Benchmarking Beowulf Clusters Using High Performance

Benchmarking Beowulf Clusters Using High Performance
Linpack (HPL) and Cellular Automata
Rafael Saldaña, Allan Espinosa
High Performance Computing Research Group
School of Science and Engineering, Ateneo de Manila University
Loyola Heights, Quezon City, Philippines 1108
Telephone/ Fax: +63 2 426 6125,
A Beowulf cluster is a parallel computer using commercialoff-the-shelf switch-based network to communicate among
processors. Ateneo de Manila University maintains two Beowulf clusters: AGILA and MEDGRID. This papers deals
with benchmarking the AGILA and MEDGRID using High
Performance Linpack (HPL) and standard cellular automata
(CA) algorithms such as the Game of Life and Fredkins rule.
The MEDGRID cluster demonstrated a peak performance
of 40 Gflops while the AGILA cluster peaked at 3.5 Gflops.
Results also show that benchmarking Beowulf clusters using
CA algorithms is comparable with that of using HPL.
departments of the School of Science and Engineering of Ateneo de Manila University built a Beowulf cluster to support
its Computational Science Initiative [2, 3]. Called AGILA,
the cluster was primarily used for teaching parallel computing to students and for faculty and student researches in
computational science (including modeling and simulation,
coding theory, and numerical weather prediction). From an
initial eight compute nodes and one master node, the AGILA cluster has been expanded to 16 compute nodes and one
master node. It runs on Fast Ethernet network technology.
Today, the master node has the following configuration:
• AMD Athlon 600 MHz Processor
• 128 Mb SDRAM
Beowulf cluster, High Performance Linpack (HPL), Cellular
Automata, cluster computing, high performance computing,
benchmarking, performance evaluation
• 4 Disk Drives totaling 173.4 Gb of Disk space
A cluster refers to independent computers combined into
a unified system through software and networking. Clusters are typically used for High Availability (HA) for greater
reliability or High Performance Computing (HPC) to provide greater computational power than a single computer
can provide [1].
A Beowulf cluster is a scalable performance cluster based
on commodity hardware, on a private system network, with
open source software (e.g., Linux) infrastructure. The performance of Beowulf clusters can be improved proportionally
with additional nodes or machines. The commodity hardware can be any of a number of mass-market, stand-alone
compute nodes, as simple as two computers each running
Linux and sharing a file system or as complex as 1024 nodes
with a high-speed, low-latency network [1].
In the year 2000, a team of researchers from the mathematics, information systems & computer science, and physics
• 2 Intel Ethernet Express Pro 100+ Fast Ethernet Controller
The slave nodes were also upgraded and now exhibit a
heterogenous structure consisting of
• 11 AMD Athlon 600 MHz Processors
• 2 Dual Intel Pentium III 600 MHz Processors
• 1 Gb of Disk space for the slave node operating system
• 4 128 Mb, 7 64 Mb, 2 512 Mb RAM
In 2003, Ateneo de Manila University acquired another
Beowulf cluster as a result of its collaboration with Japan’s
National Institute of Advanced Industrial Science and Technology (AIST) to pilot a project on the applicability of
Grid computing techniques for fMRI analysis [4]. Called
MEDGRID, the cluster consists of 8 dual processor compute
nodes and one master node, and uses MYRINET network
technology [5]. The master node consists of the following
• Dual Intel Xeon CPU 2.8 0GHz
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• 2 Gb RAM
• 3 200 Gb harddisk drive
• Intel 82545EM Gigabit Ethernet Controller
• 3Com 3c905C Fast Ethernet Controller
The compute nodes uses a Myrinet-2000 class switch for
its compute interconnect. The eight compute nodes has the
following configuration:
• Dual Intel Xeon 2.80GHz
• 1 Gb RAM
• 80 Gb harddisk drive
• Intel 82545EM Gigabit Ethernet Controller
• Myricom M3F-PCIXD Myrinet-2000 class controller,
Lanai XP Chipset version
Figure 1: P xQ grid of processes for good load balancing
Linpack is a set of Fortran subroutines that can be used for
solving various systems of linear equations [6]. For example
given a problem to solve the linear equation.
