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Surveying the wide range of parallel system architectures offered in the
supercomputer market, an Ohio
State University
researcher recently sought to establish some side-by-side performance
comparisons.
The journal Concurrency and
Computation: Practice and Experience, in February, published “Parallel
solution of the subset-sum problem: an empirical study.” The paper is
based upon a master’s thesis written last year by computer science and
engineering graduate student Saniyah Bokhari.
“We explore the parallelization of the subset-sum problem on
three contemporary but very different architectures, a 128-processor Cray
massively multithreaded machine, a 16-processor IBM shared memory machine, and
a 240-core NVIDIA graphics processing unit,” said Bokhari. “These experiments
highlighted the strengths and weaknesses of these architectures in the context
of a well-defined combinatorial problem.”
Bokhari evaluated the conventional central processing unit
architecture of the IBM 1350 Glenn Cluster at the Ohio Supercomputer Center
(OSC) and the less-traditional general-purpose graphic processing unit (GPGPU)
architecture, available on the same cluster. She also evaluated the
multithreaded architecture of a Cray Extreme Multithreading (XMT) supercomputer
at the Pacific Northwest National Laboratory’s (PNNL) Center for Adaptive
Supercomputing Software.
“Ms. Bokhari’s work provides valuable insights into
matching the best high-performance computing architecture with the
computational needs of a given research community,” noted Ashok
Krishnamurthy, interim co-executive director of OSC. “These systems are
continually evolving to incorporate new technologies, such as GPUs, in order to
achieve new, higher-performance measures, and we must understand exactly what
each new innovation offers.”
Each of the architectures Bokhari tested fall in the area of
parallel computing, where multiple processors are used to tackle pieces of
complex problems “in parallel.” The subset-sum problem she used for her study
is an algorithm with known solutions that is solvable in a period of time that
is proportional to the number of objects entered, multiplied by the sum of
their sizes. Also, she carefully timed the code runs for solving a comprehensive
range of problem sizes.
Bokhari concluded that the GPU performs well for problems
whose tables fit within the limitations of the device memory. Because GPUs
typically have memory sizes in the range of 10 gigabytes (GB), such
architectures are best for small problems that have table sizes of
approximately thirty billion bits.
She found that the IBM x3755 performed very well on
medium-sized problems that fit within its 64-GB memory, but had poor
scalability as the number of processors increased and was unable to sustain its
performance as the problem size increased. The machine tended to saturate for
problem with table sizes of 300 billion bits.
The Cray XMT showed very good scaling for large problems and
demonstrated sustained performance as the problem size increased, she said.
However, the Cray had poor scaling for small problem sizes, performing best
with table sizes of a trillion bits or more.
“In conclusion, we can state that the NVIDIA GPGPU is best
suited to small problem sizes; the IBM x3755 performs well for medium sizes,
and the Cray XMT is the clear choice for large problems,” Bokhari said. “For
the XMT, we expect to see better performance for large problem sizes, should
memory larger than 1 TB become available.”
