f) + (y & 0x7f7f7f7f));
______________________________________________________________________
That result is correct... except for the most significant bit within
each field. Computing the correct value for each field is simply a
matter of doing two 1-bit partitioned adds of the most significant
bits from x and y to the 7-bit carry result which was computed for t.
Fortunately, a 1-bit partitioned add is implemented by an ordinary
exclusive or operation. Thus, the result is simply:
______________________________________________________________________
(t ^ ((x ^ y) & 0x80808080))
______________________________________________________________________
Ok, well, maybe that isn't so simple. After all, it is six operations
to do just four adds. However, notice that the number of operations
is not a function of how many fields there are... so, with more
fields, we get speedup. In fact, we may get speedup anyway simply
because the fields were loaded and stored in a single (integer vector)
operation, register availability may be improved, and there are fewer
dynamic code scheduling dependencies (because partial word references
are avoided).
4.2.2.3. Controlling Field Values
While the other two approaches to partitioned operation implementation
both center on getting the maximum space utilization for the
registers, it can be computationally more efficient to instead control
the field values so that inter-field carry/borrow events should never
occur. For example, if we know that all the field values being added
are such that no field overflow will occur, a partitioned add
operation can be implemented using an ordinary add instruction; in
fact, given this constraint, an ordinary add instruction appears
polymorphic, and is usable for any field sizes without correction
code. The question thus becomes how to ensure that field values will
not cause carry/borrow events.
One way to ensure this property is to implement partitioned
instructions that can restrict the range of field values. The Digital
MAX vector minimum and maximum instructions can be viewed as hardware
support for clipping field values to avoid inter-field carry/borrow.
However, suppose that we do not have partitioned instructions that can
efficiently restrict the range of field values... is there a
sufficient condition that can be cheaply imposed to ensure
carry/borrow events will not interfere with adjacent fields? The
answer lies in analysis of the arithmetic properties. Adding two k-
bit numbers generates a result with at most k+1 bits; thus, a field of
k+1 bits can safely contain such an operation despite using ordinary
instructions.
Thus, suppose that the 8-bit fields in our earlier example are now
7-bit fields with 1-bit "carry/borrow spacers":
______________________________________________________________________
PE3 PE2 PE1 PE0
+----+-------+----+-------+----+-------+----+-------+
Reg0 | D' | D 6:0 | C' | C 6:0 | B' | B 6:0 | A' | A 6:0 |
+----+-------+----+-------+----+-------+----+-------+
______________________________________________________________________
A vector of 7-bit adds is performed as follows. Let us assume that,
prior to the start of any partitioned operation, all the carry spacer
bits (A', B', C', and D') have the value 0. By simply executing an
ordinary add operation, all the fields obtain the correct 7-bit
values; however, some spacer bit values might now be 1. We can
correct this by just one more conventional operation, masking-out the
spacer bits. Our 7-bit integer vector add, x+y, is thus:
______________________________________________________________________
((x + y) & 0x7f7f7f7f)
______________________________________________________________________
This is just two instructions for four adds, clearly yielding good
speedup.
The sharp reader may have noticed that setting the spacer bits to 0
does not work for subtract operations. The correction is, however,
remarkably simple. To compute x-y, we simply ensure the initial
condition that the spacers in x are all 1, while the spacers in y are
all 0. In the worst case, we would thus get:
______________________________________________________________________
(((x | 0x80808080) - y) & 0x7f7f7f7f)
______________________________________________________________________
However, the additional bitwise or operation can often be optimized
out by ensuring that the operation generating the value for x used |
0x80808080 rather than & 0x7f7f7f7f as the last step.
Which method should be used for SWAR partitioned operations? The
answer is simply "whichever yields the best speedup." Interestingly,
the ideal method to use may be different for different field sizes
within the same program running on the same machine.
4.2.3. Communication & Type Conversion Operations
Although some parallel computations, including many operations on
image pixels, have the property that the ith value in a vector is a
function only of values that appear in the ith position of the operand
vectors, this is generally not the case. For example, even pixel
operations such as smoothing require values from adjacent pixels as
operands, and transformations like FFTs require more complex (less
localized) communication patterns.
