Improving the performance of physics applications in atom-based clusters with rCUDA

Abstract

Traditionally, High-Performance Computing (HPC) has been associated with large power requirements. The reason was that chip makers of the processors typically employed in HPC deployments have always focused on getting the highest performance from their designs, regardless of the energy their processors may consume. Actually, for many years only heat dissipation was the real barrier for achieving higher performance, at the cost of higher energy consumption. However, a new trend has recently appeared consisting on the use of low-power processors for HPC purposes. The MontBlanc and Isambard projects are good examples of this trend. These proposals, however, do not consider the use of GPUs.In this paper we propose to use GPUs in this kind of low-power processor based HPC deployments by making use of the remote GPU virtualization mechanism. To that end, we leverage the rCUDA middleware in a hybrid cluster composed of low-power Atom-based nodes and regular Xeon-based nodes equipped with GPUs. Our experiments show that, by making use of rCUDA, the execution time of applications belonging to the physics domain is noticeably reduced, achieving a speed up of up to 140x with just one remote NVIDIA V100 GPU with respect to the execution of the same applications using 8 Atom-based nodes. Additionally, a rough energy consumption estimation reports improvements in energy demands of up to 37x.