@ingero
An open-source research project focused on kernel-level GPU observability and tracing CPU-GPU interactions using eBPF
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23h ago · 7 min read · TL;DR In a distributed training job, every node can look healthy on its own dashboard while throughput across the job quietly drops. The cause is almost never visible per host, because the signal is r
Join discussion4d ago · 7 min read · 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 collec
Join discussion5d ago · 7 min read · TL;DR After del tensor; torch.cuda.empty_cache(), PyTorch’s caching allocator still holds 53.7 MB that it won’t release. We traced the CUDA Runtime and Driver APIs with eBPF uprobes to see exactly wh
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May 26 · 6 min read · Three eBPF patterns hyperscalers run in production today, mapped to the equivalent patterns on the GPU plane that nobody runs in production yet. TL;DR GitHub recently disclosed using eBPF in productio
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