It is not difficult to efficiently implement 1-dimensional nearest
neighbor communication for SWAR using unpartitioned shift operations.
For example, to move a value from PEi to PE(i+1), a simple shift
operation suffices. If the fields are 8-bits in length, we would use:
______________________________________________________________________
(x << 8)
______________________________________________________________________
Still, it isn't always quite that simple. For example, to move a
value from PEi to PE(i-1), a simple shift operation might suffice...
but the C language does not specify if shifts right preserve the sign
bit, and some machines only provide signed shift right. Thus, in the
general case, we must explicitly zero the potentially replicated sign
bits:
______________________________________________________________________
((x >> 8) & 0x00ffffff)
______________________________________________________________________
Adding "wrap-around connections" is also reasonably efficient using
unpartitioned shifts. For example, to move a value from PEi to
PE(i+1) with wraparound:
______________________________________________________________________
((x << 8) | ((x >> 24) & 0x000000ff))
______________________________________________________________________
The real problem comes when more general communication patterns must
be implemented. Only the HP MAX instruction set supports arbitrary
rearrangement of fields with a single instruction, which is called
Permute. This Permute instruction is really misnamed; not only can it
perform an arbitrary permutation of the fields, but it also allows
repetition. In short, it implements an arbitrary x[y] operation.
Unfortunately, x[y] is very difficult to implement without such an
instruction. The code sequence is generally both long and
inefficient; in fact, it is sequential code. This is very
disappointing. The relatively high speed of x[y] operations in the
MasPar MP1/MP2 and Thinking Machines CM1/CM2/CM200 SIMD supercomputers
was one of the key reasons these machines performed well. However,
x[y] has always been slower than nearest neighbor communication, even
on those supercomputers, so many algorithms have been designed to
minimize the need for x[y] operations. In short, without hardware
support, it is probably best to develop SWAR algorithms as though x[y]
wasn't legal... or at least isn't cheap.
4.2.4. Recurrence Operations (Reductions, Scans, etc.)
A recurrence is a computation in which there is an apparently
sequential relationship between values being computed. However, if
these recurrences involve associative operations, it may be possible
to recode the computation using a tree-structured parallel algorithm.
The most common type of parallelizable recurrence is probably the
class known as associative reductions. For example, to compute the
sum of a vector's values, one commonly writes purely sequential C code
like:
______________________________________________________________________
t = 0;
for (i=0; i<MAX; ++i) t += x[i];
______________________________________________________________________
However, the order of the additions is rarely important. Floating
point and saturation math can yield different answers if the order of
additions is changed, but ordinary wrap-around integer additions will
yield the same results independent of addition order. Thus, we can
re-write this sequence into a tree-structured parallel summation in
which we first add pairs of values, then pairs of those partial sums,
and so forth, until a single final sum results. For a vector of four
8-bit values, just two addition steps are needed; the first step does
two 8-bit adds, yielding two 16-bit result fields (each containing a
9-bit result):
______________________________________________________________________
t = ((x & 0x00ff00ff) + ((x >> 8) & 0x00ff00ff));
______________________________________________________________________
The second step adds these two 9-bit values in 16-bit fields to
produce a single 10-bit result:
______________________________________________________________________
((t + (t >> 16)) & 0x000003ff)
______________________________________________________________________
Actually, the second step performs two 16-bit field adds... but the
top 16-bit add is meaningless, which is why the result is masked to a
single 10-bit result value.
Scans, also known as "parallel prefix" operations, are somewhat harder
to implement efficiently. This is because, unlike reductions, scans
produce partitioned results. For this reason, scans can be
implemented using a fairly obvious sequence of partitioned operations.
4.3. MMX SWAR Under Linux
For Linux, IA32 processors are our primary concern. The good news is
that AMD, Cyrix, and Intel all implement the same MMX instructions.
