[2606.05951] Demystifying NVSHMEM: A System-Level Analysis on Symmetric Memory and Device-Initiated Operations in GPU Communication

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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2606.05951 (cs)

[Submitted on 4 Jun 2026]

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

Authors:Yijun Ma, Siyuan Shen, Tiancheng Chen, Akhil Langer, Jiri Kraus, Benjamin Glick, Craig Belusar, Jeff Hammond, Torsten Hoefler<br>View a PDF of the paper titled Demystifying NVSHMEM: A System-Level Analysis on Symmetric Memory and Device-Initiated Operations in GPU Communication, by Yijun Ma and 8 other authors

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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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Distributed, Parallel, and Cluster Computing (cs.DC)

ACM classes:<br>C.2

Cite as:<br>arXiv:2606.05951 [cs.DC]

(or<br>arXiv:2606.05951v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2606.05951

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Siyuan Shen [view email]<br>[v1]<br>Thu, 4 Jun 2026 09:50:16 UTC (617 KB)

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