Validating a Revert: The Final E2E Test After the SmallVec Regression Investigation

In the high-stakes world of zero-knowledge proof generation, every second counts. The cuzk proving engine, a specialized system for Filecoin's Groth16 proofs, had just undergone a meticulous optimization campaign. One change—the SmallVec (A1) optimization in bellpepper-core's LinearCombination representation—had been identified as a subtle performance regression, causing a 5–6 second slowdown in synthesis despite reducing cache misses at every level. The assistant had reverted it. Now came the moment of truth: running the end-to-end (E2E) GPU test to confirm the system was back to baseline.

Message [msg 1090] captures this validation. It is a brief but methodologically rich exchange where the assistant reports the first E2E result, notices an anomaly, investigates, and runs a confirming measurement. On its surface, it is a simple "run test, check result, run again" sequence. But beneath that lies a careful experimental discipline that reveals much about how performance engineering is done in practice.

The Message: Two Runs, One Story

The assistant begins by reporting the first E2E result after the revert:

95.3s total (57.1s synth, 37.9s GPU). The synthesis is a bit high (~57s instead of ~55s). Let me check the daemon log for CUZK_TIMING and run a second proof (first run sometimes includes JIT warmup):

This single sentence contains a wealth of reasoning. The assistant has a mental model of what the baseline should be: approximately 55 seconds for synthesis. The observed 57.1 seconds is close but not quite right—a deviation of about 2 seconds, or roughly 4%. In isolation, this might be dismissed as noise. But the assistant recognizes that the first run of any GPU-accelerated workload on this system may include CUDA JIT compilation overhead, which can inflate timings. Rather than accept the measurement or panic, the assistant does two things: checks the GPU timing instrumentation (CUZK_TIMING) to understand the GPU-side breakdown, and schedules a second run to get a clean measurement.

The CUZK_TIMING output reveals the GPU-side breakdown:

CUZK_TIMING: prep_msm_ms=1843
CUZK_TIMING: gpu_tid=0 ntt_msm_h_ms=23273
CUZK_TIMING: gpu_tid=0 batch_add_ms=1449
CUZK_TIMING: b_g2_msm_ms=23457 num_circuits=10
CUZK_TIMING: gpu_tid=0 tail_msm_ms=1249 gpu_total_ms=25973

This shows the GPU proving time is dominated by NTT/MSM operations (23.3s) and the B_G2 MSM (23.5s), with smaller contributions from prep, batch addition, and tail MSM. The gpu_total_ms=25973 (25.97s) is notably lower than the reported 37.9s GPU time from the first run, suggesting the difference includes overhead not captured by the CUDA instrumentation—possibly host-to-device transfer, synchronization, or the aforementioned JIT warmup.

The second run confirms the suspicion:

93.0s total (55.6s synth, 37.1s GPU)

Synthesis dropped from 57.1s to 55.6s—essentially back to the ~55.5s Vec baseline established in the earlier perf stat measurements ([msg 1078]). GPU time also improved from 37.9s to 37.1s. Total time dropped from 95.3s to 93.0s, a ~2.4% improvement attributable entirely to the warmup effect.

The Thinking Process: Experimental Methodology in Action

What makes this message noteworthy is not the numbers themselves but the process behind them. The assistant demonstrates several principles of rigorous performance measurement:

1. Knowing the baseline. The assistant has internalized the expected synthesis time (~55s) from earlier measurements. Without this reference, 57.1s might have been accepted as "good enough" or, worse, attributed to the revert being incomplete.

2. Understanding system artifacts. The assistant knows that the first GPU run on this system may include CUDA JIT warmup. This is a subtle but critical piece of domain knowledge. Many performance engineers have been misled by cold-start measurements that include compilation overhead.

3. Triangulating with multiple data sources. Rather than relying solely on the end-to-end timing, the assistant checks the CUZK_TIMING instrumentation embedded in the daemon log. This provides a second, independent view of GPU performance that can help isolate where time is being spent.

4. Confirming with replication. The assistant doesn't stop at one measurement. Running a second proof provides replication and helps distinguish signal from noise.

Input Knowledge Required

To fully understand this message, one needs familiarity with several concepts:

• Groth16 proof synthesis: The CPU-bound phase of proof generation where the constraint system is evaluated and the A, B, C vectors are computed. This is the "synth" phase.
• GPU proving: The GPU-accelerated phase where multi-scalar multiplication (MSM) and number-theoretic transform (NTT) operations are performed on the GPU.
• CUDA JIT warmup: The first invocation of a CUDA kernel may trigger just-in-time compilation, adding overhead not present in subsequent runs.
• The SmallVec regression: The earlier investigation (<msg id=1064-1078>) where SmallVec was found to cause an IPC regression from 2.60 to 2.38, making it slower than plain Vec despite reducing cache misses.
• CUZK_TIMING: A custom instrumentation point in the cuzk daemon that logs GPU timing breakdowns.

Output Knowledge Created

This message produces several concrete outputs:

1. Validation that the SmallVec revert is correct. Synthesis time of 55.6s matches the Vec baseline, confirming the revert fully restores performance.
2. A clean E2E timing baseline. The 93.0s total (55.6s synth + 37.1s GPU) becomes the new reference point for future optimizations.
3. GPU timing breakdown. The CUZK_TIMING data provides a detailed picture of where GPU time is spent: ~23.3s on NTT/MSM, ~23.5s on B_G2 MSM, ~1.8s on prep, ~1.4s on batch add, ~1.2s on tail MSM.
4. Confirmation of JIT warmup effect. The ~2s difference between first and second runs quantifies the warmup overhead for this workload.

Broader Significance

This message sits at a pivotal moment in the optimization campaign. The assistant has just completed a deep investigation into why SmallVec regressed (<msg id=1064-1078>), using perf stat to discover that the 8.5% IPC drop from 2.60 to 2.38 was the root cause—not cache misses. The revert was the right call. Now the E2E test confirms the system is healthy and ready for the next phase of optimization.

The user's question in [msg 1084]—"Any ram prefetch (separate thread?) or other cache / instruction parallelism tricks we can do in synth?"—hints that the next phase will explore compute-level optimizations like software prefetching, interleaved evaluation, and allocation reduction. The clean baseline established in this message provides the foundation for those experiments.

Conclusion

Message [msg 1090] is a masterclass in performance validation. It demonstrates that rigorous measurement is not about running a single test and declaring victory, but about understanding the system well enough to recognize when a measurement is contaminated by artifacts, knowing how to isolate those artifacts, and confirming results with replication. The assistant's methodical approach—notice the anomaly, investigate with instrumentation, run a confirming measurement—turns a simple "test passed" moment into a demonstration of experimental discipline that any performance engineer can learn from.