Understanding Data Movement in AMD Multi-GPU Systems with Infinity Fabric
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910627561209856 |
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| author | Schieffer, Gabin Shi, Ruimin Markidis, Stefano Herten, Andreas Faj, Jennifer Peng, Ivy |
| author_facet | Schieffer, Gabin Shi, Ruimin Markidis, Stefano Herten, Andreas Faj, Jennifer Peng, Ivy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00801 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Understanding Data Movement in AMD Multi-GPU Systems with Infinity Fabric Schieffer, Gabin Shi, Ruimin Markidis, Stefano Herten, Andreas Faj, Jennifer Peng, Ivy Distributed, Parallel, and Cluster Computing 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. |
| title | Understanding Data Movement in AMD Multi-GPU Systems with Infinity Fabric |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2410.00801 |