However, MMX performance varies; for example, the K6 has only one MMX
pipeline - the Pentium with MMX has two. The only really bad news is
that Intel is still running those stupid MMX commercials.... ;-)
There are really three approaches to using MMX for SWAR:
1. Use routines from an MMX library. In particular, Intel has
developed several "performance libraries,"
<
http://developer.intel.com/drg/tools/ad.htm>, that offer a variety
of hand-optimized routines for common multimedia tasks. With a
little effort, many non-multimedia algorithms can be reworked to
enable some of the most compute-intensive portions to be
implemented using one or more of these library routines. These
libraries are not currently available for Linux, but could be
ported.
2. Use MMX instructions directly. This is somewhat complicated by two
facts. The first problem is that MMX might not be available on the
processor, so an alternative implementation must also be provided.
The second problem is that the IA32 assembler generally used under
Linux does not currently recognize MMX instructions.
3. Use a high-level language or module compiler that can directly
generate appropriate MMX instructions. Such tools are currently
under development, but none is yet fully functional under Linux.
For example, at Purdue University (
<
http://dynamo.ecn.purdue.edu/~hankd/SWAR/>) we are currently
developing a compiler that will take functions written in an
explicitly parallel C dialect and will generate SWAR modules that
are callable as C functions, yet make use of whatever SWAR support
is available, including MMX. The first prototype module compilers
were built in Fall 1996, however, bringing this technology to a
usable state is taking much longer than was originally expected.
In summary, MMX SWAR is still awkward to use. However, with a little
extra effort, the second approach given above can be used now. Here
are the basics:
1. You cannot use MMX if your processor does not support it. The
following GCC code can be used to test if MMX is supported on your
processor. It returns 0 if not, non-zero if it is supported.
___________________________________________________________________
inline extern
int mmx_init(void)
{
int mmx_available;
__asm__ __volatile__ (
/* Get CPU version information */
"movl $1, %%eax\n\t"
"cpuid\n\t"
"andl $0x800000, %%edx\n\t"
"movl %%edx, %0"
: "=q" (mmx_available)
: /* no input */
);
return mmx_available;
}
___________________________________________________________________
2. An MMX register essentially holds one of what GCC would call an
unsigned long long. Thus, memory-based variables of this type
become the communication mechanism between your MMX modules and the
C programs that call them. Alternatively, you can declare your MMX
data as any 64-bit aligned data structure (it is convenient to
ensure 64-bit alignment by declaring your data type as a union with
an unsigned long long field).
3. If MMX is available, you can write your MMX code using the .byte
assembler directive to encode each instruction. This is painful
stuff to do by hand, but not difficult for a compiler to generate.
For example, the MMX instruction PADDB MM0,MM1 could be encoded as
the GCC in-line assembly code:
___________________________________________________________________
__asm__ __volatile__ (".byte 0x0f, 0xfc, 0xc1\n\t");
___________________________________________________________________
Remember that MMX uses some of the same hardware that is used for
floating point operations, so code intermixed with MMX code must not
invoke any floating point operations. The floating point stack also
should be empty before executing any MMX code; the floating point
stack is normally empty at the beginning of a C function that does not
use floating point.
4. Exit your MMX code by executing the EMMS instruction, which can be
encoded as:
___________________________________________________________________
__asm__ __volatile__ (".byte 0x0f, 0x77\n\t");
___________________________________________________________________
If the above looks very awkward and crude, it is. However, MMX is
still quite young.... future versions of this document will offer
better ways to program MMX SWAR.
5. Linux-Hosted Attached Processors
Although this approach has recently fallen out of favor, it is
virtually impossible for other parallel processing methods to achieve
the low cost and high performance possible by using a Linux system to
host an attached parallel computing system. The problem is that very
little software support is available; you are pretty much on your own.
5.1. A Linux PC Is A Good Host
In general, attached parallel processors tend to be specialized to
perform specific types of functions.
Before becoming discouraged by the fact that you are somewhat on your
own, it is useful to understand that, although it may be difficult to
get a Linux PC to appropriately host a particular system, a Linux PC
is one of the few platforms well suited to this type of use.
PCs make a good host for two primary reasons. The first is the cheap
and easy expansion capability; resources such as more memory, disks,
networks, etc., are trivially added to a PC. The second is the ease
of interfacing. Not only are ISA and PCI bus prototyping cards widely
available, but the parallel port offers reasonable performance in a
completely non-invasive interface. The IA32 separate I/O space also
facilitates interfacing by providing hardware I/O address protection
at the level of individual I/O port addresses.
