Aiming to Optimize HPC Resource Utilization based on Jobs' Energy Consumption Data
- 1. Centre Informatique National de l'Enseignement Supérieur (CINES), France
- 2. Big Data & Security Bull ATOS, France
Description
Optimization of energy consumption and HPC resource utilization are high-priority issues for any supercomputing centre . Accelerators will drive the floating-point performances of exascale supercomputers; however preparing the scientific communities for these ever-changing architectures is a daunting task. Thus, the work of HPC support teams is crucial in achieving an overall optimized resource utilization. We highlight a methodology to monitor and optimize resource utilization at a national supercomputing centre, CINES [8] . We used a tool to collect the total energy consumed (kWh) by the simulation jobs on supercomputer ‘Occigen’ for several months. Based on benchmark runs and our data, we prepared a classification criterion to distinguish jobs based on their normalized energy consumption; and classified them from low to high-energy consumption classes. Our data analysis shows interesting trends that help us identify suboptimal jobs, along with other insights, to improve resource utilization as a final goal on CINES’s supercomputers
Files
WP315_energy_data_analysis_of_HPC_jobs_CLEAN.pdf
Files
(1.1 MB)
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