Optimizing communication and cooling costs in HPC data centers via intelligent job allocation

Abstract

Nearly half of the energy in the computing clusters today is consumed by the cooling infrastructure. It is possible to reduce the cooling cost by allowing the data center temperatures to rise; however, component reliability constraints impose thermal thresholds as failure rates are exponentially dependent on the processor temperatures. Existing thermally-aware job allocation policies optimize the cooling costs by minimizing the peak inlet temperatures of the server nodes. An important constraint in high performance computing (HPC) data centers, however, is performance. Specifically, HPC data centers run multi-threaded applications with significant communication among the threads. Thus, performance of such applications is strongly affected by the job allocation decisions. This paper proposes a novel job allocation methodology to jointly minimize communication cost of an HPC application while also reducing the cooling energy. The proposed method also considers temperature-dependent hardware reliability as part of the optimization.