Predictive performance and scalability modeling of a large-scale application
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Modeling Communication Overhead: MPI and MPL Performance on the IBM SP2
IEEE Parallel & Distributed Technology: Systems & Technology
Towards Efficient Supercomputing: A Quest for the Right Metric
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 11 - Volume 12
Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Accurate and efficient regression modeling for microarchitectural performance and power prediction
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
Regression Modeling Strategies
Regression Modeling Strategies
Methods of inference and learning for performance modeling of parallel applications
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
An improved model for predicting HPL performance
GPC'07 Proceedings of the 2nd international conference on Advances in grid and pervasive computing
An approach to performance prediction for parallel applications
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
A survey on techniques for improving the energy efficiency of large-scale distributed systems
ACM Computing Surveys (CSUR)
| Hi-index | 0.00 |
High-performance computing (HPC) has become an indispensable resource in science and engineering, and it has oftentimes been referred to as the "thirdpillar" of science, along with theory and experimentation. Performance tuning is a key aspect in utilizing HPC resources to the fullest extent. However, recent exascale studies suggest that power and energy consumption will be a major impediment to HPC in this coming decade. Therefore, performance tuning should evolve and take energy consumption into account. Unfortunately, the increase in system complexity and the number of tunable parameters in applications makes the performance tuning of an application cumbersome. To address these issues, we propose energy-efficient tuning via statistical regression techniques. Such techniques can be used to model the power and performance of a scientific application, and then the application parameters can be tuned to achieve the best energy efficiency possible, based on metrics such as the performance-to-power ratio. In this paper, we utilize multi-variable regression to model the power and performance of the high-performance LINPACK (HPL) benchmark. We then tune the HPL parameters for energy efficiency and compare them to the energy efficiency achieved at maximum possible performance(Rmax ). Our results show that statistical regression modeling can be used for predicting the HPL configuration for achieving the maximum energy efficiency with very high accuracy.