Reading the GPU's Pulse: A Timing Diagnostic in the cuzk Proving Engine

In the midst of an intensive optimization campaign for Filecoin's Groth16 proof generation pipeline, a single bash command speaks volumes. Message 1092 of the opencode session is deceptively simple — the assistant runs grep 'CUZK_TIMING' /tmp/cuzk-phase4-final.log | tail -5 and receives five lines of GPU timing data:

CUZK_TIMING: prep_msm_ms=1786
CUZK_TIMING: gpu_tid=0 ntt_msm_h_ms=22764
CUZK_TIMING: gpu_tid=0 batch_add_ms=1435
CUZK_TIMING: b_g2_msm_ms=23450 num_circuits=10
CUZK_TIMING: gpu_tid=0 tail_msm_ms=1260 gpu_total_ms=25460

This is not a casual peek at logs. It is a deliberate diagnostic act, performed at a critical juncture in a multi-week effort to understand, optimize, and ultimately re-architect a proving pipeline that consumes ~200 GiB of memory and takes over 90 seconds per proof. The message sits at the intersection of three converging threads: the aftermath of a failed optimization (the SmallVec revert), the validation of a successful one (parallel B_G2 MSM), and the anticipation of the user's next question about RAM prefetch and instruction-level parallelism in synthesis. To understand why this particular grep matters, we must trace the reasoning that led to it.

The Context: Phase 4 Optimization and the SmallVec Lesson

The conversation leading up to message 1092 is a case study in data-driven optimization. The assistant and user have been working through "Phase 4" of the cuzk proving engine optimization plan, which targets compute-level improvements in both CPU synthesis and GPU proving. Four optimizations were implemented in parallel: A1 (SmallVec for LinearCombination), A2 (pre-sizing for ProvingAssignment), A4 (parallelizing the B_G2 MSM on CPU), and D4 (per-MSM window tuning). But when benchmarked end-to-end, the results were puzzling — the pipeline was slower than the Phase 3 baseline.

A meticulous diagnostic process followed. The assistant reverted A2 and B1 (another optimization) and built a synth-only microbenchmark to isolate the CPU synthesis path from GPU variability. This microbenchmark revealed that A1 — the SmallVec optimization — was causing a 5–6 second regression in synthesis time. A deeper perf stat analysis compared SmallVec (capacity=2) against the original Vec implementation across four runs. The data was counterintuitive: SmallVec reduced cache misses at every level (L2, L3, DRAM) and executed 7% fewer instructions, yet it was 1.8% slower in wall-clock time. The culprit was instruction-level parallelism (IPC), which dropped from 2.60 to 2.38 — an 8.5% regression. SmallVec's enum discriminant checks and size-dependent branching created a more complex instruction stream that Zen4's out-of-order execution engine could not pipeline as efficiently as Vec's simple pointer-chasing code. The assistant concluded: "SmallVec is the wrong optimization for this workload. A1 is definitively cancelled."

This was a pivotal moment. It demonstrated that cache misses, while intuitively bad, are not the sole determinant of performance on modern microarchitectures. The simpler code path of Vec allowed the CPU to overlap memory latency with useful work, achieving higher IPC that more than compensated for the extra cache traffic. The lesson shaped everything that followed.

The Benchmark Run: Validating the Clean Baseline

With A1 reverted, the assistant rebuilt the daemon and prepared for a clean E2E benchmark — this time with only A4 (parallel B_G2 MSM) and D4 (per-MSM window tuning) active, plus the max_num_circuits=30 parameter that had been set earlier. The user interjected with a question about RAM prefetch and instruction-level parallelism tricks for synthesis (message 1084), but the assistant deferred the answer, saying "let me think about that while the daemon starts up."

Three consecutive PoRep C2 proofs were submitted to the daemon. The results were stable:

• Run 1: 95.3s total (57.1s synth, 37.9s GPU)
• Run 2: 93.0s total (55.6s synth, 37.1s GPU)
• Run 3: 93.5s total (56.0s synth, 37.3s GPU) The synthesis time had returned to the ~55–56s range (consistent with the pre-SmallVec baseline), and the GPU time was hovering around 37s. But the assistant knew that the "GPU time" reported by the benchmark included queue wait and data transfer overhead — it was not the same as the GPU's active compute time. To get the true GPU compute breakdown, they needed the internal CUZK_TIMING instrumentation that had been added during Phase 4 development.

Message 1092: The Diagnostic Grep

This is why message 1092 exists. The assistant runs grep 'CUZK_TIMING' /tmp/cuzk-phase4-final.log | tail -5 to extract the most recent GPU timing lines from the daemon's log file. The tail -5 is important — the daemon has been running for multiple proofs, and the assistant wants the timing from the last proof (run 3), which will be at the end of the log.

