A CUDA implementation of the high performance conjugate gradient benchmark

Abstract

The High Performance Conjugate Gradient (HPCG) benchmark has been recently proposed as a complement to the High Performance Linpack (HPL) benchmark currently used to rank supercomputers in the Top500 list. This new benchmark solves a large sparse linear system using a multigrid preconditioned conjugate gradient (PCG) algorithm. The PCG algorithm contains the computational and communication patterns prevalent in the numerical solution of partial differential equations and is designed to better represent modern application workloads which rely more heavily on memory system and network performance than HPL. GPU accelerated supercomputers have proved to be very effective, especially with regard to power efficiency, for accelerating compute intensive applications like HPL. This paper will present the details of a CUDA implementation of HPCG, and the results obtained at full scale on the largest GPU supercomputers available: the Cray XK7 at ORNL and the Cray XC30 at CSCS. The results indicate that GPU accelerated supercomputers are also very effective for this type of workload.