Weaver: Diagnosing Extra Kernel and Synchronization Interference in GPU Workloads

Abstract

Modern GPU workloads use batching, asynchronous execution, kernel overlap, and GPU sharing to improve utilization, but these optimizations make kernel-level slowdown hard to diagnose. A target kernel may be blocked by extra synchronization or helper kernels, or slowed by concurrent kernels after it starts. Existing tools provide GPU visibility, but they either remain too heavy for continuous online use or focus on coarse-grained symptoms, leaving fine-grained kernel-level root causes to manual analysis. This poster presents Weaver, a low-overhead cross-layer diagnosis framework for GPU kernels. Weaver builds a semantic execution graph from operator, kernel timeline, and warp/block evidence, distinguishes blocked and slowed kernels, and reports an interpretable root-cause chain. Our prototype evaluation shows that Weaver continuously collects runtime evidence with low overhead and accurately localizes anomalies.