Ax = b.
In Equation 1 the matrix A will be decomposed into a
product of well structured matrices which is easier to manipulate in order to solve the linear equation in 1. The Linpack
benchmark was originally a supplement report in the original Linpack User’s guide. As the years go by, the content of
the performance data increased. The report contains the the
performance of solving the general dense matrix 1 in double
precision arithmetic. The original benchmark evolved into
the High Performance Linpack or HPL benchmark suite and
is used by various institutions to measure the general performance of their supercomputers. The TOP500 project [7]
uses the suite to providea reliable basis for tracking and detecting trends in high performance computing since 1993.
HPL is a software package that solves a (random) dense
linear system in double precision (64 bits) arithmetic on
distributed-memory computers. It can thus be regarded as
a portable as well as freely available implementation of the
High Performance Computing Linpack Benchmark [8].
Below is a description of the HPL’s Main Algorithm [9]:
“This software package solves a linear system of order n :
Ax = b by first computing the LU factorization with row partial pivoting of the n-by-n + 1 coefficient matrix [Ab] = [[L, U ]y].
Since the lower triangular factor L is applied to b as the factorization progresses, the solution x is obtained by solving the upper
triangular system U x = y. The lower triangular matrix L is left
unpivoted and the array of pivots is not returned.
The data is distributed onto a two-dimensional P -by-Q grid of
processes according to the block-cyclic scheme to ensure ‘good’
load balance as well as the scalability of the algorithm. The n-byn + 1 coefficient matrix is first logically partitioned into nb-by-nb
blocks, that are cyclically ‘dealt’ onto the P -by-Q process grid.
This is done in both dimensions of the matrix (Figure 1).
The right-looking variant has been chosen for the main loop of
the LU factorization. This means that at each iteration of the
loop a panel of nb columns is factorized, and the trailing submatrix is updated. Note that this computation is thus logically
partitioned with the same block size nb that was used for the data
distribution (Figure 2).”
High performance computing facilities measure the of their
supercomputers in terms of FLOPS (Floating Point Operations Per Second) [10]. The HPL software package was
specifically designed to measure the performance of distributedmemory computers. The first benchmarking study of the
Figure 2: LU Factorization of the matrix
AGILA Beowulf cluste reached a peak perfomance of 3.16
Gflops [2, 3]. The benchmark provides a testing the timing
program to quantify the accuracy of the obtained solution
of the matrix and the time it took to compute.
In the current study, the authors used HPL to evaluate the
performance of the AGILA and MEDGRID clusters. This
paper serves as a second benchmark report of the AGILA
cluster. On the other hand, MEDGRID does not yet have
any published documentation on its peak performance prior
to this study.
BLAS is a collection of basic linear algebra subprograms
[11]. To succesfully build HPL, a C interface to BLAS was
used to be able to use the MPI middleware interface of the
clusters. ATLAS (Automatically Tuned Linearly Algebra
Software) [12] was used in order to automatically obtain the
parameters for efficient linear algebra computations. The
software package can be obtained from its
project page [13]. The sourcecode was downloaded as a tarball and built into both MEDGRID and AGILA. According to the INSTALL.txt file of the package, there are two
mandatory steps for a successful ATLAS installation: the
configuration and building of the source files. The Makefile
provides a step by step configuration instructions to properly tune ATLAS for MEDGRID and AGILA. The following
lines shows excerpts from a shell session in AGILA for building ATLAS.
$HOME>cd /opt
/opt>tar -xjf $HOME/atlas3.6.0.tar.bz2
/opt>cd ATLAS
/opt/ATLAS> make config
ATLAS3.6.0 configure started.