Linux also makes a good host OS. The free availability of full source
code, and extensive "hacking" guides, obviously are a tremendous help.
However, Linux also provides good near-real-time scheduling, and there
is even a true real-time version of Linux at
<
http://luz.cs.nmt.edu/~rtlinux/>. Perhaps even more important is the
fact that while providing a full UNIX environment, Linux can support
development tools that were written to run under Microsoft DOS and/or
Windows. MSDOS programs can execute within a Linux process using
dosemu to provide a protected virtual machine that can literally run
MSDOS. Linux support for Windows 3.xx programs is even more direct:
free software such as wine, <
http://www.linpro.no/wine/>, simulates
Windows 3.11 well enough for most programs to execute correctly and
efficiently within a UNIX/X environment.
The following two sections give examples of attached parallel systems
that I'd like to see supported under Linux....
5.2. Did You DSP That?
There is a thriving market for high-performance DSP (Digital Signal
Processing) processors. Although these chips were generally designed
to be embedded in application-specific systems, they also make great
attached parallel computers. Why?
o Many of them, such as the Texas Instruments ( <
http://www.ti.com/>)
TMS320 and the Analog Devices ( <
http://www.analog.com/>) SHARC DSP
families, are designed to construct parallel machines with little
or no "glue" logic.
o They are cheap, especially per MIP or MFLOP. Including the cost of
basic support logic, it is not unheard of for a DSP processor to be
one tenth the cost of a PC processor with comparable performance.
o They do not use much power nor generate much heat. This means that
it is possible to have a bunch of these chips powered by a
conventional PC's power supply - and enclosing them in your PC's
case will not turn it into an oven.
o There are strange-looking things in most DSP instruction sets that
high-level (e.g., C) compilers are unlikely to use well - for
example, "Bit Reverse Addressing." Using an attached parallel
system, it is possible to straightforwardly compile and run most
code on the host, while running the most time-consuming few
algorithms on the DSPs as carefully hand-tuned code.
o These DSP processors are not really designed to run a UNIX-like OS,
and generally are not very good as stand-alone general-purpose
computer processors. For example, many do not have memory
management hardware. In other words, they work best when hosted by
a more general-purpose machine... such as a Linux PC.
Although some audio cards and modems include DSP processors that Linux
drivers can access, the big payoff comes from using an attached
parallel system that has four or more DSP processors.
Because the Texas Instruments TMS320 series,
<
http://www.ti.com/sc/docs/dsps/dsphome.htm>, has been very popular
for a long time, and it is trivial to construct a TMS320-based
parallel processor, there are quite a few such systems available.
There are both integer-only and floating-point capable versions of the
TMS320; older designs used a somewhat unusual single-precision
floating-point format, but the new models support IEEE formats. The
older TMS320C4x (aka, 'C4x) achieves up to 80 MFLOPS using the TI-
specific single-precision floating-point format; in contrast, a single
'C67x will provide up to 1 GFLOPS single-precision or 420 MFLOPS
double-precision for IEEE floating point calculations, using a VLIW-
based chip architecture called VelociTI. Not only is it easy to
configure a group of these chips as a multiprocessor, but in a single
chip, the 'C8x multiprocessor will provide a 100 MFLOPS IEEE floating-
point RISC master processor along with either two or four integer
slave DSPs.
The other DSP processor family that has been used in more than a few
attached parallel systems lately is the SHARC (aka, ADSP-2106x) from
Analog Devices <
http://www.analog.com/>. These chips can be
configured as a 6-processor shared memory multiprocessor without
external glue logic, and larger systems also can be configured using
six 4-bit links/chip. Most of the larger systems seem targeted to
military applications, and are a bit pricey. However, Integrated
Computing Engines, Inc., <
http://www.iced.com/>, makes an interesting
little two-board PCI card set called GreenICE. This unit contains an
array of 16 SHARC processors, and is capable of delivering a peak
speed of about 1.9 GFLOPS using a single-precision IEEE format.
GreenICE costs less than $5,000.
