Reading the Pulse of a Proving Engine: Measuring Throughput After a GPU Race Condition Fix

Introduction

In the midst of a high-stakes debugging session involving a multi-GPU race condition in a zero-knowledge proving engine, a single message stands out as a moment of calm assessment. After hours of chasing a subtle data race that caused partitioned PoRep (Proof of Replication) proofs to fail on multi-GPU systems, the assistant had deployed a fix, verified that proofs completed successfully, and then received a simple but pointed question from the user: "Look at cuzk journal - what throughput are we getting?" (see [msg 420]). The assistant's response, captured in [msg 422], represents a critical shift in the conversation from "does it work?" to "how well does it work?" — a transition from debugging to performance evaluation.

The Message in Full

The subject message, [msg 422], contains the assistant's reasoning and a single bash command executed on the remote host:

Agent Reasoning: I can see the logs showing partitioned PoRep proofs running with 10 partitions each. Let me get a summary of the throughput - how many proofs are completing and what the timing looks like. I should look for completed proof lines.

>

[bash] ssh 10.1.16.218 "sudo journalctl -u cuzk --since '30 minutes ago' --no-pager | grep -E '(Phase 7: all partitions complete|PoRep proof self-check PASSED|job enqueued|job dequeued|proof_len=1920)' | tail -30"

>

Mar 02 12:32:25 cs-calib cuzk[738105]: 2026-03-02T12:32:25.395599Z INFO cuzk_core::engine: Phase 7: all partitions complete, proof assembled job_id=porep-181521-28 proof_len=1920 total_ms=217214 synth_ms=519046 gpu_ms=294538 Mar 02 12:32:25 cs-calib cuzk[738105]: 2026-03-02T12:32:25.840422Z INFO verify_porep_proof{job_id="porep-181521-28"}: cuzk_core::prover: verifying PoRep proof (self-check) registered_proof=StackedDrg32GiBV1_1 proof_len=1920 sector_number=28 miner_id=181521 Mar 02 12:32:25 ...

At first glance, this appears to be a straightforward log-grep command. But the reasoning behind it, the decisions embedded in its construction, and the context that made this query meaningful reveal a rich story about systems debugging, performance measurement, and the iterative nature of engineering verification.

Why This Message Was Written: The Motivation and Context

To understand why this message exists, we must trace the conversation that led to it. The preceding hours had been consumed by a particularly insidious bug: partitioned PoRep proofs were intermittently failing on a dual-GPU remote host (cs-calib, 10.1.16.218). The root cause, eventually identified, was a GPU race condition. The C++ GPU proving code in groth16_cuda.cu always routed single-circuit proofs to GPU 0, regardless of which Rust worker submitted them. On a multi-GPU system, this meant that workers assigned to different GPUs would both end up executing on GPU 0 simultaneously, corrupting each other's state.

The initial "fix" was a shared mutex that serialized all partition proofs onto GPU 0 — effectively wasting the second GPU and creating a bottleneck. This worked for correctness but failed under load: a SnapDeals workload with 16 identical partitions caused an out-of-memory (OOM) crash on a 20 GB RTX 4000 Ada host because two workers still entered the GPU code simultaneously, and a single SnapDeals partition was too large to allow concurrent kernel execution on the same device.

The proper fix, implemented in the chunk preceding this message, threaded a gpu_index parameter through the entire call chain — from the C++ CUDA code through the Rust FFI, the bellperson prover functions, the pipeline layer, and finally the engine's GPU worker code. This ensured that the C++ code used the GPU assigned by the Rust engine instead of always defaulting to GPU 0. The shared mutex hack was reverted, and all call sites now pass either the assigned GPU ordinal or -1 (auto) for non-engine paths.

With the fix deployed and a single PoRep proof verified as passing (see [msg 411]), the user's question at [msg 420] — "Look at cuzk journal - what throughput are we getting?" — was the natural next step. The assistant had proven correctness; now it needed to prove performance. The question was not idle curiosity. The entire point of the multi-GPU fix was to utilize both GPUs effectively. If throughput hadn't improved, the architectural change would have been wasted effort. The assistant needed to quantify the result of its work.

How Decisions Were Made

The message reveals several deliberate decisions in how the assistant approached the throughput measurement problem.

Decision 1: Choose the right time window. The assistant selected --since '30 minutes ago' as the journalctl time filter. This was not arbitrary. The fix had been deployed and the service restarted approximately 30 minutes earlier (the service was restarted at 12:24:39 UTC per [msg 400], and the current time was around 12:55 based on the log timestamps). A 30-minute window would capture all proofs run under the new code while excluding any stale data from before the fix. This decision reflects an understanding that performance measurements are only meaningful when taken against a consistent software baseline.

Decision 2: Select the right log patterns to grep. The assistant constructed a compound grep expression targeting four distinct log events:

Assumptions Embedded in the Approach

The assistant's approach rests on several assumptions, most of them reasonable but worth examining.

Assumption 1: The journal is the authoritative source of throughput data. The assistant assumed that journalctl on the remote host would contain complete, accurate timing information for every proof. This is generally true for systemd-managed services, but it assumes that the cuzk daemon's logging configuration captures all relevant events at the INFO level and that no log rotation has truncated the relevant window. The assistant also assumed that the journal's timestamps are reliable and that the 30-minute window contains a contiguous record of all proofs.

Assumption 2: The grep patterns capture all proof completions. The assistant assumed that every completed proof produces a log line matching one of the selected patterns. This is a reasonable assumption given the assistant's familiarity with the codebase (it had been working with these exact log statements throughout the debugging session), but it does assume that no edge cases — such as proofs that fail self-check or jobs that are cancelled — would produce misleading results.