Probing to make operating system determination:
Operating system configured as Linux
Probing for architecture:
Architecture is set to ATHLON
Probing for supported ISA extensions:
AltiVec: NO.
AltiVec: NO.
ATLAS has detected that your machine has 3DNow! capability, and ATLAS
can use these instructions to dramatically speed up single precision
operations. However, 3DNow! does not use IEEE compliant arithmetic;
in particular, it does not handle NaNs or Infinities at all (operations
on them are essentially undefined), and it does not handle overflow or
underflow correctly. There may be other discrepencies as well. Therefore,
only enable 3DNow! if you are very sure that these shortcomings in
accuracy do not concern you. In general, getting the answer very fast
is no substitute for getting the *correct* answer, so just say no if
you are at all unsure.
Use 3DNow! for computation? [n]: y
Required cache flush detected as : 1048576 bytes
Looking for compilers (this may take a while):
/usr/bin/gcc : v2.95.3
F77 = /usr/bin/g77 -fomit-frame-pointer -O
CC = /usr/bin/gcc -fomit-frame-pointer -O3 -funroll-all-loops
MCC = /usr/bin/gcc -fomit-frame-pointer -O
ATLAS has default parameters for OS=’Linux’ and system=’ATHLON’.
If you want to just trust these default values, you can use express setup,
drastically reducing the amount of questions you are required to answer
use express setup? [y]: y
You need to choose a name which represents this architecture (eg. UltraSparc,
Dec21164, etc). Do not use a generic name (eg. solaris, linux), which might
apply to different hardware. This architecture name will be appended to the
name of the created make include file, and appear in all subdirectories, so
don’t make it longer than you like to type. The name should follow the rules
for file names (so don’t use punctuation and spaces, for instance).
Enter Architecture name (ARCH) [Linux_ATHLON3DNow2]: agila
<arch> set to ’agila’
The ATLAS install process is heavily file-based, and this can cause major
reliability problems when interacting with an overloaded or malfunctioning
remotely mounted filesystem. ATLAS therefore has a mechanism in place to
allow for a delay before a file is declared to not be there, so that
slow NFS (i.e., waiting for amd timout) problems can be overcome, or for
handling slightly differing clocks between server/client. This problem is
magnified if doing cross-compilation. In the question below, we ask how
much of a delay, in seconds, ATLAS should tolerate between file creation
and appearance. If you are installing on a local filesystem (eg. /tmp) or
a smooth-running NFS system, answer 0; for a moderately loaded NFS server, you
may want a value in the 10 second range, and for cross-compiling systems or
NFS servers experiencing errors, you may want to go as high as a couple
of minutes (120).
Enter File creation delay in seconds [0]: 0
I’m going to ask you for information about your Fortran 77 compiler. ATLAS
does not need Fortran77 to build, so if you don’t have a Fortran compiler,
the install can still be completed successfully. However, ATLAS built without
a Fortran compiler will not be callable from Fortran (i.e., the user should
use the C interface), and we will not be able to do full testing, since some of
the tester code is written in Fortran77.
F77 & FLAGS: /usr/bin/g77 -fomit-frame-pointer -O
CC & FLAGS: /usr/bin/gcc -fomit-frame-pointer -O3 -funroll-all-loops
MCC & FLAGS: /usr/bin/gcc -fomit-frame-pointer -O
Finding F77 to C calling conventions (this may take a while):
Calculated F77/C interoperation conventions:
Suffix F77 names with underscores with __
F77 INTEGER -> C int
F77 strings handled via standard sun style
The ATLAS team has provided a default install for your architecture. If you
want, these default values can be used, and ATLAS can skip most of the search
for your machine. This will greatly decrease the amount of time required for
the install, allow you to take advantage of any special features found by the
ATLAS team, and provide protection against install miscues caused by unreliable
timing results, assuming you really have the machine ATLAS thinks you have. If
your machine is non-standard in some way, or you just want to see the ATLAS
search for yourself, you should answer no to the following question. Otherwise,
it is highly recommended to accept the default of yes.