In my opinion, attached parallel DSPs really deserve a lot more
attention from the Linux parallel processing community....
5.3. FPGAs And Reconfigurable Logic Computing
If parallel processing is all about getting the highest speedup, then
why not build custom hardware? Well, we all know the answers; it
costs too much, takes too long to develop, becomes useless when we
change the algorithm even slightly, etc. However, recent advances in
electrically reprogrammable FPGAs (Field Programmable Gate Arrays)
have nullified most of those objections. Now, the gate density is
high enough so that an entire simple processor can be built within a
single FPGA, and the time to reconfigure (reprogram) an FPGA has also
been dropping to a level where it is reasonable to reconfigure even
when moving from one phase of an algorithm to the next.
This stuff is not for the weak of heart: you'll have to work with
hardware description languages like VHDL for the FPGA configuration,
as well as writing low-level code to interface to programs on the
Linux host system. However, the cost of FPGAs is low, and especially
for algorithms operating on low-precision integer data (actually, a
small superset of the stuff SWAR is good at), FPGAs can perform
complex operations just about as fast as you can feed them data. For
example, simple FPGA-based systems have yielded better-than-
supercomputer times for searching gene databases.
There are other companies making appropriate FPGA-based hardware, but
the following two companies represent a good sample.
Virtual Computer Company offers a variety of products using
dynamically reconfigurable SRAM-based Xilinx FPGAs. Their 8/16 bit
"Virtual ISA Proto Board" <
http://www.vcc.com/products/isa.html> is
less than $2,000.
The Altera ARC-PCI (Altera Reconfigurable Computer, PCI bus),
<
http://www.altera.com/html/new/pressrel/pr_arc-pci.html>, is a
similar type of card, but uses Altera FPGAs and a PCI bus interface
rather than ISA.
Many of the design tools, hardware description languages, compilers,
routers, mappers, etc., come as object code only that runs under
Windows and/or DOS. You could simply keep a disk partition with
DOS/Windows on your host PC and reboot whenever you need to use them,
however, many of these software packages may work under Linux using
dosemu or Windows emulators like wine.
6. Of General Interest
The material covered in this section applies to all four parallel
processing models for Linux.
6.1. Programming Languages And Compilers
I am primarily known as a compiler researcher, so I'd like to be able
to say that there are lots of really great compilers automatically
generating efficient parallel code for Linux systems. Unfortunately,
the truth is that it is hard to beat the performance obtained by
expressing your parallel program using various explicit communication
and other parallel operations within C code that is compiled by GCC.
The following language/compiler projects represent some of the best
efforts toward producing reasonably efficient code from high-level
languages. Generally, each is reasonably effective for the kinds of
programming tasks it targets, but none is the powerful general-purpose
language and compiler system that will make you forever stop writing C
programs to compile with GCC... which is fine. Use these languages
and compilers as they were intended, and you'll be rewarded with
shorter development times, easier debugging and maintenance, etc.
There are plenty of languages and compilers beyond those listed here
(in alphabetical order). A list of freely available compilers (most
of which have nothing to do with Linux parallel processing) is at
<
http://www.idiom.com/free-compilers/>.
6.1.1. Fortran 66/77/PCF/90/HPF/95
At least in the scientific computing community, there will always be
Fortran. Of course, now Fortran doesn't mean the same thing it did in
the 1966 ANSI standard. Basically, Fortran 66 was pretty simple
stuff. Fortran 77 added tons of features, the most noticeable of
which were the improved support for character data and the change of
DO loop semantics. PCF (Parallel Computing Forum) Fortran attempted
to add a variety of parallel processing support features to 77.
Fortran 90 is a fully-featured modern language, essentially adding
C++-like object-oriented programming features and parallel array
syntax to the 77 language. HPF (High-Performance Fortran,
<
http://www.crpc.rice.edu/HPFF/home.html>), which has itself gone
through two versions (HPF-1 and HPF-2), is essentially the enhanced,
standardized, version of what many of us used to know as CM Fortran,
MasPar Fortran, or Fortran D; it extends Fortran 90 with a variety of
parallel processing enhancements, largely focussed on specifying data
layouts. Finally, Fortran 95 represents a relatively minor
enhancement and refinement of 90.
