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LEO: Tracing GPU Stall Root Causes via Cross-Vendor Backw...
[Submitted on 21 Apr 2026 (v1), last revised 15 Jul 2026 (this v · 2026-04-22 · via cs.DC updates on arXiv.org

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Abstract:More than half of the Top 500 supercomputers employ GPUs as accelerators. On GPU-accelerated platforms, developers face a key diagnostic gap: profilers show source lines where stalls occur, but not why they occur. Furthermore, the same kernel may have different stalls and underlying causes on different GPUs. This paper presents LEO, a root-cause analyzer for NVIDIA, AMD, and Intel GPUs that performs backward slicing from stalled instructions, considering dependencies arising from registers as well as vendor-specific synchronization mechanisms. LEO attributes GPU stalls to source instructions with the goal of explaining root causes of these inefficiencies. Across 21 workloads on three GPU platforms, LEO-guided optimizations deliver geometric-mean speedups of 1.73$\times$--1.82$\times$. Our case studies show that (1) the same kernel may require different optimizations for different GPU architectures, and (2) LEO's structured diagnostics improve code optimization with large language models relative to code-only and raw-stall-count baselines.

Submission history

From: Yuning Xia [view email]
[v1] Tue, 21 Apr 2026 22:23:55 UTC (184 KB)
[v2] Wed, 15 Jul 2026 22:02:45 UTC (184 KB)