Bayesian optimization of hpc systems for energy efficiency

Abstract

Energy efficiency is a crucial factor in developing large supercomputers and cost-effective datacenters. However, tuning a system for energy efficiency is difficult because the power and performance are conflicting demands. We applied Bayesian optimization (BO) to tune a graphics processing unit (GPU) cluster system for the benchmark used in the Green500 list, a popular energy-efficiency ranking of supercomputers. The resulting benchmark score enabled our system, named “kukai”, to earn second place in the Green500 list in June 2017, showing that BO is a useful tool. By determining the search space with minimal knowledge and preliminary experiments beforehand, BO could automatically find a sufficiently good configuration. Thus, BO could eliminate laborious manual tuning work and reduce the occupancy time of the system for benchmarking. Because BO is a general-purpose method, it may also be useful for tuning any practical applications in addition to Green500 benchmarks.