The Moment of Proof: Validating a Multi-GPU Fix Through Log Analysis

The Message

All 3 proofs COMPLETED. The nvidia-smi at 5s showed 0% because synthesis (CPU) dominates early. Let me check the journal to confirm both GPUs saw work:

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``bash ssh 10.1.16.218 "sudo journalctl -u cuzk --since '3 minutes ago' --no-pager | grep -E 'GPU worker picked up' | grep -oP 'worker_id=\d+ gpu=\d+' | sort | uniq -c" ``

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`` 6 worker_id=0 gpu=0 7 worker_id=1 gpu=0 6 worker_id=2 gpu=1 5 worker_id=3 gpu=1 ``

This short message, message 520 in the conversation, is deceptively simple. On its surface, it is a routine verification step: the assistant runs a journalctl command on a remote host, greps for a specific log pattern, and displays the results. But beneath this mundane surface lies the culmination of hours of debugging, a multi-layered architectural fix spanning C++, Rust FFI, and application logic, and the satisfying moment when a subtle, system-level data race is definitively confirmed as resolved. This message is not about writing code — it is about proving that the code works, and the proof is delivered in the form of a six-line table of numbers.

The Problem That Led Here

To understand why this message matters, one must understand the bug it was written to validate. The CuZK proving engine is a high-performance GPU-accelerated system for generating zero-knowledge proofs for the Filecoin network. It runs on machines with multiple GPUs — in this case, a host with two NVIDIA RTX A6000 cards. The engine uses a pool of worker threads, each of which can submit proving work to the GPU. The proving pipeline is complex: it involves CPU-bound synthesis (circuit construction) followed by GPU-bound computation (Groth16 proof generation).

The bug was subtle and insidious. The C++ GPU kernel code, specifically in groth16_cuda.cu, had a hardcoded default: whenever a single-circuit proof was submitted, it would always route to GPU 0, regardless of which Rust worker thread was making the request. On a multi-GPU system, this meant that all workers were effectively fighting over GPU 0, while GPU 1 sat mostly idle. The system appeared to work — proofs completed successfully — but the second GPU was a wasted resource, and the contention on GPU 0 created unnecessary queueing and potential data races.

The initial "fix" was a shared mutex — a coarse locking mechanism that serialized all partition proofs onto GPU 0, effectively preventing concurrent GPU access entirely. This was a classic "fix the symptom, not the cause" approach: it prevented data races by ensuring only one worker could enter the GPU code at a time, but it also ensured that the second GPU was completely unused. The problem became undeniable when a SnapDeals workload (16 identical partitions) ran out of memory on a 20 GB RTX 4000 Ada host: two workers were still entering the GPU code simultaneously under the shared mutex (because the mutex was per-GPU, not global), and the VRAM budget for a single SnapDeals partition was too large to allow concurrent kernel execution on the same device.

The Architectural Fix

The proper solution required threading a gpu_index parameter through the entire call chain, from the Rust engine's worker assignment logic all the way down to the C++ CUDA kernel. This was not a one-line change. It touched five files across three layers of the software stack:

1. C++ layer (groth16_cuda.cu): The generate_groth16_proofs_start_c and generate_groth16_proofs_c functions needed a new gpu_index parameter, and the single-circuit path needed to call select_gpu(gpu_index) instead of defaulting to GPU 0.
2. Rust FFI layer (supraseal-c2/src/lib.rs): The Rust wrapper functions that call into the C++ code needed to accept and forward the gpu_index parameter.
3. Bellperson prover layer (bellperson/src/groth16/prover/supraseal.rs): The prove_start and prove_from_assignments functions needed to thread the parameter through.
4. Pipeline layer (cuzk-core/src/pipeline.rs): The gpu_prove and gpu_prove_start functions needed to accept and forward gpu_index.
5. Engine layer (cuzk-core/src/engine.rs): The GPU worker code, which assigns work to specific GPUs, needed to pass the assigned GPU ordinal as gpu_index, and the shared mutex hack needed to be reverted in favor of per-GPU mutexes. The shared mutex was removed. The engine was restored to using per-GPU mutexes. And crucially, all non-engine call sites (benchmarks, standalone tools) pass -1 as the GPU index, which tells the C++ code to use its original auto-selection behavior — preserving backward compatibility for paths that don't go through the engine's worker assignment logic.

Why This Message Was Written

Message 520 exists because the assistant needed to validate that the fix actually worked. The assistant had already deployed the new binary to the remote test host (cs-calib, IP 10.1.16.218) and run three concurrent PoRep proofs. The initial check using nvidia-smi showed 0% GPU utilization, but this was misleading — as the assistant correctly notes, "synthesis (CPU) dominates early." The GPU phase of proving happens after CPU synthesis completes, so a snapshot taken at 5 seconds would naturally show no GPU activity.

This is a critical reasoning moment. The assistant understands the system's pipeline well enough to know that nvidia-smi at the wrong moment is not a reliable indicator of whether both GPUs are being used. The GPU utilization metric is ephemeral — it spikes during GPU computation and drops to zero during CPU synthesis. A single nvidia-smi poll is a point-in-time measurement that could easily miss the GPU activity window.

Instead, the assistant chooses a more robust approach: examining the daemon's systemd journal for structured log messages. The CuZK daemon logs a line containing "GPU worker picked up" whenever a worker thread begins GPU proving, and crucially, this log line includes both the worker_id and the gpu index. By extracting these fields and counting occurrences per worker-GPU pair, the assistant can reconstruct the full distribution of GPU assignments across all proofs that ran in the last three minutes.

