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

TL;DR

This paper presents a CUDA-aware MPI communication scheme for high-order stencil computations, significantly improving scalability and efficiency in GPU-based magnetohydrodynamics simulations.

Contribution

It introduces a generic GPU communication scheme using CUDA-aware MPI that enhances intra-node locality and scales efficiently across multiple GPUs for high-order stencil computations.

Findings

Strong scaling from 1 to 64 GPUs at 50-87% efficiency

20-60x speedup over CPU solvers

9-12x energy efficiency improvement on 16 nodes

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 $20$--$60\times$ speedup and $9$--$12\times$ improved energy efficiency in compute-bound benchmarks on $16$ nodes.