The Docker Build That Delivered a Diagnosis: Instrumenting a GPU Proving Pipeline

Message Overview

The subject message ([msg 2990]) is a single Docker build command and its truncated output:

[assistant] [bash] cd /tmp/czk && DOCKER_BUILDKIT=1 docker build --no-cache -f Dockerfile.cuzk-rebuild -t cuzk-rebuild:timing . 2>&1 | tail -40
#15 100.1 note: but type `JobTracker` is only usable at visibility `pub(self)`
#15 100.1    --> cuzk-core/src/engine.rs:121:1
#15 100.1     |
#15 100.1 121 | struct JobTracker {
#15 100.1     | ^^^^^^^^^^^^^^^^^
#15 100.1     = note: `#[warn(private_interfaces)]` on by default
#15 100.1 
#15 100.1 warning: type `JobTracker` is more private than the item `process_monolithic_result`
#15 100.1    --> cuzk-core/src/engine.rs:454:1
#15 100.1     |
#15 100.1 454 | / pub(crate) fn process_monolithic_re...

At first glance, this appears to be a routine build step—just another Docker invocation in a long debugging session. But in the context of the broader investigation, this message represents a pivotal moment: the point at which carefully crafted instrumentation crosses the threshold from development into deployment, transforming from code on disk into a weapon for diagnosing one of the most stubborn performance mysteries in the cuzk proving pipeline.

Context: The GPU Underutilization Mystery

To understand why this Docker build matters, one must understand the investigation it serves. The cuzk daemon is a CUDA-accelerated zero-knowledge proving engine for Filecoin, processing SnapDeals proofs on a machine equipped with an RTX 5090 GPU (32 GB VRAM), 755 GiB of RAM, and a PCIe Gen5 x16 connection capable of ~50 GB/s. Despite this formidable hardware, the team observed that GPU utilization hovered around a frustrating ~50%, with multi-second idle gaps visible even at 200ms resolution in monitoring graphs. Occasionally, three to five partitions would chain together with "perfect" utilization, only to fall back into idle gaps.

The team had already implemented significant improvements: priority-based scheduling for both synthesis and GPU pipelines (boosting throughput from 0.485 to 0.602 proofs per minute, a 24% gain), a budget-based memory manager, and a comprehensive status monitoring API. But the GPU utilization problem remained stubbornly unsolved.

The primary suspect, as documented in the assistant's detailed plan ([msg 2975]), was malloc_trim(0) contention. The finalizer task holds a fin_tracker.lock().await (a tokio Mutex with FIFO-fair semantics) for the entire duration of process_partition_result, which calls libc::malloc_trim(0) on every partition completion. With a 400+ GiB heap and fragmented allocations, malloc_trim can take hundreds of milliseconds. The GPU worker acquires the same tracker lock twice on its hot path—once to check if the partition failed, once to mark it busy—before starting the next prove. If multiple finalizers are queued, each doing malloc_trim, the GPU worker would wait in line. This hypothesis was compelling, but the team's methodology was disciplined: gather real data before fixing.

The Instrumentation Campaign

The assistant had spent the preceding messages ([msg 2971] through [msg 2985]) adding precise timing instrumentation throughout the critical paths. Three categories of timing were added:

  1. GPU worker hot path timing (GPU_TIMING): Measures status_ms (reading partition status), fail_check_ms (first tracker lock acquisition), mark_busy_ms (second tracker lock acquisition), and total_overhead_ms—the overhead between finishing one prove and starting the next.
  2. Spawn-blocking timing (GPU_TIMING): Measures spawn_to_enter_ms (latency entering the blocking context), prove_start_ms (time in gpu_prove_start), and total_pre_prove_ms.
  3. Finalizer timing (FIN_TIMING): Measures finish_ms (time in prove_finish), drop_res_ms (dropping the reservation), lock_wait_ms (waiting for the tracker lock), process_ms (time in process_partition_result), and total_ms. The missing piece—instrumenting the malloc_trim(0) calls themselves—was added in messages [msg 2981] and [msg 2982], wrapping each call site with Instant::now() and logging the duration as malloc_trim_ms within the FIN_TIMING log line. This would finally reveal whether malloc_trim was the culprit or a red herring.

