Paper Info
Title
Quantifying the Performance and Energy-Efficiency Impact of Hardware Transactional Memory on Scientific Applications on Large-Scale NUMA Systems
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.
Year
DOI
Venue
2018
10.1109/IPDPS.2018.00090
2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Keywords
Field
DocType
Hardware transactional memory,non uniform memory access,scientific applications,high performance,energy efficiency
Synchronization,Efficient energy use,Computer science,Transactional memory,Software,Transactional leadership,Benchmark (computing),Distributed computing
Conference
ISSN
ISBN
Citations 
1530-2075
978-1-5386-4369-3
2
PageRank 
References 
Authors
0.37
17
2
Name
Order
Citations
PageRank
Jinsu Park1367.43
Woongki Baek240225.85