Improving energy efficiency of HPC applications using unbalanced GPU power capping

Energy efficiency represents a significant challenge in the domain of High-Performance Computing (HPC). One potential key parameter to improve energy efficiency is the use of power capping, a technique for controlling the power limits of a device, such as a CPU or GPU.In this paper, we propose to examine the impact of GPU power capping in the context of HPC applications using heterogeneous computing systems. To this end, we first conduct an extensive study of the impact of GPU power capping on a compute intensive kernel, namely matrix multiplication kernel (GEMM), on different Nvidia GPU architectures. Interestingly, such compute-intensive kernels are up to 30 % more energy efficient when the GPU is set to 55-70 % of its Thermal Design Power (TDP). Using the best power capping configuration provided by this study, we investigate how setting different power caps for GPU devices of a heterogeneous computing node can improve the energy efficiency of the running application. We consider dense linear algebra task-based operations, namely matrix multiplication and Cholesky factorization.We show how the underlying runtime system scheduler can then automatically adapt its decisions to take advantage of the heterogeneous performance capability of each GPU. The results show that for a given platform equipped with four GPU devices, applying a power cap on all GPUs improves the energy efficiency for matrix multiplication up to 24.3 % (resp. 33.78 %) for double (resp. single) precision.