Accelerating Large-Scale Interconnection Network Simulation by Cellular Automata Concept
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- YOKOTA Takashi
- Department of Information Systems Science, Graduate School of Engineering, Utsunomiya University
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- OOTSU Kanemitsu
- Department of Information Systems Science, Graduate School of Engineering, Utsunomiya University
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- OHKAWA Takeshi
- Department of Information Systems Science, Graduate School of Engineering, Utsunomiya University
Abstract
<p>State-of-the-art parallel systems employ a huge number of computing nodes that are connected by an interconnection network. An interconnection network (ICN) plays an important role in a parallel system, since it is responsible to communication capability. In general, an ICN shows non-linear phenomena in its communication performance, most of them are caused by congestion. Thus, designing a large-scale parallel system requires sufficient discussions through repetitive simulation runs. This causes another problem in simulating large-scale systems within a reasonable cost. This paper shows a promising solution by introducing the cellular automata concept, which is originated in our prior work. Assuming 2D-torus topologies for simplification of discussion, this paper discusses fundamental design of router functions in terms of cellular automata, data structure of packets, alternative modeling of a router function, and miscellaneous optimization. The proposed models have a good affinity to GPGPU technology and, as representative speed-up results, the GPU-based simulator accelerates simulation upto about 1264 times from sequential execution on a single CPU. Furthermore, since the proposed models are applicable in the shared memory model, multithread implementation of the proposed methods achieve about 162 times speed-ups at the maximum.</p>
Journal
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E102.D (1), 52-74, 2019-01-01
The Institute of Electronics, Information and Communication Engineers
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Details 詳細情報について
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- CRID
- 1390001288106907776
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- NII Article ID
- 130007542036
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- ISSN
- 17451361
- 09168532
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed