Ohio State University Researcher Compares Parallel Systems
From the post:
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 former 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.
What I found fascinating about this approach was the comparison of:
the strengths and weaknesses of these architectures in the context of a well-defined combinatorial problem.
True enough, there is a place for general methods and solutions, but one pays the price for using general methods and solutions.
Thinking that for subject identity and “merging” in a “big data” context, that we will need a deeper understanding of specific identity and merging requirements. So that the result of that study is one or more well-defined combinatorial problems.
That is to say that understanding one or more combinatorial problems precedes proposing a solution.
You can view/download the thesis by Saniyah Bokhari, Parallel Solution of the Subset-sum Problem: An Empirical Study
Or view the article (assuming you have access):
Parallel solution of the subset-sum problem: an empirical study
Abstract (of the article):
The subset-sum problem is a well-known NP-complete combinatorial problem that is solvable in pseudo-polynomial time, that is, time proportional to the number of input objects multiplied by the sum of their sizes. This product defines the size of the dynamic programming table used to solve the problem. We show how this problem can be parallelized on three contemporary architectures, that is, a 128-processor Cray Extreme Multithreading (XMT) massively multithreaded machine, a 16-processor IBM x3755 shared memory machine, and a 240-core NVIDIA FX 5800 graphics processing unit (GPU). We show that it is straightforward to parallelize this algorithm on the Cray XMT primarily because of the word-level locking that is available on this architecture. For the other two machines, we present an alternating word algorithm that can implement an efficient solution. Our results show that the GPU performs well for problems whose tables fit within the device memory. Because GPUs typically have memories in the order of 10GB, such architectures are best for small problem sizes that have tables of size approximately 1010. The IBM x3755 performs very well on medium-sized problems that fit within its 64-GB memory but has poor scalability as the number of processors increases and is unable to sustain performance as the problem size increases. This machine tends to saturate for problem sizes of 1011 bits. The Cray XMT shows very good scaling for large problems and demonstrates sustained performance as the problem size increases. However, this machine has poor scaling for small problem sizes; it performs best for problem sizes of 1012 bits or more. The results in this paper illustrate that the subset-sum problem can be parallelized well on all three architectures, albeit for different ranges of problem sizes. The performance of these three machines under varying problem sizes show the strengths and weaknesses of the three architectures. Copyright © 2012 John Wiley & Sons, Ltd.
