Split Wisely: When Work Partitioning is Energy-Optimal on Heterogeneous Hardware
| Autor: | Andrew Haigh, Anish Varghese, Luke Angove, Gaurav Mitra, Alistair P. Rendell |
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| Rok vydání: | 2016 |
| Předmět: |
020203 distributed computing
Xeon business.industry Computer science 020209 energy 02 engineering and technology Energy consumption Supercomputer Partition (database) Matrix multiplication Embedded system 0202 electrical engineering electronic engineering information engineering System on a chip business Computer hardware Efficient energy use |
| Zdroj: | HPCC/SmartCity/DSS |
| DOI: | 10.1109/hpcc-smartcity-dss.2016.0113 |
| Popis: | Heterogeneous System-on-Chip (SoC) processors are increasingly gaining traction in the High Performance Computing (HPC) community as alternate building blocks for future exascale systems. Key issues relating to their promise of energy efficiency include i) absolute performance, ii) finding an energy-optimal balance in the use of different on-chip devices and iii) understanding the performance-energy trade-offs while using different on-chip devices. In this paper we explore these issues through an energy usage model designed to predict the existence of an energy-optimal work partition between different processing elements on heterogeneous systems for any application. We validate our model by measuring performance and energy consumption of matrix multiplication on the NVIDIA Tegra K1 and X1 systems. An environment for monitoring and responding to energy usage is also outlined and used to perform high resolution measurements. Comparisons are drawn with conventional HPC systems housing Intel Xeon CPUs alongside NVIDIA GPUs. |
| Databáze: | OpenAIRE |
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