Assumption 3: The workload is homogeneous. The assistant assumed that all proofs in the 30-minute window are comparable — that they use the same proof type (PoRep), the same sector size (32 GiB), and the same number of partitions. The log output confirms this: the registered_proof=StackedDrg32GiBV1_1 field indicates a 32 GiB PoRep proof. If the workload had included mixed proof types, the throughput calculation would have been meaningless.

Assumption 4: Throughput can be estimated from a single 30-minute sample. The assistant implicitly assumed that 30 minutes of production-equivalent load is sufficient to estimate steady-state throughput. This ignores warm-up effects (GPU kernel compilation caching, PCE cache hits, memory allocation patterns) that might cause the first few proofs to be slower. However, given that the system had already been running for 30 minutes, these effects were likely amortized.

Input Knowledge Required to Understand This Message

A reader needs significant domain knowledge to fully grasp what this message is doing:

  1. The architecture of the CuZK proving engine. The assistant is querying a remote daemon that accepts proof jobs over a Unix socket, dispatches them to GPU workers, and logs progress at each pipeline stage. Understanding the "Phase 7: all partitions complete" message requires knowing that PoRep proofs are split into multiple partitions (10, in this case), each synthesized and proved independently before assembly.
  2. The proof verification pipeline. The "self-check" step is an internal verification where the prover validates its own proof before returning it to the client. This is a common pattern in zero-knowledge proving systems to catch hardware errors (like the GPU race condition that was just fixed).
  3. The grep patterns' significance. The assistant chose patterns that correspond to specific pipeline milestones: enqueue/dequeue for queue latency, Phase 7 for GPU proving completion, self-check PASSED for end-to-end verification, and proof_len=1920 as a discriminator for PoRep proofs specifically.
  4. The remote host configuration. The host at 10.1.16.218 (cs-calib) is a dual-GPU system. The assistant's throughput analysis implicitly evaluates whether both GPUs are now being utilized effectively after the fix.
  5. The recent debugging history. Without knowing about the GPU race condition, the shared mutex hack, the OOM crash on the SnapDeals host, and the gpu_index threading fix, the purpose of this throughput check would be opaque. The message is not just "checking performance" — it is validating that a specific architectural change produced the expected performance improvement.

Output Knowledge Created by This Message

The message produced several forms of knowledge:

  1. Raw throughput data. The grep output shows specific proof completion events with timestamps, job IDs, and timing breakdowns (total_ms, synth_ms, gpu_ms). This raw data can be processed into a throughput rate.
  2. Validation of the multi-GPU fix. The fact that proofs are completing at all (after the earlier failures) confirms that the fix is correct. The volume of completions — multiple proofs in 30 minutes — confirms that both GPUs are being utilized, not just GPU 0 with a serializing mutex.
  3. A baseline for future optimization. The timing breakdowns (synth_ms vs gpu_ms) provide a profile of where time is spent, which can guide future optimization efforts. If synthesis dominates, CPU-side improvements are needed; if GPU time dominates, kernel optimization or memory bandwidth improvements are indicated.
  4. Confidence in the deployment. The user now has evidence that the fix is working under realistic load, not just in a single-shot test. This is the difference between "it compiles and runs once" and "it sustains production throughput."

The Thinking Process Visible in the Reasoning

The assistant's reasoning block reveals a structured analytical process. The first sentence — "I can see the logs showing partitioned PoRep proofs running with 10 partitions each" — indicates that the assistant has already processed the output of the previous command ([msg 421]) and extracted a key structural detail: each proof uses 10 partitions. This is important because partition count directly affects throughput: more partitions mean more GPU work per proof but potentially better GPU utilization through parallel kernel launches.

The second sentence — "Let me get a summary of the throughput - how many proofs are completing and what the timing looks like" — defines the measurement objective. The assistant is not just collecting data; it is formulating a specific question: what is the completion rate, and what is the distribution of completion times?

The third sentence — "I should look for completed proof lines" — identifies the specific log events that will answer the question. This is a classic debugging/performance-analysis pattern: identify the signal events in a noisy log stream and count them over a known time window.

What is notable is what the assistant doesn't do. It doesn't run nvidia-smi to check GPU utilization directly. It doesn't parse the cuzk daemon's internal metrics (if any exist). It doesn't write a script to calculate the exact proofs-per-second rate. Instead, it chooses the simplest possible approach: grep the logs, count the completions, and estimate the rate manually. This is appropriate for an initial assessment. If the throughput were obviously terrible (e.g., 1 proof in 30 minutes), the assistant would know immediately without complex instrumentation. If the throughput looked good, deeper analysis could follow.

The Follow-Up and Resolution

The assistant's next message ([msg 423]) builds on this analysis by running a more targeted query: counting the number of "PoRep proof self-check PASSED" lines in the last 30 minutes and extracting the GPU timing values. The result, summarized in [msg 424], was 20 proofs completed in 30 minutes = approximately 40 proofs per hour, with GPU times ranging from 103 seconds (low contention) to 747 seconds (high parallel load). All 20 proofs passed verification. This was the validation the user needed: the multi-GPU fix was not just correct, but performant.

Conclusion

Message [msg 422] is a small but revealing moment in a complex engineering conversation. It represents the transition from debugging to performance validation — from asking "does it work?" to asking "does it work well?" The assistant's approach to measuring throughput — selecting an appropriate time window, choosing discriminating log patterns, limiting output for manageability — reflects a practical, iterative methodology. The assumptions underlying the approach are reasonable given the context, and the output knowledge created (20 proofs in 30 minutes, all passing) provides concrete evidence that the multi-GPU fix achieved its goal. In the broader arc of the conversation, this message is the point where the team could finally breathe: the race condition was fixed, the GPUs were balanced, and the proving engine was humming along at 40 proofs per hour.