What works with C generally can also work with f2c, g77 (a nice Linux-
specific overview is at <
http://linux.uni-
regensburg.de/psi_linux/gcc/html_g77/g77_91.html>), or the commercial
Fortran 90/95 products from
<
http://extweb.nag.co.uk/nagware/NCNJNKNM.html>. This is because all
of these compilers eventually come down to the same code-generation
used in the back-end of GCC.
Commercial Fortran parallelizers that can generate code for SMPs are
available from <
http://www.kai.com/> and
<
http://www.psrv.com/vast/vast_parallel.html>. It is not clear if
these compilers will work for SMP Linux, but it should be possible
given that the standard POSIX threads (i.e., LinuxThreads) work under
SMP Linux.
The Portland Group, <
http://www.pgroup.com/>, has commercial
parallelizing HPF Fortran (and C, C++) compilers that generate code
for SMP Linux; they also have a version targeting clusters using MPI
or PVM. FORGE/spf/xHPF products at <
http://www.apri.com/> might
also be useful for SMPs or clusters.
Freely available parallelizing Fortrans that might be made to work
with parallel Linux systems include:
o ADAPTOR (Automatic DAta Parallelism TranslaTOR,
<
http://www.gmd.de/SCAI/lab/adaptor/adaptor_home.html>), which can
translate HPF into Fortran 77/90 code with MPI or PVM calls, but
does not mention Linux.
o Fx <
http://www.cs.cmu.edu/~fx/Fx> at Carnegie Mellon targets some
workstation clusters, but Linux?
o HPFC (prototype HPF Compiler,
<
http://www.cri.ensmp.fr/~coelho/hpfc.html>) generates Fortran 77
code with PVM calls. Is it usable on a Linux cluster?
o Can PARADIGM (PARAllelizing compiler for DIstributed-memory
General-purpose Multicomputers,
<
http://www.crhc.uiuc.edu/Paradigm/>) be used with Linux?
o The Polaris compiler,
<
http://ece.www.ecn.purdue.edu/~eigenman/polaris/>, generates
Fortran code for shared memory multiprocessors, and may soon be
retargeted to PAPERS Linux clusters.
o PREPARE,
<
http://www.irisa.fr/EXTERNE/projet/pampa/PREPARE/prepare.html>,
targets MPI clusters... it is not clear if it can generate code to
run on IA32 processors.
o Combining ADAPT and ADLIB, shpf (Subset High Performance Fortran
compilation system,
<
http://www.ccg.ecs.soton.ac.uk/Projects/shpf/shpf.html>) is public
domain and generates Fortran 90 with MPI calls... so, if you have
a Fortran 90 compiler under Linux....
o SUIF (Stanford University Intermediate Form, see
<
http://suif.stanford.edu/>) has parallelizing compilers for both C
and Fortran. This is also the focus of the National Compiler
Infrastructure Project... so, is anybody targeting parallel Linux
systems?
I'm sure that I have omitted many potentially useful compilers for
various dialects of Fortran, but there are so many that it is
difficult to keep track. In the future, I would prefer to list only
those compilers known to work with Linux. Please email comments
and/or corrections to
[email protected].
6.1.2. GLU (Granular Lucid)
GLU (Granular Lucid) is a very high-level programming system based on
a hybrid programming model that combines intensional (Lucid) and
imperative models. It supports both PVM and TCP sockets. Does it run
under Linux? More information is available at
<
http://www.csl.sri.com/GLU.html>.
6.1.3. Jade And SAM
Jade is a parallel programming language that extends C to exploit
coarse-grain concurrency in sequential, imperative programs. It
assumes a distributed shared memory model, which is implemented by SAM
for workstation clusters using PVM. More information is available at
<
http://suif.stanford.edu/~scales/sam.html>.
6.1.4. Mentat And Legion
Mentat is an object-oriented parallel processing system that works
with workstation clusters and has been ported to Linux. Mentat
Programming Language (MPL) is an object-oriented programming language
based on C++. The Mentat run-time system uses something vaguely
resembling non-blocking remote procedure calls. More information is
available at <
http://www.cs.virginia.edu/~mentat/>.
