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arXiv · 2004.05371

A Study of Single and Multi-device Synchronization Methods in Nvidia GPUs

Lingqi Zhang·Mohamed Wahib·Haoyu Zhang·Satoshi Matsuoka
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Abstract

GPUs are playing an increasingly important role in general-purpose computing. Many algorithms require synchronizations at different levels of granularity in a single GPU. Additionally, the emergence of dense GPU nodes also calls for multi-GPU synchronization. Nvidia's latest CUDA provides a variety of synchronization methods. Until now, there is no full understanding of the characteristics of those synchronization methods. This work explores important undocumented features and provides an in-depth analysis of the performance considerations and pitfalls of the state-of-art synchronization methods for Nvidia GPUs. The provided analysis would be useful when making design choices for applications, libraries, and frameworks running on single and/or multi-GPU environments. We provide a case study of the commonly used reduction operator to illustrate how the knowledge gained in our analysis can be useful. We also describe our micro-benchmarks and measurement methods.

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Lingqi Zhang, Mohamed Wahib, Haoyu Zhang, Satoshi Matsuoka. 2020-04-11. A Study of Single and Multi-device Synchronization Methods in Nvidia GPUs. https://arxiv.org/abs/2004.05371

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