Use supplied default values for install? [y]: y
Creating subdirectories:
Checking for already existing subdirectories
Subdirectories successfully created.
........ no
Storing L1 cache size of 64KB.
Moving config logfiles ConfSummary.log and ConfDump.log to bin/agila/INSTALL_LOG/
Configuration completed successfully. You may want to examine the make include
file (Make.agila) for accuracy before starting the install with the command:
make install arch=agila
rm -f ./xconfig
/opt/ATLAS>make install arch=agila
After installing the dependencies on the system, HPL was
configured based on the Make.PII Linux CBLAS gm Makefile
from the hpl/setup directory. The lines pointing to the MPI
libraries were changed to indicate that MEDGRID uses the
SCore Cluster System Software [14] to utilize the Myrinet
interconnect hardware. It has its own version of the mpich
[15] MPI-1 implementation designed for the system. For
AGILA, the pre-existing CBLAS interface that was used in
its initial deployment was used.
Cellular automata (CA) are dynamic systems of discrete
lattice sites whose behavior are specified in terms of local
rules or relations. First introduced by John von Neumann
and Stanislaw Ulam in the late 1940s, cellular automata
have been used to model successfully many complex phenomena. Cellular automata are characterized by the following:
1. Space is represented bya uniform lattice.
2. Each cell contains some data and is connected to other
cells in a particular pattern.
3. time advances in descrete steps.
4. The state of each cell is governed by a set of rules that
are applied at each time step.
The general CA rule is that the state of each cell in the
next timestep depends on the state of the cell and some other
cells in the lattice. The collection of all these cells is called
the neighborhood of the cell. Some examples of CA neighborhoods are the Von Neumann neighborhood (wherein each
cell is surrounded by four nearest neighbors North, East,
South, and West) and the Moore neighborhood (wherein
each cell is surrounded by eight nearest neighbors North,
Northeast, East, Southeast, South, Southwest, West, and
Invented by John Conway in the 1960’s, he Game of Life
[16] is a simple cellular automaton based on the following
rules on a Moore neighborhood.
1. Any live cell with fewer than two neighbours dies, as
if by loneliness.
2. Any live cell with more than three neighbours dies, as
if by overcrowding.
Unpacking Architectural defaults . . . done.
Creating make include file Make.agila
Make.agila successfully created.
3. Any live cell with two or three neighbours lives, unchanged, to the next generation.
4. Any dead cell with exactly three neighbours comes to
rs rs rsrs rsrs
rsrsrs rs rsrsrs rs rs
rsrsrsrsrs rsrsrs rsrsrs rs rsrs
rsrsrsrsrsrsrs rsrs rsrsrsrs
rs rsrsrsrsrsrsrsrsrs
rs rs
rs rsrsrsrsrsrsrsrsrsrsrsrsrs
rs rsrs rsrs rsrs
rsrsrsrsrsrsrsrs rsrsrsrs
rsrsrsrsrsrsrs rs rsrsrsrsrsrsrsrsrsrsrsrs
8000 10000 12000
10000 15000 20000 25000 30000
Problem size N
Problem size N
Figure 3: HPL benchmark results to obtain AGILA’s peak using 16 processors.
Figure 4: HPL benchmark results showing MEDGRID’s peak performance using 14 processors
A variant of the Game of Life, Fredkins algorithm uses a
von Neumann neighborhood. Its rules are as follows: The
cell will be alive in the next generation if the number of
neighbors is odd. Otherwise, the cell will be dead in the
next iteration of the automaton.
Saldaña and Yu [17] made use of standard CA algorithms
to run on the AGILA cluster, using the data parallelization
approach. Their MPI implementation of the CA rules divides the CA lattice into smaller matrices and distributes
these data to N running processes. In the current study,
the authors modified the C programs to include Fredkins
CA rule.
the previous peak preformance test, MEDGRID is significantly faster than AGILA. Another interesting observation
from Figure is that for every processor added to accomplish
HPL, the performance contribution decreases. It exhibits a
variant of the “diminishing returns principle”.
