The Critical Handoff: Deploying an Instrumented Binary to Diagnose GPU Underutilization

The Message

Build succeeded. Now let me extract the binary and deploy it:

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``bash docker rm -f cuzk-timing-extract 2>/dev/null; docker create --name cuzk-timing-extract cuzk-rebuild:timing /cuzk && docker cp cuzk-timing-extract:/cuzk /tmp/cuzk-timing && docker rm cuzk-timing-extract && md5sum /tmp/cuzk-timing && ls -lh /tmp/cuzk-timing ``

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`` 2d6c5825e1038fb8f140735416af822c593912de928a0e8891a5dce03a4f148e cuzk-timing-extract 7726e73956bd36e20a4acfdf11e734ef /tmp/cuzk-timing -rwxr-xr-x 1 theuser theuser 27M Mar 13 19:13 /tmp/cuzk-timing ``

At first glance, this appears to be a routine deployment step: extract a compiled binary from a Docker image and verify its integrity. But this message, sent by the AI assistant in the cuzk proving engine development session, represents a pivotal transition point in a sophisticated performance debugging operation. It is the moment when hypothesis meets reality—when weeks of instrumentation work are finally put to the test against a live, production-grade GPU proving workload.

The Context: A Mystery of Missing Megawatts

To understand why this message was written, one must understand the problem it aims to solve. The cuzk (CUDA ZK) proving daemon is a high-performance system for generating zero-knowledge proofs on NVIDIA GPUs. It operates on a remote machine with a single RTX 5090 (32 GB VRAM), 755 GiB of RAM, and a PCIe Gen5 x16 connection capable of approximately 50 GB/s of theoretical bandwidth. The system processes Filecoin proof types—SnapDeals, WindowPoSt, and WinningPoSt—through a pipeline that synthesizes constraint systems on the CPU, then proves them on the GPU.

The symptom was stark and frustrating: the GPU was running at approximately 50% utilization, with multi-second idle gaps visible even at 0.2-second resolution in monitoring graphs. These gaps appeared between partition proves—the GPU would finish one partition, then sit idle while waiting for the next. Given that the system had a backlog of 27 provers waiting in the GPU queue (synthesis complete, ready for proving) and only 6 active synthesis threads, the bottleneck was clearly not a lack of work. Something in the handoff between CPU-side preparation and GPU-side execution was causing the GPU to starve.

The investigation had already ruled out several suspects. Priority scheduling had been implemented and verified, eliminating GPU contention between jobs as the cause. The synthesis dispatch had been serialized to enforce strict ordering. Yet the utilization gap persisted. The primary hypothesis centered on two malloc_trim(0) calls inside the process_partition_result function, which ran while holding a tokio::sync::Mutex on the job tracker. With a 400+ GiB heap and severe fragmentation, malloc_trim could take hundreds of milliseconds per call—and with many finalizer tasks queued, each doing its own malloc_trim, the GPU worker would be forced to wait in line for the tracker lock before it could start the next prove.

But this was only a hypothesis. The team's discipline demanded evidence before action. As noted in the session's instructions, the user had "insisted on gathering real timing data before fixing." This message is the direct consequence of that insistence.## The Instrumentation Campaign

The message sits at the end of a long chain of precise, surgical modifications to the engine's hot paths. Over the preceding messages, the assistant had added GPU_TIMING log lines around every critical section of the GPU worker loop: the time spent checking the status tracker (status_ms), the first tracker lock acquisition for the fail check (fail_check_ms), the second tracker lock for marking the worker busy (mark_busy_ms), the overhead of spawn_blocking, and the actual prove_start duration. Similarly, FIN_TIMING instrumentation had been added to the finalizer task, measuring finish_ms (the prove_finish call), drop_res_ms (dropping the reservation), lock_wait_ms (waiting for the tracker lock), and process_ms (the process_partition_result call containing malloc_trim).

The final piece—instrumenting the malloc_trim(0) calls themselves—was completed in the message immediately preceding this one ([msg 2981]-[msg 2982]). The assistant added timing wrappers around both malloc_trim calls inside process_partition_result, logging malloc_trim_ms as part of the FIN_TIMING output. This was the last missing data point needed to either confirm or refute the malloc_trim contention hypothesis.

With the instrumentation complete, the assistant ran cargo check ([msg 2988]), which compiled cleanly with only pre-existing warnings about a visibility issue unrelated to the new code. Then came the Docker build ([msg 2990]), which also succeeded. The message we are analyzing is the immediate sequel: extracting the binary from the Docker image and preparing it for deployment.

The Decisions Embedded in This Message

This message embodies several important decisions, even though they are expressed through a single shell command. The first is the decision to use Docker as the build isolation mechanism. The cuzk project uses a Dockerfile.cuzk-rebuild that encapsulates the full build environment, including the CUDA toolkit, the Rust toolchain with the cuda-supraseal feature flag, and all native dependencies. Building inside Docker ensures reproducibility and avoids polluting the host system with build dependencies. The --no-cache flag was used to force a clean build, guaranteeing that the instrumentation changes were compiled from scratch rather than relying on cached layers that might not reflect the latest edits.

