Name
Abstract | ||
|---|---|---|
Energy efficiency is a crucial factor in developing large super-computers 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-92040-5_3 | HIGH PERFORMANCE COMPUTING, ISC HIGH PERFORMANCE 2018 |
Keywords | Field | DocType |
Bayesian optimization, Energy efficiency, Automatic parameter tuning | Minimal knowledge,Ranking,Efficient energy use,Computer science,Bayesian optimization,Graphics processing unit,Computer engineering,Benchmarking,Distributed computing | Conference |
Volume | ISSN | Citations |
10876 | 0302-9743 | 2 |
PageRank | References | Authors |
0.39 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takashi Miyazaki | 1 | 12 | 0.88 |
| Issei Sato | 2 | 331 | 41.59 |
| Nobuyuki Shimizu | 3 | 37 | 7.76 |