The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Adagio: making DVS practical for complex HPC applications
Abstract
Power and energy are first-order design constraints in high performance computing. Current research using dynamic voltage scaling (DVS) relies on trading increased execution time for energy savings, which is unacceptable for most high performance computing applications. We present Adagio, a novel runtime system that makes DVS practical for complex, real-world scientific applications by incurring only negligible delay while achieving significant energy savings. Adagio improves and extends previous state-of-the-art algorithms by combining the lessons learned from static energy-reducing CPU scheduling with a novel runtime mechanism for slack prediction. We present results using Adagio for two real-world programs, UMT2K and ParaDiS, along with the NAS Parallel Benchmark suite. While requiring no modification to the application source code, Adagio provides total system energy savings of 8% and 20% for UMT2K and ParaDiS, respectively, with less than 1% increase in execution time.
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Cites
Voltage scheduling problem for dynamically variable voltage processors
Ishihara, T., & Yasuura, H. (1998) · Proceedings of the 1998 international symposium on Low power electronics and design - ISLPED '98 · doi:10.1145/280756.280894 · Cited by 803
A Power-Aware Run-Time System for High-Performance Computing
Hsu, C.-H., & Feng, W.-C. (2005) · ACM/IEEE SC 2005 Conference (SC'05) · doi:10.1109/sc.2005.3 · Cited by 402
Task scheduling and voltage selection for energy minimization
Zhang, Y., Hu, X. S., & Chen, D. Z. (2002) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/513918.513966 · Cited by 379
Zhu, D. K., Melhem, R., & Childers, B. R. (2003) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2003.1214320 · Cited by 361
Thermal Performance Challenges from Silicon to Systems
Viswanath, R., Wakharkar, V., Watwe, A., Lebonheur, V., Manufacturing Group, & Intel Corp (2000) · Cited by 317
Ge, R., Feng, X., & Cameron, K. W. (2005) · ACM/IEEE SC 2005 Conference (SC'05) · doi:10.1109/sc.2005.57 · Cited by 315
CPU MISER: A Performance-Directed, Run-Time System for Power-Aware Clusters
Ge, R., Feng, X., Feng, W.-C., & Cameron, K. W. (2007) · 2007 International Conference on Parallel Processing (ICPP 2007) · doi:10.1109/icpp.2007.29 · Cited by 238
Lim, M. Y., Freeh, V. W., & Lowenthal, D. K. (2006) · Proceedings of the 2006 ACM/IEEE conference on Supercomputing - SC '06 · doi:10.1145/1188455.1188567 · Cited by 162
Self-citationExploring the Energy-Time Tradeoff in MPI Programs on a Power-Scalable Cluster
Freeh, V. W., Pan, F., Kappiah, N., Lowenthal, D. K., & Springer, R. (2005) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2005.214 · Cited by 122
Self-citationMinimizing execution time in MPI programs on an energy-constrained, power-scalable cluster
Springer, R., Lowenthal, D. K., Rountree, B., & Freeh, V. W. (2006) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/1122971.1123006 · Cited by 93
Self-citation
Cited by
Workload-Aware Runtime Energy Management for HPC Systems
Reddy Basireddy, K., Wachter, E. W., Al-Hashimi, B. M., & Merrett, G. (2018) · 2018 International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcs.2018.00057 · Cited by 13
Metadata
- Authors
- Rountree, B., Lownenthal, D. K., de Supinski, B. R., Schulz, M., Freeh, V. W., & Bletsch, T.
- Venue
- Proceedings of the 23rd international conference on Supercomputing
- Publisher
- ACM
- Published
- 2009-06-08
- Pages
- 460-469
- OpenAlex
- W1973923004
- Cited by
- 290
- References
- 12
- FWCI
- 9.66
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Rountree, B., Lownenthal, D. K., de Supinski, B. R., Schulz, M., Freeh, V. W., & Bletsch, T. (2009). Adagio: making DVS practical for complex HPC applications. Proceedings of the 23rd international conference on Supercomputing. https://doi.org/10.1145/1542275.1542340
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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