The Deployment That Confirmed a Bottleneck: SCPing an Instrumented Binary to Uncover GPU Underutilization

In the course of a deep investigation into GPU underutilization within the cuzk proving pipeline, a single seemingly mundane command — an SCP transfer — represents the critical bridge between hypothesis and confirmation. The message at <msg id=3043> reads:

[assistant] [bash] scp -P 40612 /tmp/cuzk-timing2 root@141.0.85.211:/data/cuzk-timing2 2>&1

This is a Secure Copy command that transfers a newly built binary (cuzk-timing2) from a local build environment to a remote machine at IP address 141.0.85.211 on port 40612, placing it in /data/cuzk-timing2. On its surface, it is a routine deployment action. But to understand why this message exists — the reasoning, the investigative context, and the decisions that led to this exact command — requires reconstructing the multi-hour diagnostic journey that preceded it.

The Investigative Context: Why This Message Was Written

The team had been grappling with a persistent performance problem: the GPU in the cuzk proving daemon was achieving only approximately 50% utilization. Despite having two GPU workers running in parallel, the GPU would burst to near 100% utilization for roughly 1–2 seconds, then fall into idle gaps lasting 2–8 seconds. The proving pipeline was throughput-bound, and every second of GPU idleness translated directly to slower proof generation.

Earlier messages in the conversation reveal a systematic diagnostic process. The team had already ruled out initial suspects like tracker lock contention and malloc_trim overhead by adding Rust-side instrumentation (GPU_TIMING and FIN_TIMING logging). When those proved clean, the focus shifted to the C++ CUDA kernel code inside groth16_cuda.cu. The team added timing around GPU mutex acquisition, barrier waits, and the ntt_msm_h phase ([msg 3033]–[msg 3036]).

The critical breakthrough came when the team examined the existing CUZK_NTT_H timing logs already present in the C++ code ([msg 3039]). These logs broke down the ntt_msm_h function into its components:

| Component | Typical Time | Notes | |---|---|---| | ntt_kernels | 2239–8918 ms | 4x variation | | coset_intt_sync | ~113 ms | Stable | | msm_invoke | ~630 ms | Stable | | batch_add | ~400 ms | Stable | | tail_msm | ~197 ms | Stable |

The actual GPU compute (MSM, batch addition, tail MSM, inverse NTT) accounted for only about 1.3 seconds per partition. The wild variability lived entirely in ntt_kernels, which ranged from 2.2 to 8.9 seconds. This phase encompasses the Host-to-Device (H2D) transfer of the a/b/c synthesis vectors plus the NTT kernel execution itself.

The User's Key Observation

The user then contributed a crucial piece of evidence ([msg 3041]): during the idle gaps visible in nvtop, the PCIe RX (receive) bandwidth was only 1–4 GB/s, but during compute bursts it would spike to 50 GB/s. This was the fingerprint of the bottleneck.

The assistant's reasoning in <msg id=3042> connected the dots. The 50 GB/s bursts occur during MSM operations, which read the SRS (Structured Reference String) points from CUDA pinned host memory (cudaHostAlloc). These pinned allocations enable direct PCIe DMA at near line rate. The 1–4 GB/s during gaps, by contrast, is the H2D copy of the a/b/c NTT input vectors — which are regular Rust Vec<Fr> allocations from the synthesis phase, backed by standard malloc memory. When CUDA's cudaMemcpy copies from unpinned host memory, the driver must stage the data through a small internal pinned bounce buffer, throttling throughput by a factor of 10–50x.

The assistant's reasoning explicitly states: "That explains the ntt_kernels variance (2.2-8.9s) — it's memory bandwidth contention on the host side during the staged copy, made worse by 20+ synthesis threads thrashing memory."

