Transcript What is it? The Richmond Supercomputing Cluster History The Supercomputing Cluster at the University of Richmond is used to perform computationally intensive running simulations.

What is it?
The Richmond Supercomputing Cluster
History
The Supercomputing Cluster at the University of Richmond is used
to perform computationally intensive running simulations and data
analysis. It consists of one master node and 52 slave nodes of
which 49 are dual 1.4 GHz Athlon class and 3 2.0 GHz Athlon
Class.
The cluster project at Richmond began in the spring of 1999.
Professors Mike Vineyard and Jerry Gilfoyle realized the need for
an increase in computing power to support their research in
nuclear physics at the Thomas Jefferson National Accelerator
Facility (JLab). Using funds from their Department of Energy
(DOE) research grant and other monies from the University, they
put together a prototype computing cluster in the summer of 1999.
That prototype (shown below) is no longer operational.
Details on Using the Supercomputer
The Supercomputing Cluster runs on the Linux operating system
and uses the Beowulf system for managing batch jobs. Programs
are written in Fortan, Perl, or C++ and are submitted to the master
node. The master node then sends the commands to the slave
nodes and
What we are currently working on?
Out-of-Plane Measurements of the Structure Functions of the
Deuteron
Research cluster prototype consisting of 12 nodes.
Working with SpiderWulf. Students Sherwyn Granderson (left) monitors the
status of the cluster while Rusty Burrell and Kuri Gill (right) review some results from
We now know that particles called quarks are the basis for the
recent data analysis.
atoms, molecules, and atomic nuclei that form us and our world.
Nevertheless, how these quarks actually combine to form that matter
Why we need it...
is still a mystery. The deuteron is an essential testing ground for any
theory of the nuclear force because it is the simplest nucleus in
Although initial analysis of data taken in CLAS is done at Jefferson Lab, we
nature. In this project we use the unique capabilities of the CLAS
analyze this data more deeply at the University of Richmond. Performing this
detector at Jefferson Lab to make some of the first measurements of second pass analysis requires us to be able to store full data sets from a
little-known electric and magnetic properties of the deuteron. In the
particular running period, so that we don’t have to repeatedly move data on
past scattering experiments like those done at JLab were confined to and off the system. The set we are currently working on is about 1 Terabyte
reactions where the debris from the collision was in the same plane
(1,000 Gigabytes), and past data sets have been even larger. The computing
(usually horizontal) as the incoming and outgoing projectile (an
cluster has a maximum capacity of 4.4Terabytes.
electron in this case). With CLAS we can now measure particles that
are scattered out of that plane and are sensitive to effects that have
been often ignored up to now. These measurements will open a new
window into the atomic nucleus.
For more, see “Nuclear Physics at UR” poster.
University of
Richmond
Undergraduate Education
Students who choose to participate in an undergraduate research
project develop a deeper understanding of not only nuclear
physics, but how to go about solving a daunting, computational
challenge to develop a new application. One vital aspect to
studying nuclear physics is scientific computing. Along with pure
physics instruction, students are given access to the
supercomputer and learn how to use it. They learn about new
operating systems and programming languages, as well as the
networking structure and usage of the supercomputing cluster.
ENIAC: The world’s first electronic
digital computer, 1945
SpiderWulf: UR’s supercomputing
cluster, 2002-present
In addition to disk space, considerable computing power is necessary to
perform not only data analysis but also precise simulations of CLAS in order
to produce publication-quality measurements and results. For example,
running a typical simulation on our prototype cluster developed in 1999 (see
“History”) would take about 39 days. This is dar too long to expect the
analysis to be complete in a reasonable amount of time. On our current
cluster, this same job can be run over a weekend or even overnight.
The current Research cluster was assembled by LinuxLabs in the
winter of 2001-2002 and arrived in February 2002. After a
commissioning phase, the cluster became fully operational in fall,
2002. The funds for the project came from a successful grant
application by Dr. Gilfoyle and Dr. Vineyard to the Major Research
Instrumentation Program of the National Science Foundation for
$175,000. The current configuration consists of 52, 512-MByteRAM, 1.4 GHz Athlon remote nodes and one master. Each node
has a single 18-GByte disk. It is supported by 3 TBytes of space in
three RAID disks and communicating by another 1.4 GHz Athlon
fileserver.
The Power of SpiderWulf
Often in the world of computing (and specifically supercomputing), a measure of
FLOPS (floating point operations per second) is used to compare computer
performances. In order to see where our cluster stands, we measured our
machine and compared it to the top supercomputers in the world since 1993. The
# Year
Name
Location
# of processors GFLOPS
results are shown below.
1.
1993
CM-5/1024
Los Alamos National
Laboratory
1,024
59.7
2.
1996
SR2201/1024
University of Tokyo
1,024
232.4
3.
1999
ASCI Blue-Pacific
SST IBM SP604e
Lawrence Livermore
5,808
National Laboratory (LLNL)
2144
4.
2001
ASCI White, SP
Power3 375Mhz
Lawrence Livermore
National Laboratory
8,192
7,304
5.
2001
SpiderWulf
University of Richmond
53
35.99
6.
2006
eServer Blue Gene
Solution
DOE/NNSA/LLNL
131,072
280,600
The data points are GigaFLOPS per processor, as many of these supercomputers
contain hundreds or thousands of processors. The red points represent the top
supercomputer of each year, and the blue point represents where our cluster fell
in the year that it was purchased.
For further comparison, we measured the FLOPS of a typical desktop PC, and
compared to our measure of the cluster….