Scalable communication for high-order stencil computations using CUDA-aware MPI

Modern compute nodes in high-performance computing provide a tremendous level of parallelism and processing power. However, as arithmetic performance has been observed to increase at a faster rate relative to memory and network bandwidths, optimizing data movement has become critical for achieving strong scaling in many communication-heavy applications. This performance gap has been further accentuated with the introduction of graphics processing units, which can provide by multiple factors higher throughput in data-parallel tasks than central processing units. In this work, we explore the computational aspects of iterative stencil loops and implement a generic communication scheme using CUDA-aware MPI, which we use to accelerate magnetohydrodynamics simulations based on high-order finite differences and third-order Runge-Kutta integration. We put particular focus on improving intra-node locality of workloads. In comparison to a theoretical performance model, our implementation exhibits strong scaling from one to $64$ devices at $50\%$--$87\%$ efficiency in sixth-order stencil computations when the problem domain consists of $256^3$--$1024^3$ cells.