Legion <
http://www.cs.virginia.edu/~legion/> is built on top on
Mentat, providing the appearance of a single virtual machine across
wide-area networked machines.
6.1.5. MPL (MasPar Programming Language)
Not to be confussed with Mentat's MPL, this language was originally
developed as the native parallel C dialect for the MasPar SIMD
supercomputers. Well, MasPar isn't really in that business any more
(they are now NeoVista Solutions, <
http://www.neovista.com>, a data
mining company), but their MPL compiler was built using GCC, so it is
still freely available. In a joint effort between the University of
Alabama at Huntsville and Purdue University, MasPar's MPL has been
retargeted to generate C code with AFAPI calls (see section 3.6), and
thus runs on both Linux SMPs and clusters. The compiler is, however,
somewhat buggy... see
<
http://www.math.luc.edu/~laufer/mspls/papers/cohen.ps>.
6.1.6. PAMS (Parallel Application Management System)
Myrias is a company selling a software product called PAMS (Parallel
Application Management System). PAMS provides very simple directives
for virtual shared memory parallel processing. Networks of Linux
machines are not yet supported. See <
http://www.myrias.com/> for
more information.
6.1.7. Parallaxis-III
Parallaxis-III is a structured programming language that extends
Modula-2 with "virtual processors and connections" for data
parallelism (a SIMD model). The Parallaxis software comprises
compilers for sequential and parallel computer systems, a debugger
(extensions to the gdb and xgbd debugger), and a large variety of
sample algorithms from different areas, especially image processing.
This runs on sequential Linux systems... an old version supported
various parallel targets, and the new version also will (e.g.,
targeting a PVM cluster). More information is available at
<
http://www.informatik.uni-stuttgart.de/ipvr/bv/p3/p3.html>.
6.1.8. pC++/Sage++
pC++/Sage++ is a language extension to C++ that permits data-parallel
style operations using "collections of objects" from some base
"element" class. It is a preprocessor generating C++ code that can
run under PVM. Does it run under Linux? More information is
available at <
http://www.extreme.indiana.edu/sage/>.
6.1.9. SR (Synchronizing Resources)
SR (Synchronizing Resources) is a concurrent programming language in
which resources encapsulate processes and the variables they share;
operations provide the primary mechanism for process interaction. SR
provides a novel integration of the mechanisms for invoking and
servicing operations. Consequently, all of local and remote procedure
call, rendezvous, message passing, dynamic process creation,
multicast, and semaphores are supported. SR also supports shared
global variables and operations.
It has been ported to Linux, but it isn't clear what parallelism it
can execute with. More information is available at
<
http://www.cs.arizona.edu/sr/www/index.html>.
6.1.10. ZPL And IronMan
ZPL is an array-based programming language intended to support
engineering and scientific applications. It generates calls to a
simple message-passing interface called IronMan, and the few functions
which constitute this interface can be easily implemented using nearly
any message-passing system. However, it is primarily targeted to PVM
and MPI on workstation clusters, and Linux is supported. More
information is available at
<
http://www.cs.washington.edu/research/projects/orca3/zpl/www/>.
6.2. Performance Issues
There are a lot of people who spend a lot of time benchmarking
particular motherboards, network cards, etc., trying to determine
which is the best. The problem with that approach is that by the time
you've been able to benchmark something, it is no longer the best
available; it even may have been taken off the market and replaced by
a revised model with entirely different properties.
Buying PC hardware is like buying orange juice. Usually, it is made
with pretty good stuff no matter what company name is on the label.
Few people know, or care, where the components (or orange juice
concentrate) came from. That said, there are some hardware
differences that you should pay attention to. My advice is simply
that you be aware of what you can expect from the hardware under
Linux, and then focus your attention on getting rapid delivery, a good
price, and a reasonable policy for returns.
An excellent overview of the different PC processors is given in
<
http://www.pcguide.com/ref/cpu/fam/>; in fact, the whole WWW site
<
http://www.pcguide.com/> is full of good technical overviews of PC
hardware. It is also useful to know a bit about performance of
specific hardware configurations, and the Linux Benchmarking HOWTO
<
http://sunsite.unc.edu/LDP/HOWTO/Benchmarking-HOWTO.html> is a good
place to start.
