The Moment of Certainty: How an nvtop Screenshot and a Request for Logging Resolved a GPU Utilization Mystery

Message in Focus

User (msg 3031): @2026-03-13-192826_2329x324_scrot.png nvtop at 0.1s - does that fit? Can we add logging to be certain of the reason?

This short, two-sentence message from the user arrives at a pivotal moment in a deep debugging session. The team has been investigating why GPU utilization hovers around a frustrating ~50% in the cuzk zero-knowledge proof proving pipeline, despite a deliberate architectural choice to deploy two GPU workers precisely to overlap PCIe transfers with GPU compute. The message contains two elements: a visual artifact (an nvtop screenshot at 0.1-second resolution) and a request for action ("Can we add logging to be certain?"). Together, they mark the transition from hypothesis to verification.

The Context That Produced This Message

To understand why this message was written, one must trace the investigation that preceded it. The session had been tracking a persistent underutilization problem. The cuzk pipeline processes zero-knowledge proofs in partitions, each requiring GPU compute time of roughly 1.5–2 seconds. Yet the observed prove_start_ms (the wall-clock time of the C++ generate_groth16_proofs_start_c function) ranged from 4.2 to 16.2 seconds per partition — far exceeding the actual GPU compute. The assistant had already ruled out several suspects: Rust-side hot-path overhead was negligible, the finalizer's tracker lock showed no contention, and malloc_trim (32–271ms) was not on the critical path.

The breakthrough came when the assistant examined the C++ CUDA timing instrumentation (CUZK_TIMING logs) retrieved from the remote machine. These logs revealed a stark asymmetry: batch_add_ms and tail_msm_ms were nearly constant at ~400ms and ~197ms respectively, but ntt_msm_h_ms varied wildly from 2,699ms to 8,860ms — a 3× swing. The prep_msm_ms (CPU preprocessing) also varied from 1,488ms to 4,178ms. The assistant hypothesized that ntt_msm_h was not pure GPU compute but included significant Host-to-Device (H2D) PCIe transfer time for the NTT input vectors, and that memory bandwidth contention from concurrent synthesis threads was throttling these transfers from the expected PCIe Gen5 x16 line rate (~50 GB/s) down to a crawl of 1–4 GB/s.

This was a compelling hypothesis, but it remained inference. The CUZK_TIMING logs measured the combined time of the NTT setup phase without separating H2D copy from kernel execution. The assistant could see the symptom but not the mechanism.

The nvtop Screenshot: Visual Evidence at 0.1s Resolution

The user's first contribution — the nvtop screenshot — provides the missing visual dimension. nvtop is a GPU process monitoring tool analogous to htop for CPUs, showing real-time GPU utilization, memory usage, and PCIe bandwidth. The user captured it at 0.1-second polling resolution, which is fast enough to resolve individual partition cycles (each lasting several seconds).

The assistant's analysis of the screenshot (visible in the subsequent message [msg 3032]) confirmed the hypothesis: the blue GPU utilization line showed short bursts at 75–100% (the actual CUDA kernel execution — MSMs, NTTs, batch additions) separated by gaps of 1–5 seconds at 0–25% utilization. The cyan memory bandwidth line dropped near zero during these gaps, confirming the GPU was truly idle rather than running background transfers. Crucially, some residual activity appeared on the cyan line between the main compute spikes — consistent with H2D copies happening inside ntt_msm_h before the NTT kernel launched.

The PCIe bandwidth readings (RX: 1.015 GiB/s, TX: 94.49 MiB/s) told their own story. A healthy PCIe Gen5 x16 link should deliver ~50 GB/s. The observed 1 GiB/s RX rate was consistent with a bottlenecked, bounce-buffered transfer path — exactly what one would expect if the a/b/c synthesis vectors were allocated as standard heap memory rather than pinned (page-locked) memory, forcing CUDA to stage transfers through a small pinned bounce buffer.

The user's question — "does that fit?" — is thus a request for validation: does the visual pattern in nvtop align with the assistant's theory of H2D transfer bottlenecks and memory bandwidth contention? The answer, as the assistant's reasoning shows, is yes — the pattern fits perfectly. The bursty compute, the residual PCIe activity in gaps, and the low sustained transfer rate all point to the same root cause.

