From Kernel Scheduler to Python Source Line: Tracing a GPU Stall End to End
The article discusses the challenges of diagnosing GPU stalls during training steps in machine learning. It highlights how traditional tools often fail to provide actionable insights due to a lack of correlated data across different layers. The introduction of an eBPF agent allows for better tracing and understanding of the root causes of these stalls by correlating events across the GPU, CUDA driver, and Python source code.
- ▪A GPU can report high utilization while still being the bottleneck in a training step.
- ▪Traditional debugging methods often involve adding timing prints, which can be inefficient and uninformative.
- ▪An eBPF agent can correlate data across the Linux kernel, CUDA driver, and Python interpreter to identify the exact cause of stalls.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3853036) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ingero Team Posted on May 29 • Originally published at ingero.io From Kernel Scheduler to Python Source Line: Tracing a GPU Stall End to End #ebpf #gpu #python #observability TL;DR A GPU that reports 97% utilization can still be the slowest part of a training step, and the reason usually lives outside the GPU: a CPU scheduler preemption, a driver-level allocation, a collective waiting on a straggler rank.
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