The second decision is the extraction method: docker create to create a container without running it, docker cp to copy the binary out, then docker rm to clean up. This is a deliberate choice over docker run because the container's entry point (/cuzk) is the binary itself—running it would start the daemon, not return the binary. By creating a container in a stopped state, the assistant can copy the filesystem artifact directly.

The third decision is the verification step: md5sum and ls -lh. The MD5 hash (7726e73956bd36e20a4acfdf11e734ef) and file size (27 MB) serve as a fingerprint for the binary. When the binary is later deployed to the remote machine and produces timing logs, the team can confirm that the logs came from this exact build. This is particularly important in a debugging scenario where multiple binary versions may be in play.

Assumptions and Their Implications

This message makes several assumptions worth examining. The first is that the Docker build environment faithfully reproduces the remote execution environment. The remote machine runs inside a Docker container with an overlay filesystem—a detail that had caused deployment issues earlier in the session. The binary is built on the development machine (not the remote) and must be compatible with the remote's CUDA runtime, glibc, and kernel. If there is a mismatch (e.g., different CUDA driver versions), the binary could crash at startup or produce incorrect results. The assistant implicitly trusts that the Docker build image matches the remote's runtime environment.

The second assumption is that the instrumentation overhead is negligible. Adding Instant::now() calls and logging on every hot-path iteration adds CPU cycles and I/O. In a system where microseconds matter, these measurements could perturb the very behavior being measured. The assistant assumes that the overhead of a few Instant::now() calls and info!() log statements is small relative to the multi-second gaps being investigated—a reasonable assumption, but one that should be validated.

The third assumption is that the logs will be accessible and interpretable. The deployment plan calls for grepping GPU_TIMING and FIN_TIMING from the log file. This assumes that logging is configured to output to a file, that the log level is set to include info! messages, and that the log format is parseable. If any of these assumptions fail, the entire instrumentation effort yields no data.

Knowledge Required and Knowledge Created

To fully understand this message, one needs input knowledge spanning several domains: Docker container lifecycle (create, cp, rm), CUDA GPU programming concepts (H2D transfers, pinned memory, PCIe bandwidth), Rust async runtime internals (tokio mutex fairness, spawn_blocking), Linux memory management (malloc_trim, heap fragmentation), and the specific architecture of the cuzk proving pipeline (synthesis, GPU proving, finalizer tasks, job tracker).

The output knowledge created by this message is concrete and actionable: a 27 MB instrumented binary with a known MD5 hash, ready for deployment. But more importantly, this message creates the conditions for knowledge discovery. The binary, once deployed and run, will produce timing logs that will either confirm the malloc_trim hypothesis or redirect the investigation to other causes. In this sense, the message is not an endpoint but a gateway—the instrument that will generate the data needed to make the next decision.

The Thinking Process Visible in the Reasoning

The assistant's reasoning, visible in the preceding messages, reveals a methodical approach to performance debugging. Rather than guessing at the bottleneck, the assistant systematically added instrumentation to every potential suspect: the tracker lock acquisitions, the spawn_blocking overhead, the prove_start duration, the finalizer steps, and finally the malloc_trim calls. Each instrumentation point was chosen to test a specific hypothesis about where time was being spent.

The reasoning also shows an awareness of the "observer effect"—the concern that instrumentation itself could alter behavior. The assistant chose to use Instant::now() (a monotonic clock with nanosecond precision but low overhead) and info!() logging (asynchronous in most logging frameworks) to minimize perturbation. The decision to instrument malloc_trim separately within process_partition_result, rather than relying on the aggregate process_ms timing, shows a desire for precision: if malloc_trim is the culprit, its contribution should be measured directly, not inferred.

The Broader Significance

This message is a microcosm of the engineering discipline required to debug high-performance systems. The problem—GPU underutilization—could have many causes: memory bandwidth contention, lock contention, CPU-side bottlenecks, PCIe transfer limits, or GPU kernel launch overhead. Each cause requires a different fix. Without data, the team would be guessing. With data, they can act with confidence.

The message also illustrates the importance of build and deployment infrastructure in the debugging loop. The ability to modify code, rebuild, extract, deploy, and gather logs in minutes rather than hours is what makes iterative performance debugging feasible. The Docker build, the extraction script, the SSH deployment—all of these are invisible scaffolding that enables the real work of understanding and fixing the system.

In the end, this message is about trust: trust in the instrumentation, trust in the build process, trust in the deployment pipeline, and trust that the data will reveal the truth. The binary is extracted, the hash is recorded, and the next step awaits. The investigation continues.