The AGILA HPCS reached a peak performance of 3.4 Gigaflops. This is slightly higher than the first documented
peak performance of AGILA, i.e.,3.16 Gflops [2, 3]. In contrast to AGILA, the Ateneo’s new cluster MEDGRID, reached
a peak performance of 40 Gigaflops. But it can be noticable in Figure 4 that the distribution of HPL performance
in higher problem sizes starts to scatter from problem size
20000 to 28000. MEDGRID stabilizes to about 35 Gigaflops
as described into an exponential trendline.
To demonstrate the effects of the number of processors to
HPL, the grid ratio P and Q are changed in the HPL.dat
configuration file as described in Table 1. As expected from
1 double
8 bytes
Total Memory Size in bytes ·
The current peak performance of the two clusters were
measured by increasing the problem size N up until the entire RAM was filled up by the dense matrix. N was obtained
using simple dimensional analysis described in 2. Most of
the memory will contain the N 2 double precision floating
point numbers which is 8 bytes in size. The HPL website [8]
suggested that program should take up 80% of the cluster’s
volatile memory.
rs rs
4.1 HPL Benchmarking
rs rs
The results were plotted fitted to an exponential function
using the “fit” command of gnuplot [18].
The timing metrics of the modified CAEngine was obtained by running a 1000x1000 grid lattice for 500 iterations.
It can be observed in Figures 6 and 7 that the performance
improvements in simulation time decreases as the MPI environment tries to run more processes. This behavior is the
same as the timing metrics tests performed using HPL in
the previous section. There is also a minimal different with
the performance of the Game of Life and Fredkin’s rule. The
grid size on AGILA was constrained to small dimensions because it can only handle small amounts of data because of
its limited memory size. The same grid size is applied to run
on MEDGRID. MEDGRID executed the program 10 times
faster than AGILA as seen in Figure 7. It is interesting to
note, however, that running a single process on MEDGRID
gave better performance than two processors.
Time (seconds)
4.2 CA Benchmarking
10 12
Number of processors
Figure 5: MEDGRID HPL performance vs the number of processors using a problem size N = 10000
It can be inferred that the properties of Symmetric Multiprocessor architectures played a role in the process. It
maybe possible that the inter-process communications between the two processors in a dual CPU machine in the
MEDGRID cluster created an overhead. This may have
caused the delay during the event when two of the processors
in a compute node tried to communicate with each other.
The MEDGRID cluster is found to be significantly faster
and more efficient in performance than the AGILA cluster.
This can be attributed to the fact the MEDGRID cluster is
younger and is powered by Dual Xeon processors as compared to AGILA cluster which is made up of AMD K6 and
Athlon processors at half the clock speed. According to
the Rocks Cluster list of users [19], two CPU commodity
Pentium 4 machines can achieve a peak performance of 3
Gigaflops – which is approximately the peak speed of the
AGILA cluster. Therefore, it is highly recommended that
AGILA cluster be prepared for decommissioning or for upgrading to a newer cluster.
Although cellular automata algorithms do not use floating point operations, benchmarking Beowulf clusters using
CA is still possible, as demonstrated in this study. Both
the timing metrics using HPL and CA (see Figures 6 and
Figure 7) exhibited the same behavior in different timing
Game of Life
Fredkinś Rule
Time (seconds)
+ +
+ + + +
+ + + + + +
8 10 12 14
Number of processors
Figure 6: Timing performance of CA algorithms using AGILA with 16 processors
10 12
Number of processors
Game of Life
Fredkinś Rule
Time (seconds)
Table 1: Grid ratio configuration for HPL in processor scaling
Figure 7: Timing performance of CA algorithms in
MEDGRID with 14 processors
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