~/talk

Auto-instrumentation for GPU performance using eBPF

Talkat DevConf.CZ 2025 · 2025-06-12..06-14· [machine-learning, observability]
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Prototype uses eBPF probes on CUDA libcalls to capture kernel launches, host↔device memory copies and allocations at the CPU side, then exposes those events as Prometheus-exported kernel launch and memcpy metrics for Grafana. Implementation runs with minimal CPU overhead, is framework-agnostic for CUDA, open-source on GitHub, and shows per-container/kernel cardinality and memory-copy patterns. Limitations: no GPU-internal stats or per-kernel execution time and stack unwinding is partial when frame pointers are optimized away.

AI-written summary · about

  1. 0:00GPU monitoring with eBPF
  2. 1:25Startup GPU struggles
  3. 3:59GPU failure risks
  4. 6:06Current tool limitations
  5. 9:58Grafana Beyla intro
  6. 10:41Zero-instrumentation tracking
  7. 13:39CUDA kernel costs
  8. 16:14GPU metrics in Prometheus
  9. 17:42GPU memory patterns
  10. 20:15GPU stack traces
  11. 21:38eBPF GPU limits
  12. 22:42GPU vs modern monitoring
  13. 24:02Q&A
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