The output reveals the internal GPU pipeline breakdown:

• prep_msm_ms=1786: Preparation for the multi-scalar multiplication (MSM) took 1.79 seconds. This includes setting up device buffers, loading SRS data, and initializing the MSM context.
• ntt_msm_h_ms=22764: The Number Theoretic Transform (NTT) and MSM on the H field took 22.76 seconds. This is the main GPU computation for the Groth16 proof, where the bulk of the elliptic curve operations happen.
• batch_add_ms=1435: Batch addition of elliptic curve points took 1.44 seconds. This is a GPU-optimized operation that adds many points in parallel.
• b_g2_msm_ms=23450 num_circuits=10: The B_G2 MSM — which was the target of optimization A4 (parallelization across CPU threads) — took 23.45 seconds for 10 circuits. This is the dominant GPU cost.
• tail_msm_ms=1260: The tail MSM (final aggregation) took 1.26 seconds.
• gpu_total_ms=25460: Total active GPU compute time was 25.46 seconds. The data reveals a striking fact: the GPU's active compute time is only ~25.5 seconds, but the benchmark reported ~37.3 seconds for the "gpu" phase. The difference of nearly 12 seconds represents time the GPU spends waiting — for synthesis to produce the a/b/c vectors, for data transfers from host to device, and for queue synchronization. This is the gap that the async overlap architecture (planned in Segment 10) is designed to close.

What the Data Tells Us About the Optimization State

The CUZK_TIMING output serves multiple purposes. First, it validates that the A4 optimization (parallel B_G2 MSM) is functioning: the b_g2_msm_ms line shows 23.45 seconds for 10 circuits. Without the parallelization, this would likely be higher, though the assistant does not have a direct pre-A4 baseline for this metric. The num_circuits=10 confirms that a single PoRep C2 proof involves 10 distinct circuits — a structural detail of the Filecoin proof architecture.

Second, the data reveals the GPU compute profile: the two dominant costs are B_G2 MSM (23.4s) and NTT+MSM on H (22.8s). These run largely concurrently on the GPU, which is why the total GPU time (25.5s) is less than their sum. The remaining phases (prep, batch_add, tail) are relatively minor, accounting for about 4.5 seconds combined.

Third, the data sets a baseline for further optimization. If the assistant can reduce synthesis time (currently ~55s) or overlap it more effectively with GPU computation, the total proof time could approach the GPU's active compute window of ~25s — a theoretical 3.7x improvement over the current 93s total. This is the vision that drives the ongoing work.

Assumptions and Limitations

The assistant makes several assumptions when interpreting this data. The CUZK_TIMING instrumentation is assumed to be accurate — that the GPU timers are correctly capturing kernel execution time without including PCIe transfer overhead or driver queueing delay. The num_circuits=10 value is taken as a constant for PoRep C2 proofs, but it could vary with sector size or proof parameters. The assistant also assumes that the tail -5 command captures the correct timing lines — that no other log output has interleaved with the CUZK_TIMING lines in a way that shifts the window.

There is also a subtle assumption about the relationship between the benchmark's "gpu" timing and the CUZK_TIMING "gpu_total_ms". The assistant does not comment on the ~12 second gap, but the data is clearly being gathered to inform the user's question about synthesis optimizations. The gap represents the overhead of the current synchronous pipeline — synthesis must complete before GPU proving can begin, and data must be transferred across the PCIe bus. Understanding this gap is essential for designing the async overlap architecture that could hide synthesis latency behind GPU computation.

The Unanswered Question

Message 1092 is also notable for what it doesn't contain. The user asked about RAM prefetch and instruction-level parallelism tricks in synthesis (message 1084), and the assistant promised to think about it. But the assistant then became absorbed in the E2E benchmarking process — running three proofs, checking GPU timing, and gathering data. The CUZK_TIMING grep is a preparatory step for that answer, not the answer itself. The assistant is arming themselves with data before responding to a complex architectural question.

This pattern — deferring a thoughtful answer to gather more data — is characteristic of rigorous engineering. Rather than speculating about prefetch strategies, the assistant first establishes the current baseline, validates the optimization state, and then will use that foundation to reason about the next steps. The user's question about RAM prefetch is particularly relevant because the perf stat analysis had just revealed that IPC, not cache misses, was the binding constraint on Zen4. Any prefetch strategy would need to improve instruction throughput, not just reduce cache misses.

Conclusion

Message 1092 is a small but revealing moment in a large optimization effort. A single grep command extracts the GPU's internal timing signature, providing the empirical foundation for the next round of decisions. The data confirms that the GPU compute pipeline is efficient (~25.5s active time) but that the overall proof time (~93s) is dominated by synthesis and synchronization overhead. The gap between these numbers defines the optimization frontier.

For the reader unfamiliar with the conversation, this message illustrates the iterative, data-driven nature of systems optimization: formulate a hypothesis, implement a change, measure the result, diagnose the unexpected, revert the failures, and gather the next layer of data. The CUZK_TIMING lines are not just numbers — they are the pulse of a complex distributed system, and the assistant is listening carefully before making the next move.