The Intel IA32 processors have many special registers that can be used
to measure the performance of a running system in exquisite detail.
Intel VTune, <
http://developer.intel.com/design/perftool/vtune/>,
uses the performance registers extensively in a very complete code-
tuning system... that unfortunately doesn't run under Linux. A
loadable module device driver, and library routines, for accessing the
Pentium performance registers is available from
<
http://www.cs.umd.edu/users/akinlar/driver.html>. Keep in mind that
these performance registers are different on different IA32
processors; this code works only with Pentium, not with 486, Pentium
Pro, Pentium II, K6, etc.
Another comment on performance is appropriate, especially for those of
you who want to build big clusters and put them in small spaces. At
least some modern processors incorporate thermal sensors and circuits
that are used to slow the internal clock rate if operating temperature
gets too high (an attempt to reduce heat output and improve
reliability). I'm not suggesting that everyone should go buy a
peltier device (heat pump) to cool each CPU, but you should be aware
that high operating temperature does not just shorten component life -
it also can directly reduce system performance. Do not arrange your
computers in physical configurations that block airflow, trap heat
within confined areas, etc.
Finally, performance isn't just speed, but also reliability and
availability. High reliability means that your system almost never
crashes, even when components fail... which generally requires
special features like redundant power supplies and hot-swap
motherboards. That usually isn't cheap. High availability refers to
the concept that your system is available for use nearly all the
time... the system may crash when components fail, but the system is
quickly repaired and rebooted. There is a High-Availability HOWTO
that discusses many of the basic issues. However, especially for
clusters, high availablity can be achieved simply by having a few
spares. I recommend at least one spare, and prefer to have at least
one spare for every 16 machines in a large cluster. Discarding faulty
hardware and replacing it with a spare can yield both higher
availability and lower cost than a maintenance contract.
6.3. Conclusion - It's Out There
So, is anybody doing parallel processing using Linux? Yes!
It wasn't very long ago that a lot of people were wondering if the
death of many parallel-processing supercomputer companies meant that
parallel processing was on its way out. I didn't think it was dead
then (see <
http://dynamo.ecn.purdue.edu/~hankd/Opinions/pardead.html>
for a fun overview of what I think really happened), and it seems
quite clear now that parallel processing is again on the rise. Even
Intel, which just recently stopped making parallel supercomputers, is
proud of the parallel processing support in things like MMX and the
upcoming IA64 EPIC (Explicitly Parallel Instruction Computer).
If you search for "Linux" and "parallel" with your favorite search
engine, you'll find quite a few places are involved in parallel
processing using Linux. In particular, Linux PC clusters seem to be
popping-up everywhere. The appropriateness of Linux, combined with
the low cost and high performance of PC hardware, have made parallel
processing using Linux a popular approach to supercomputing for both
small, budget-constrained, groups and large, well-funded, national
research laboratories.
Various projects listed elsewhere in this document maintain lists of
"kindred" research sites that have similar parallel Linux
configurations. However, at
<
http://yara.ecn.purdue.edu/~pplinux/Sites/>, there is a hypertext
document intended to provide photographs, descriptions, and contact
information for all the various sites using Linux systems for parallel
processing. To have information about your site posted there:
o You must have a "permanent" parallel Linux site: an SMP, cluster
of machines, SWAR system, or PC with attached processor, which is
configured to allow users to execute parallel programs under Linux.
A Linux-based software environment (e.g., PVM, MPI, AFAPI) that
directly supports parallel processing must be installed on the
system. However, the hardware need not be dedicated to parallel
processing under Linux, and may be used for completely different
purposes when parallel programs are not being run.
o Request that your site be listed. Send your site information to
[email protected]. Please follow the format used in other
entries for your site information. No site will be listed without
an explicit request from the contact person for that site.
There are 14 clusters in the current listing, but we are aware of at
least several dozen Linux clusters world-wide. Of course, listing
does not imply any endorsement, etc.; our hope is simply to increase
awareness, research, and collaboration involving parallel processing
using Linux.