The Decision: Deploy the Instrumented Binary

At this point, the team had a strong hypothesis but needed definitive confirmation. The existing timing logs did not capture two critical metrics:

  1. Barrier wait time: How long does the GPU thread block inside barrier.wait() while holding the GPU mutex, waiting for the CPU prep_msm_thread to finish its preprocessing?
  2. Mutex contention time: How long does one worker wait for the other to release the GPU mutex? These gaps could represent additional GPU idle time not captured by the CUZK_NTT_H instrumentation. To measure them, the assistant had already edited the C++ source code to add precise timestamps around the mutex lock acquisition (line 900) and the barrier wait inside groth16_cuda.cu ([msg 3033]–[msg 3036]). The Docker build cuzk-rebuild:timing2 was compiled and the binary extracted ([msg 3040], [msg 3042]). The subject message — the SCP command — is the deployment of that instrumented binary to the remote machine where the proving daemon runs. Without this step, the new timing data would remain trapped in the build artifact, never reaching the live environment where real workloads could exercise the code paths and produce the confirming logs.

Assumptions and Knowledge Required

To understand this message, one must be familiar with several layers of context:

Output Knowledge Created

This message does not produce analytical output itself — it is an operational step. The output is the presence of the instrumented binary at /data/cuzk-timing2 on the remote machine, ready for execution. The knowledge that will be created comes from running that binary and collecting its timing logs, which will confirm or refute the H2D bottleneck hypothesis.

However, the message also implicitly documents the deployment process: the exact path, the port, the filename, and the fact that the binary was built and extracted successfully (the md5sum 004c4012991f066033410715a503a0ee was verified in the previous message).

The Thinking Process Visible in the Reasoning

The assistant's reasoning in <msg id=3042> reveals a structured analytical process:

  1. Observation: RX bandwidth is 1–4 GB/s during gaps vs 50 GB/s during compute.
  2. Pattern matching: The 50 GB/s matches known pinned-memory DMA throughput. The 1–4 GB/s matches staged copies through a bounce buffer.
  3. Root cause identification: The a/b/c vectors are standard heap allocations, not pinned memory.
  4. Quantification: At 1–4 GB/s, a ~1.5 GiB transfer takes 0.4–1.5 seconds. At 50 GB/s, it would take ~40 ms.
  5. Cross-referencing: This aligns with the ntt_kernels variance (2.2–8.9s) observed in the CUZK_NTT_H logs.
  6. Action: Deploy the instrumented binary to gather definitive mutex/barrier timing data. The reasoning also shows the assistant considering and rejecting alternative approaches. It briefly considers pre-allocating pinned buffers for synthesis outputs or using a memory pool, but notes that device memory constraints make this impractical (32 GiB GPU memory, needing 16 GiB just for staging buffers with two workers). It concludes that registering host vectors for direct DMA is the cleaner approach, but first wants to confirm the hypothesis with timing data.

Mistakes and Incorrect Assumptions

One subtle assumption worth examining: the assistant assumed that the ntt_kernels timing includes the H2D transfer. In the execute_ntt_msm_h function, the timing spans from before the NTT kernel launches to after synchronization. If the H2D copy is synchronous (as it likely is for single-circuit mode), then yes, the transfer time is captured. But if the copy is asynchronous and the synchronization point is elsewhere, the timing might be misleading. The subsequent deployment and log collection would resolve this ambiguity.

Another assumption is that the new instrumentation would not introduce significant overhead. Adding gettimeofday calls around mutex operations is low-cost, but on a heavily contended mutex, even microsecond-level overhead could slightly perturb timing. The assistant implicitly assumes this perturbation is negligible relative to the multi-second timescales being measured — a reasonable engineering judgment.

Conclusion

The SCP command in <msg id=3043> is a small action with large significance. It represents the moment when a well-developed hypothesis about GPU underutilization — rooted in the difference between pinned and unpinned memory allocation — is put to the test. The instrumented binary carries the timing probes that will reveal exactly how much GPU time is lost to H2D transfer bottlenecks versus mutex contention versus barrier synchronization. In the messages that follow, the team will kill the old process, wait for memory to drain, launch the new binary, and finally collect the logs that confirm the diagnosis — leading directly to the design and implementation of a zero-copy pinned memory pool solution that eliminates the bottleneck entirely.