dc.citation.conferencePlace |
SP |
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dc.citation.conferencePlace |
Spain |
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dc.citation.endPage |
104 |
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dc.citation.startPage |
94 |
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dc.citation.title |
International Symposium on Code Generation and Optimization |
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dc.contributor.author |
Nguyen, Dong |
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dc.contributor.author |
Lee, Jongeun |
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dc.date.accessioned |
2023-12-19T21:08:08Z |
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dc.date.available |
2023-12-19T21:08:08Z |
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dc.date.created |
2016-06-09 |
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dc.date.issued |
2016-03-12 |
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dc.description.abstract |
Stream graphs can provide a natural way to represent many applications in multimedia and DSP domains. Though the exposed parallelism of stream graphs makes it relatively easy to map them to GP (General Purpose)-GPUs, very large stream graphs as well as how to best exploit multi-GPU platforms to achieve scalable performance poses great challenges for stream graph mapping. Previous work considers either a single GPU only or is based on a crude heuristic that achieves a very low degree of workload balancing, and thus shows only limited scalability. In this paper we present a highly scalable GP-GPU mapping technique for large stream graphs with the following highlights: (1) an accurate GPU performance estimation model for subsets of stream graphs, (2) a novel partitioning heuristic exploiting stream graph's structural properties, and (3) ILP (Integer Linear Programming) formulation of the mapping problem. Our experimental results on a real GPU platform demonstrate that our technique can generate scalable performance for up to 4 GPUs with large stream graphs, and can generate highly optimized multi-GPU code especially for compute-bound ones. |
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dc.identifier.bibliographicCitation |
International Symposium on Code Generation and Optimization, pp.94 - 104 |
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dc.identifier.doi |
10.1145/2854038.2854055 |
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dc.identifier.scopusid |
2-s2.0-84968779599 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/32808 |
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dc.identifier.url |
http://dl.acm.org/citation.cfm?doid=2854038.2854055 |
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dc.language |
영어 |
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dc.publisher |
Association for Computing Machinery, Inc |
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dc.title |
Communication-aware mapping of stream graphs for multi-GPU platforms |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2016-03-12 |
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