The tremendous advance in computer technology in the past decade has made it
possible to achieve the performance of a supercomputer on a very small budget.
We have built a multi-CPU cluster of Pentium PC capable of parallel
computations using the Message Passing Interface (MPI). We will discuss the
configuration, performance, and application of the cluster to our work in
physics.Comment: 3 pages, uses Latex and aipproc.cl

Chang, D.

Gregory, E. B.

Lin, Y.

Luo, X. Q.

Wang, Y. L.

Yang, J. C.

English

arXiv

X. Q. Luo

Crossref

Parallel computing on a PC cluster

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Parallel Computing on a PC Cluster
X.Q. Luo1, E.B. Gregory1, J. C. Yang2, Y. L. Wang2, D. Chang2, and Y. Lin2
1Department of Physics, Zhongshan University, Guangzhou 510275, China
2Guoxun, Ltd, Guangzhou, China
Abstract. The tremendous advance in computer technology in the past decade has made it possible to achieve the
performance of a supercomputer on a very small budget. We havbuilt a multi-CPU cluster of Pentium PC capable of
parallel computations using the Message Passing Interface(MPI). We will discuss the configuration, performance, and
application of the cluster to our work in physics.
INTRODUCTION
The lattice field theory group at the Zhongshan Uni-
versity has faced the familiar pressures of trying to bal-
ance the need for increased computational power against
the constraints of an academic research budget. The
group’s primary research interest is in lattice quantum
chromodynamics (QCD), the study of quark interactions.
This field lends itself well to numerical simulation, but
requires significant computational resources for forefront
research. Traditionally this has been the domain of super-
computers. However, in recent years, advances in tech-
nology and falling hardware prices have blurred the dis-
tinction between the definition of a supercomputer and
personal computer. Desktop machines of today are far
more powerful than the supercomputers of yesteryear.
A further development is that the fastest computers to-
day are in fact parallel computers, with multiple proces-
sors working together on a problem. Parallel computa-
tion has its limitations, the biggest being that it is applica-
ble only to problems that can be divided into concurrent
tasks. Furthermore, since communication between pro-
cessors is usually the slowest part of the computation, the
separate tasks should ideally be as independent as possi-
ble. Fortunately many computational physics problems,
including lattice QCD, fall into the category of paralleliz-
able problems. Indeed, many computational problems in
the commercial world are suitable for this type of com-
putation as well. Applications of parallel computation
include graphics and animation, telecommunications and
internet service, and many other fields heavily reliant on
computer processing.
It is possible to join multiple cheap, fast PC type com-
puters to build a parallel “supercomputer” with an arbi-
trarily high aggregate speed. A cluster of this type is
called a “Beowulf Cluster” (1) and the idea was pioneered
by the United States’ National Aeronautics and Space
Administration.
CONSTRUCTION
Hardware
One big advantage of a PC cluster over other types
of supercomputers is the low cost and easy availability of
the hardware components. All the hardware in our cluster
is available at retail computer suppliers. This gives great
flexibility in both building and the cluster and in any fu-
ture upgrades or expansions we may choose to make.
Our cluster consists of ten PC type computers, each
with two 500 MHz Pentium III processors inside. The
logic behind dual CPU machines is that one can dou-
ble the number of processors without the expense of ad-
ditional, cases, power supplies, motherboards, network
cards, et cetera. Also, the inter-node communication
speed is faster for each pair of processors in the same box
as compared to communication between separate com-
puters. Each computer has an 8GB EIDE hard drive,
128 MB of memory, a 100Mbit/s ethernet card, a simple
graphics card a floppy drive and a CDROM. In practice
the CDROM, the floppy drive, and even the graphics card
could be considered extraneous, as all interactions with
the nodes could be done through the network. However,
with these components, all of which are relatively cheap
in comparison to the total cost, the operating system in-
stallation and occasional maintenance is significantly eas-
ier. One computer has a larger hard disk (20 GB), and a
SCSI card for interaction with a tape drive. For the en-
tire cluster we have only one console consisting of a key-
board, mouse and monitor.
A fast ethernet switch handles the inter-node commu-
nication. The switch has 24 ports so there is ample room
for future expansion of the cluster to up to a total of
48 processors. Of course it is possible to link multiple
switches or use nodes with more that two processors, so
the possibilities for a larger cluster are nearly limitless.
The layout of the cluster is illustrated in Figure 1.
Software
The cluster runs on the Linux operating system. Linux
is powerful and inexpensive. It easily supports important
features like multiple processors. It allows the configura-
tion of a network file server. We have mounted the largest
hard disk on to all of the machines in the cluster. Each
machine can read and write to it as if it were physically
part of that computer. Linux also supports a network in-
formation system to share user accounts across the entire
cluster. One uses the same account and home directory,
no matter which machine he or she logs into. Standard
Linux distributions also supply C, C++, and Fortran com-
pilers.
We can use the the cluster for parallel processing by
using the message passing interface (MPI)(2), a library
of communication functions and programs that allow for
communication between processes on different CPUs.
The programmer must design the parallel algorithm so
that it appropriately divides the task among the individual
processors. He or she must then include message passing
functions in the code which allow information to be sent
and received by the various processors. MPI is one of the
most popular standards for message passing parallel pro-
gramming, and is widely used in the physics community.
Therefore we are able to share parallel programs in C,
C++, or Fortran with collaborators elsewhere in the world
who may even be running MPI on a different platform.
PERFORMANCE
Serial Benchmark
We have run the LINPACK benchmark (3), a standard
serial benchmark test on our computers to measure the
speed of a single processor. The benchmark showed that
a single 500 MHz Pentium III processor is capable of a
peak speed between 84 Mflops and 114 Mflops (million
floating point operations per second) for single precision
arithmetic and between 62 Mflop and 68 Mflop for double
precision arithmetic. The peak aggregate speed for the
entire cluster of twenty processors, is therefore about 2
Gflops.
Table 1. Comparison of performance of MPI QCD
benchmark. Comparison data from Hioki and Naka-
mura. (6)
Machine µ-sec/link MB/sec
SX-4 4.50 45
SR2201 31.4 28
Cenju-3 57.42 8.1
Paragon 149 9.0
ZSU’s Pentium cluster 7.3 11.5
QCD Benchmark
As we primarily developed the cluster for numerical
simulations of lattice QCD, we have also performed a
benchmark which specifically tests the performance in a
parallel lattice QCD code. Lattice QCD simulations are
well suited for parallelization (5) as they involve mostly
local calculations on a multi-dimensional lattice. The al-
gorithm can conveniently divide the lattice and assign the
sections to different processors. The communication be-
tween the nodes therefore is not extremely large. Hioki
and Nakamura (6) provide comparison performance data
on SX-4 (NEC), SR2201 (Hitachi), Cenju-3 (NEC) and
Paragon (Intel) machines. Specifically, we compare the
computing time per link update in microseconds per link
and the inter-node communication speed in MB/sec. The
link update is a fundamental computational task within
the QCD simulation and is therefore a useful standard.
The test was a simulation of improved pure gauge lattice
action (1× 1 plaquette and 1× 2 rectangle terms) on a
164 lattice. In each case the simulation was run on 16
processors. The results are summarised in Table 1.
Cost Comparison
We believe that such a parallel cluster of PCs may be
the cheapest solution to the problem of developing com-
puting resources for scientific simulations. In 1999, our
cluster cost about US$14,000, including all hardware and
software. This equates to roughly $7/Mflop. We can com-
pare this to a commercial supercomputer. The Cray T3E-
1200E uses 1.2 Gflop processors (4). The basic starting
model comes with six processors for a total peak speed
of 7.2 Gflops. The cost for the six node model, though,
is US$630,000, or $87.50/Mflop. Our home made super-
computer is more than an order of magnitude cheaper.
FIGURE 1. The layout of a 10 dual-CPU node cluster.
Of course this is a naive comparison, as the Cray dif-
fers in many ways. Notably, faster individual proces-
sors means serial jobs will run much faster, and parallel
programs will require fewer processors, and hence less
inter-processor communication. Furthermore the inter-
processor communication is much faster on the Cray.
It is clear, however that for numerical tasks that are
easily broken in fairly independent tasks, a farm of PCs
is an extremely economical solution by comparison. Ad-
ditionally, the PC cluster is highly scalable. PCs and their
components are so ubiquitous, that expansion of the sys-
tem is trivial. Nearly anyone with a screwdriver, can up-
grades or replaceme components so it is not nescessary to
have a service contract with a commercial vendor.
CONCLUSIONS
We feel that our parallel cluster of PC type computers
is an example of an economical way to build a powerful
computing resource for academic purposes. On an MPI
QCD benchmark simulation it compares favorably with
other MPI platforms. It is also drastically cheaper than
commercial supercomputers for the same amount of pro-
cessing speed. PC clusterssuch as this one have applica-
tions in both academia and in commercial enterprises. It
is particularly suitable for developing research groups in
countries where funding for pure research is more scarce.
We believe that our cluster may be the first such facility
at an academic physics department in mainland China.
ACKNOWLEDGEMENTS
This work is supported by the National Science Fund
for Distinguished Young Scholars (19825117), National
Science Foundation, Guangdong Provincial Natural Sci-
ence Foundation (990212) and Ministry of Education of
China. We are grateful for generous additional support
from Guoxun (Guangdong National Communication Net-
work) Ltd.. We would also like to thank Shinji Hioki of
Tezukayama University for the use of the QCDimMPI
code. The C version of the LINPACK benchmark was
written by Bonnie Toy.
REFERENCES
1. http://www.beowulf.org/.
2. http://www-unix.mcs.anl.gov/mpi/
3. http://www.netlib.org/benchmark/index.html
4. http://www.cray.com.
5. R. Gupta,General Physics Motivations for
Numerical Simulations of Quantum Field Theory
http://xxx.lanl.gov/abs/hep-lat/9905027.
6. S. Hioki, A. Nakamura.Nucl. Phys.B(Proc. Suppl.)73,
895,(1999).
7. http://tupc3472.tezukayama-u.ac.jp/QCDMPI/


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Parallel Computing on a PC Cluster

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