Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Power and Energy Profiling of Scientific Applications on Distributed Systems
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
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
Energy Profiling and Analysis of the HPC Challenge Benchmarks
International Journal of High Performance Computing Applications
PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications
IEEE Transactions on Parallel and Distributed Systems
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Recent studies point to power consumption becoming the major design constraint in exascale computing systems. Current scientific benchmarks, such as LINPACK, only evaluate high-performance computing (HPC) systems when running at full throttle, i.e., 100 % workload, resulting in more of a focus on performance than on power and energy consumption. In contrast, efforts like SPECpower evaluate the energy efficiency of a server at varying workloads. This is analogous to evaluating the fuel efficiency of an automobile at varying speeds. However, the applicability of SPECpower to HPC is limited at best.Given the absence of a scientific benchmark to evaluate the energy efficiency of HPC system at different workloads, we propose GBench (short for Green Benchmark), a methodology to evaluate the energy efficiency of supercomputers and enable a more rigorous study of energy efficiency in HPC. We use LINPACK as a case study and demonstrate the efficacy of our methodology by identifying application parameters impacting performance and providing a systematic methodology to vary the workload of LINPACK.