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Syncopate: Efficient Multi-GPU AI Kernels via Automatic C...
[Submitted on 28 Jan 2026 (v1), last revised 1 Jul 2026 (this ve · 2026-01-28 · via cs.DC updates on arXiv.org

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Abstract:Communication has become a first-order bottleneck in large-scale GPU workloads, and existing distributed compilers address it mainly by overlapping whole compute and communication kernels at the stream level. This coarse granularity incurs extra kernel launches, forces device-wide synchronizations at kernel boundaries, and leaves substantial slack when the slowest tile or kernel stretches the communication tail. We present Syncopate, a compiler and runtime that enables automatic fine-grained overlap inside a single fused kernel. Syncopate introduces a communication chunk abstraction that decouples communication granularity from kernel structure and backend mechanisms, allowing chunk-level plans to be ported from existing distributed compilers, written directly by users, or instantiated from reusable templates. Given a local Triton kernel and a chunk schedule, Syncopate performs transformations to align computation with chunk availability. Implemented as a source-to-source compiler on Triton, Syncopate delivers an average end-to-end speedup of 1.3$\times$ and up to 4.7$\times$ on multi-GPU workloads. Our code is open-sourced at this https URL.

Submission history

From: Xinwei Qiang [view email]
[v1] Wed, 28 Jan 2026 13:29:51 UTC (429 KB)
[v2] Fri, 27 Mar 2026 08:04:43 UTC (429 KB)
[v3] Fri, 3 Apr 2026 01:00:32 UTC (428 KB)
[v4] Wed, 1 Jul 2026 05:30:30 UTC (1,235 KB)