TY - GEN
T1 - Hybrid MPI/OpenMP power-aware computing
AU - Lit, Dong
AU - De Supinski, Bronis R.
AU - Schulz, Martin
AU - Cameron, Kirk
AU - Nikolopoulos, Dimitrios S.
PY - 2010
Y1 - 2010
N2 - Power-aware execution of parallel programs is now a primary concern in large-scale HPC environments. Prior research in this area has explored models and algorithms based on dynamic voltage and frequency scaling (DVFS) and dynamic concurrency throttling (DCT) to achieve power-aware execution of programs written in a single programming model, typically MPI or OpenMP. However, hybrid programming models combining MPI and OpenMP are growing in popularity as emerging large-scale systems have many nodes with several processors per node and multiple cores per processor. In this paper we present and evaluate solutions for power-efficient execution of programs written in this hybrid model targeting large-scale distributed systems with multicore nodes. We use a new power-aware performance prediction model of hybrid MPI/OpenMP applications to derive a novel algorithm for power-efficient execution of realistic applications from the ASC Sequoia and NPB MZ benchmarks. Our new algorithm yields substantial energy savings (4.18% on average and up to 13.8%) with either negligible performance loss or performance gain (up to 7.2%).
AB - Power-aware execution of parallel programs is now a primary concern in large-scale HPC environments. Prior research in this area has explored models and algorithms based on dynamic voltage and frequency scaling (DVFS) and dynamic concurrency throttling (DCT) to achieve power-aware execution of programs written in a single programming model, typically MPI or OpenMP. However, hybrid programming models combining MPI and OpenMP are growing in popularity as emerging large-scale systems have many nodes with several processors per node and multiple cores per processor. In this paper we present and evaluate solutions for power-efficient execution of programs written in this hybrid model targeting large-scale distributed systems with multicore nodes. We use a new power-aware performance prediction model of hybrid MPI/OpenMP applications to derive a novel algorithm for power-efficient execution of realistic applications from the ASC Sequoia and NPB MZ benchmarks. Our new algorithm yields substantial energy savings (4.18% on average and up to 13.8%) with either negligible performance loss or performance gain (up to 7.2%).
KW - MPI
KW - OpenMP
KW - Performance modeling
KW - Power-aware high-performance computing
UR - http://www.scopus.com/inward/record.url?scp=77953990600&partnerID=8YFLogxK
U2 - 10.1109/IPDPS.2010.5470463
DO - 10.1109/IPDPS.2010.5470463
M3 - Conference contribution
AN - SCOPUS:77953990600
SN - 9781424464432
T3 - Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010
BT - Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010
T2 - 24th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2010
Y2 - 19 April 2010 through 23 April 2010
ER -