Life & fun · December 16, 2025
Deterministic PCIe Diagnostics for GPUs on Linux
I built a small Linux tool to deterministically verify GPU PCIe link health and bandwidth. It reports: - Negotiated PCIe generation and width - Peak Host→Device and Device→Host memcpy bandwidth - Sustained PCIe TX/RX utilization via NVML - A rule-based verdict derived from observable hardware data only This exists because PCIe issues (Gen downgrades, reduced lane width, risers, bifurcation) are often invisible at the application layer and can’t be fixed by kernel tuning or async overlap. Linux-only: it relies on sysfs and PCIe AER exposure that Windows does not provide.
In plain words
Deterministic PCIe Diagnostics for GPUs on Linux is a command-line tool that verifies GPU PCIe link health and bandwidth on Linux systems. It measures negotiated PCIe generation and width, peak memory copy bandwidth in both directions, and sustained TX/RX utilization, then produces a verdict based on observable hardware data. The tool targets developers and system administrators debugging GPU connectivity issues like lane width reduction or generation downgrade that remain invisible to applications. It works only on Linux due to reliance on sysfs and PCIe AER exposure.
written from the facts on this page · September 2026
Does the same job
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The standard GPU utilization metric reported by nvidia-smi, nvtop, Weights & Biases, Amazon CloudWatch, Google Cloud Monitoring, and Azure Monitor is highly misleading. It reports the fraction of time that any kernel is running on the GPU, which means a GPU can report 100% utilization even if only a small portion of its compute capacity is actually being used. In practice, we've seen workloads with ~1–10% real compute throughput while dashboards show 100%. This becomes a problem when teams rely on that metric for capacity planning or optimization decisions, it can make underutilized systems…
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PantheonGPU – GPU health testing and AI workload benchmarking19d ago · pantheongpu.com · ▲13Hi HN, I built PantheonGPU because I wanted a better way to answer a simple question: is this GPU actually healthy and performing the way it should? A GPU can show normal temperatures and utilization and still be underperforming, unstable under certain workloads, or have memory, PCIe, or configuration issues. PantheonGPU actively tests the GPU instead of only monitoring telemetry. It currently includes 45+ tests covering compute, tensor workloads, memory, cache, PCIe, thermals, stability, and AI/LLM inference. It supports both NVIDIA CUDA and AMD ROCm. I’m also exploring a larger use…
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