The Request for Logging: From Inference to Certainty

The second half of the message — "Can we add logging to be certain of the reason?" — is arguably the more important contribution. It reflects a disciplined engineering mindset: hypothesis is not enough; one needs instrumentation to confirm. The existing CUZK_TIMING logs measured aggregate phase durations but did not break down the critical path inside the GPU mutex. Specifically, the code had no instrumentation to answer two questions:

How long does the GPU thread wait at barrier.wait() for the CPU prep_msm_thread to finish? This wait happens while holding the GPU mutex, meaning if the CPU preprocessing is slower than the NTT setup, the GPU idles and blocks the other worker from acquiring the mutex.
How long does H2D transfer take within ntt_msm_h versus actual kernel execution? Without separating these, one cannot distinguish between a compute-bound bottleneck and a transfer-bound bottleneck. The user's request implicitly recognizes that the existing timing data, while suggestive, is not definitive. The ntt_msm_h_ms value of 8,860ms could be caused by slow H2D copies, or by slow NTT kernels, or by contention at the barrier. Only by adding precise timestamps around the mutex acquisition, the barrier wait, and the H2D transfer itself can the team pinpoint the exact mechanism.

Assumptions and Implicit Knowledge

The message rests on several assumptions. First, the user assumes that 0.1-second nvtop resolution is sufficient to resolve the relevant dynamics. Given that partition cycles last several seconds and the gaps are 1–5 seconds, this is a reasonable assumption — the 0.1s polling captures the burst structure without aliasing.

Second, the user assumes that adding logging will definitively resolve the uncertainty. This is a methodological assumption: that the bottleneck is measurable and that the right instrumentation will reveal it. The assistant's subsequent reasoning validates this by identifying specific instrumentation points: timestamps around std::unique_lock<std::mutex> acquisition (line 900 of groth16_cuda.cu), around barrier.wait(), and around the H2D cudaMemcpy calls within ntt_msm_h.

Third, the user assumes the assistant has access to modify the C++ code and redeploy. This is a practical assumption about the development workflow — that the iteration cycle of edit, compile, deploy, and test is fast enough to make logging worthwhile.

The Thinking Process Visible in the Assistant's Response

The assistant's reasoning in [msg 3032] reveals a systematic approach to the user's request. It does not simply say "yes, let's add logging." Instead, it first validates the nvtop data against the existing hypothesis, then identifies the specific instrumentation gaps, then reads the C++ source to find the exact lines where timestamps should be inserted. The reasoning shows the assistant mentally simulating the flow: "the blue spikes show ~1-2s active bursts with 2-8s gaps... the cyan line shows some low-level activity in the gaps — that's likely the H2D PCIe transfers inside ntt_msm_h before kernels launch."

The assistant then identifies the two critical unknowns: barrier wait time and mutex contention time. It reads the C++ source to find the exact lines (line 900 for mutex acquisition, the barrier.wait() call within the GPU thread) and plans to add instrumentation there. This is not a generic "add more logging" response — it is a targeted instrumentation plan informed by a deep understanding of the code's concurrency model.

Output Knowledge and Impact

This message and its response produce several concrete outcomes. First, the nvtop analysis confirms the H2D bottleneck hypothesis, ruling out alternative explanations like GPU kernel inefficiency or CPU-side overhead. Second, the decision to add granular C++ instrumentation creates a path to definitive proof. Third, the specific instrumentation points identified (mutex acquisition, barrier wait, H2D copy) become the blueprint for the next implementation step.

In the broader arc of the session, this message is the turning point. Before it, the team had a hypothesis supported by aggregate timing data. After it, they have a plan to instrument the exact mechanism. The subsequent chunks (Chunk 0 and Chunk 1 of Segment 22) describe the implementation of a zero-copy pinned memory pool solution — the architectural fix that addresses the root cause the user's nvtop screenshot helped confirm. The user's simple two-sentence message thus serves as the catalyst that transforms inference into certainty and certainty into action.