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- W2887397191 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." @default.
- W2887397191 created "2018-08-22" @default.
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- W2887397191 date "2018-05-01" @default.
- W2887397191 modified "2023-10-16" @default.
- W2887397191 title "Quantifying the Performance and Energy-Efficiency Impact of Hardware Transactional Memory on Scientific Applications on Large-Scale NUMA Systems" @default.
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- W2887397191 doi "https://doi.org/10.1109/ipdps.2018.00090" @default.
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