Abstract
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. Our GPU implementation scales strongly from one to 64 devices at 50%–87% of the expected efficiency based on a theoretical performance model. Compared with a multi-core CPU solver, our implementation exhibits 20–60× speedup and 9–12× improved energy efficiency in compute-bound benchmarks on 16 nodes.
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