dc.citation.conferencePlace |
CN |
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dc.citation.conferencePlace |
Vancouver |
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dc.citation.endPage |
813 |
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dc.citation.startPage |
804 |
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dc.citation.title |
IEEE International Parallel and Distributed Processing Symposium |
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dc.contributor.author |
Park, Jinsu |
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dc.contributor.author |
Baek, Woongki |
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dc.date.accessioned |
2023-12-19T15:49:34Z |
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dc.date.available |
2023-12-19T15:49:34Z |
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dc.date.created |
2018-11-21 |
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dc.date.issued |
2018-05-21 |
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dc.description.abstract |
Hardware transactional memory (HTM) is supported by widely-used commodity processors. While the effectiveness of HTM has been evaluated based on small-scale multi-core systems, it still remains unexplored to quantify the performance and energy-efficiency of HTM for scientific workloads on large-scale NUMA systems, which have been increasingly adopted to high-performance computing. To bridge this gap, this work investigates the performance and energy-efficiency impact of HTM on scientific applications on large-scale NUMA systems. We first quantify the performance and energy efficiency of HTM for scientific workloads based on the widely-used CLOMP-TM benchmark. We then discuss a set of generic software optimizations that can be effectively used to improve the performance and energy efficiency of transactional scientific workloads on large-scale NUMA systems. Finally, we present case studies in which we apply a set of the optimizations to representative transactional scientific applications and significantly optimize their performance and energy efficiency on large-scale NUMA systems. |
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dc.identifier.bibliographicCitation |
IEEE International Parallel and Distributed Processing Symposium, pp.804 - 813 |
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dc.identifier.doi |
10.1109/IPDPS.2018.00090 |
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dc.identifier.scopusid |
2-s2.0-85052245544 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/32730 |
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dc.identifier.url |
https://ieeexplore.ieee.org/abstract/document/8425234 |
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dc.language |
영어 |
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dc.publisher |
IEEE |
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dc.title |
Quantifying the Performance and Energy-Efficiency Impact of Hardware Transactional Memory on Scientific Applications on Large-Scale NUMA Systems |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2018-05-21 |
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