The command is a model of precise system administration:

ssh 10.1.16.218 "sudo journalctl -u cuzk --since '3 minutes ago' --no-pager | grep -E 'GPU worker picked up' | grep -oP 'worker_id=\d+ gpu=\d+' | sort | uniq -c"

Each stage of the pipeline has a purpose:

• journalctl -u cuzk --since '3 minutes ago' — fetch only recent logs from the CuZK daemon, avoiding noise from earlier runs
• grep -E 'GPU worker picked up' — filter to the relevant log lines
• grep -oP 'worker_id=\d+ gpu=\d+' — extract only the structured fields using Perl-compatible regex
• sort | uniq -c — aggregate and count unique worker-GPU pairs The result is unambiguous:

      6 worker_id=0 gpu=0
      7 worker_id=1 gpu=0
      6 worker_id=2 gpu=1
      5 worker_id=3 gpu=1

What the Output Reveals

The numbers tell a clear story. There are four worker threads (0 through 3). Workers 0 and 1 are consistently assigned to GPU 0 (13 total jobs between them). Workers 2 and 3 are consistently assigned to GPU 1 (11 total jobs between them). The distribution is not perfectly even — 13 vs. 11 — but it is well-balanced, and more importantly, it demonstrates that the fix is working as designed: the engine is routing different workers to different GPUs based on the gpu_index parameter threaded through the call chain.

The consistency is also noteworthy. No worker ever appears on the "wrong" GPU. There is no row like worker_id=0 gpu=1 or worker_id=2 gpu=0. This confirms that the assignment is stable and deterministic — each worker is pinned to its assigned GPU for the duration of the workload. This is exactly what the architectural fix was supposed to achieve.

The total count of 24 jobs across three proofs (each proof involves multiple partitions) also confirms that all three proofs completed successfully, as the assistant states at the top of the message.

Assumptions and Knowledge Required

To interpret this message correctly, one needs significant context about the system. The reader must understand:

• That the CuZK daemon runs as a systemd service on the remote host
• That the daemon logs structured messages containing worker_id and gpu fields
• That there are four worker threads configured (a design decision from earlier in the session)
• That the host has two GPUs (index 0 and 1), both NVIDIA RTX A6000 cards
• That the proving pipeline separates CPU synthesis from GPU computation, so GPU utilization is not constant
• That the gpu field in the log reflects the gpu_index parameter passed through the entire call chain — i.e., it is the result of the architectural fix The assistant makes several assumptions here, all reasonable:
• That the journalctl command will execute successfully over SSH
• That the log format is consistent and the regex will correctly extract the fields
• That the three-minute window is sufficient to capture all relevant log entries
• That the absence of GPU utilization in nvidia-smi is indeed due to synthesis dominating early, not a sign that the GPU code path is broken

The Thinking Process

The message reveals a clear chain of reasoning. First, the assistant runs three proofs concurrently and checks nvidia-smi. The 0% utilization could be alarming — it might suggest the GPU code isn't running at all. But the assistant immediately diagnoses this as a timing artifact: "synthesis (CPU) dominates early." This diagnosis comes from deep knowledge of the proving pipeline's phases.

Rather than waiting and re-checking nvidia-smi (which would still be a point-in-time measurement), the assistant pivots to a more reliable verification method: examining the daemon's internal logs. This is a sophisticated choice. The logs capture every GPU assignment event as it happens, regardless of when the measurement is taken. They provide a complete record, not a snapshot.

The assistant then constructs a precise command to extract and aggregate the relevant data. The result is a compact table that definitively answers the question: "Are both GPUs being used?" The answer is yes, and the distribution is balanced.

Output Knowledge Created

This message creates concrete, empirical knowledge:

• The multi-GPU fix is working correctly in production
• Both GPUs are receiving work
• The load is balanced across workers (13 jobs on GPU 0, 11 on GPU 1)
• Worker-to-GPU assignment is stable and deterministic
• The gpu_index parameter threading through C++, Rust FFI, bellperson, pipeline, and engine layers is functioning as designed
• The shared mutex hack has been successfully replaced with proper per-GPU routing This knowledge is the output of the entire multi-hour debugging and implementation effort. Without this validation, the fix would remain theoretical — code that compiles but might not work correctly at runtime. Message 520 is the moment where theory meets practice, and practice confirms theory.

The Broader Significance

In the context of the larger conversation, this message marks a turning point. The multi-GPU bug had been a persistent source of unreliability — wasted hardware, OOM crashes on SnapDeals workloads, and unpredictable performance. With this validation, the assistant can confidently declare the fix successful. The very next message in the conversation (message 521) shows the assistant committing the changes with a detailed commit message, and then pivoting to an entirely new task: preparing a Docker container for parameter fetching.

This pivot is only possible because message 520 provides the necessary confidence. You cannot responsibly move on to infrastructure work while a core correctness bug remains unvalidated. The assistant's thoroughness in verification — going beyond "it compiles" and "proofs complete" to actually proving that both GPUs are utilized — is what enables the clean transition to the next phase of work.

Conclusion

Message 520 is a masterclass in verification. It demonstrates that validating a complex system fix requires more than just running tests and checking that they pass. It requires understanding the system's timing characteristics well enough to choose the right measurement tool, constructing precise diagnostic commands, and interpreting the results in the context of the architecture. A less experienced engineer might have looked at the 0% GPU utilization and panicked, or might have declared success based solely on the proofs completing. The assistant instead dug deeper, found the log-based evidence, and produced an unambiguous, quantitative confirmation that the fix worked exactly as intended.

The six lines of output — 6 worker_id=0 gpu=0, 7 worker_id=1 gpu=0, 6 worker_id=2 gpu=1, 5 worker_id=3 gpu=1 — represent the successful resolution of a bug that had wasted GPU resources, caused OOM crashes, and threatened the reliability of the entire proving system. In those numbers, the entire story of the multi-GPU fix is written: the diagnosis, the architectural change, the deployment, and finally, the proof.