Why This Message Matters

Message [msg 2990] is the Docker build that packages all this instrumentation into a deployable binary. It follows immediately after cargo check ([msg 2988]) confirmed the code compiles cleanly (only pre-existing warnings about JobTracker visibility, which are unrelated to the new code). The build uses DOCKER_BUILDKIT=1 for modern build engine features, --no-cache to force a clean rebuild incorporating the latest uncommitted changes, and the tag cuzk-rebuild:timing to distinguish this build as the instrumented version.

The output shown is the tail of the build log, truncated to 40 lines. It reveals that the build reached step #15 at 100.1 seconds—a substantial build time reflecting the complexity of compiling a CUDA-dependent Rust project with GPU kernels. The warnings visible in the output are the same pre-existing JobTracker visibility warnings from cargo check, confirming that the build completed successfully without new errors.

Input Knowledge Required

To fully understand this message, one needs to know:

Output Knowledge Created

This message produces one critical output: the Docker image cuzk-rebuild:timing. From this image, the binary can be extracted and deployed to the remote machine. The subsequent steps (not shown in this message but documented in the plan) would be:

  1. Extract the binary: docker create --name X cuzk-rebuild:timing /cuzk && docker cp X:/cuzk /tmp/out && docker rm X
  2. Upload to remote: scp /tmp/out/cuzk root@141.0.85.211:/data/cuzk-timing
  3. Kill the old daemon, wait 90-120 seconds
  4. Start the new daemon with the instrumented binary
  5. Let it process live SnapDeals workload
  6. Grep logs for GPU_TIMING and FIN_TIMING lines
  7. Analyze the timing data to identify the actual bottleneck

The Thinking Process Visible in Reasoning

The assistant's approach reveals a disciplined debugging methodology. Rather than jumping to conclusions about the malloc_trim hypothesis, the team insisted on gathering empirical evidence. The todo list maintained throughout the session shows a clear progression: add instrumentation → cargo check → Docker build → deploy → gather logs → analyze → fix. Each step is gated by the previous one's success.

The choice of Docker build over native compilation reflects practical constraints: the development environment may not have the CUDA toolkit and GPU libraries needed for compilation, or the build dependencies are complex enough that containerization is the reliable path. The --no-cache flag is particularly telling—it indicates that the team cannot risk Docker's layer caching serving a stale binary without the latest instrumentation. This is a production-debugging mindset: when chasing an elusive performance bug, every build must be known-good.

The truncated output (only 40 lines via tail) is also informative. The assistant is not interested in the full build log—only in confirming that the build succeeded. The presence of the familiar JobTracker warnings serves as a fingerprint: if these warnings appear, the build reached the Rust compilation stage and completed. Any new error would have appeared in these final lines.

The Ironic Twist

What makes this message particularly interesting in retrospect is that the malloc_trim hypothesis—the very reason this instrumentation was built and deployed—turned out to be incorrect. As revealed in the chunk summaries for Segment 22, the real bottleneck was the Host-to-Device (H2D) transfer of the a/b/c synthesis vectors inside execute_ntts_single. These vectors were standard heap allocations, forcing CUDA to stage transfers through a small pinned bounce buffer at 1-4 GB/s instead of the PCIe Gen5 x16 line rate of ~50 GB/s. The actual GPU compute (MSM, batch_add, tail_msm) was a stable ~1.2 seconds per partition, while the ntt_kernels phase varied wildly from 287ms to 8918ms depending on memory bandwidth contention from concurrent synthesis threads.

The solution would not be to move malloc_trim out of the lock, but to implement a zero-copy pinned memory pool—extending bellperson's ProvingAssignment to use a PinnedVec backed by reusable cudaHostAlloc buffers. This would eliminate the staged copy entirely, collapsing the H2D transfer from seconds to milliseconds.

But none of that was known at the time of this message. The Docker build in [msg 2990] represents the moment before discovery—the point where the team had a hypothesis, built the tools to test it, and was about to ship those tools to the remote machine. The build's success meant the investigation could proceed. The data it would eventually produce would upend the team's assumptions and lead to a fundamentally different fix.

Conclusion

Message [msg 2990] is a Docker build—mundane in isolation, but in context, it is the critical bridge between instrumentation and insight. It represents the disciplined, data-driven approach to performance debugging: form a hypothesis, instrument to test it, deploy the instrumentation, gather real-world data, and let the data guide the fix. The build's success enabled the deployment that would ultimately reveal the true bottleneck, proving once again that in performance engineering, the most valuable tool is not intuition but measurement.