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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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/ingero/from-kernel-scheduler-to-python-source-line-tracing-a-gpu-stall-end-to-end-3f7f |
| Publication time | Fri, 29 May 2026 13:10:00 +0000 |
| Retrieval time | 2026-05-29T13:20:00.413Z |
| Last seen | 2026-05-29T13:20:00.413Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | bLs2fG_O5wxK |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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