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arXiv · 2410.00801

Understanding Data Movement in AMD Multi-GPU Systems with Infinity Fabric

Gabin Schieffer·Ruimin Shi·Stefano Markidis·Andreas Herten·Jennifer Faj·Ivy Peng
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Abstract

Modern GPU systems are constantly evolving to meet the needs of computing-intensive applications in scientific and machine learning domains. However, there is typically a gap between the hardware capacity and the achievable application performance. This work aims to provide a better understanding of the Infinity Fabric interconnects on AMD GPUs and CPUs. We propose a test and evaluation methodology for characterizing the performance of data movements on multi-GPU systems, stressing different communication options on AMD MI250X GPUs, including point-to-point and collective communication, and memory allocation strategies between GPUs, as well as the host CPU. In a single-node setup with four GPUs, we show that direct peer-to-peer memory accesses between GPUs and utilization of the RCCL library outperform MPI-based solutions in terms of memory/communication latency and bandwidth. Our test and evaluation method serves as a base for validating memory and communication strategies on a system and improving applications on AMD multi-GPU computing systems.

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Gabin Schieffer, Ruimin Shi, Stefano Markidis, Andreas Herten, Jennifer Faj, Ivy Peng. 2024-10-01. Understanding Data Movement in AMD Multi-GPU Systems with Infinity Fabric. https://arxiv.org/abs/2410.00801

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