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arXiv · 2606.05951

Demystifying NVSHMEM: A System-Level Analysis on Symmetric Memory and Device-Initiated Operations in GPU Communication

Yijun Ma·Siyuan Shen·Tiancheng Chen·Akhil Langer·Jiri Kraus·Benjamin Glick·Craig Belusar·Jeff Hammond·Torsten Hoefler
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Abstract

NVSHMEM is NVIDIA's OpenSHMEM-based PGAS communication library for GPU clusters, enabling GPU-initiated, one-sided communication through symmetric memory. Despite its growing adoption, a system-level understanding of its design and behavior remains scattered across documentation, source code, and application experience. This paper presents a concise study of NVSHMEM's programming model, implementation, and performance characteristics, focusing on symmetric memory, one-sided operations, and device-side collectives. We also examine DeepEP as a case study of NVSHMEM in performance-critical sparse deep learning workloads. Our analysis shows that NVSHMEM pioneered a device-side symmetric-memory programming model that enables fine-grained GPU-driven communication and is important for approaching the hardware performance limit. Overall, this work defines NVSHMEM's role as a systems building block, highlights its design tradeoffs, and identifies opportunities for improving GPU communication runtimes.

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Yijun Ma, Siyuan Shen, Tiancheng Chen, Akhil Langer, Jiri Kraus, Benjamin Glick, Craig Belusar, Jeff Hammond, Torsten Hoefler. 2026-06-04. Demystifying NVSHMEM: A System-Level Analysis on Symmetric Memory and Device-Initiated Operations in GPU Communication. https://arxiv.org/abs/2606.05951

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