The Pivot Point: When a User Says "Run Microbenchmarks and Maybe Log in Detail"

Subject message: [user] Run microbenchmarks and maybe log in detail ([msg 867])

Introduction

In any optimization campaign, there comes a moment when speculation must yield to measurement. That moment arrived in message [msg 867] of this opencode session, where the user issued a deceptively simple directive: "Run microbenchmarks and maybe log in detail." This two-sentence message, barely six words of instruction, marks a critical pivot in the Phase 4 compute-optimization work on the cuzk SNARK proving engine. It is not merely a request for data — it is a methodological correction, a demand for rigor in the face of confusing results, and a recognition that the assistant had been operating on inference rather than evidence.

To understand the weight of this message, one must appreciate what preceded it. The assistant had just implemented five optimizations from c2-optimization-proposal-4.md — replacing heap-allocated vectors with SmallVec, pre-sizing large allocation vectors, parallelizing CPU-side MSM computations, pinning GPU memory with cudaHostRegister, and tuning per-MSM window sizes. The first end-to-end benchmark on an RTX 5070 Ti with real 32 GiB PoRep data had revealed a stark regression: 106 seconds total versus the 88.9-second Phase 3 baseline ([msg 862]). Synthesis time had risen from 54.7s to 61.6s, and GPU time had ballooned from 34.0s to 44.2s. The assistant was caught off guard.

The Context of Confusion

The assistant's immediate response to the regression reveals a mind trying to reason its way out of uncertainty. In [msg 864], the assistant performed a differential analysis: the 8.6-second gap between the bellperson internal GPU timer (35.6s) and the wrapper timer (44.2s) was attributed to cudaHostRegister overhead — 30 calls pinning ~120 GiB of memory. The synthesis regression was blamed on the A2 pre-sizing change: allocating 328 GiB upfront across 10 parallel circuits would cause a page-fault storm and TLB pressure. The assistant then began reverting A2 at the call sites ([msg 866]).

But this analysis, while plausible, was speculative. The assistant had not actually measured the individual contributions of each optimization. The SmallVec change (A1) was assumed harmless because it only touched stack-allocated data. The parallel B_G2 change (A4) was assumed irrelevant for single-circuit runs. The per-MSM window tuning (D4) was assumed neutral. These were assumptions, not measurements — and the user recognized the gap.

Why This Message Was Written

The user's message is a response to the assistant's speculative analysis and partial reversion. The user is saying, in effect: "Stop guessing which optimization caused the regression. Run proper microbenchmarks that isolate each change, and add detailed logging so we can see exactly where time is being spent."

The motivation is methodological rigor. The assistant had fallen into a common trap in performance engineering: reasoning about performance without data. The user's intervention redirects the session from inference to measurement. The phrase "maybe log in detail" is particularly telling — it suggests the user suspects the current logging is insufficient to diagnose the problem, and that detailed phase-level timing is needed.

This message also reflects a pragmatic engineering judgment. Rather than continuing to revert changes based on hunches, the user wants to build instrumentation that will enable systematic A/B testing. This is the difference between debugging by intuition and debugging by measurement — and the user is insisting on the latter.

The Thinking Process Visible in the Assistant's Response

The assistant's response in [msg 868] shows immediate comprehension and course-correction. The assistant stops the running daemon and memory monitor, then begins adding detailed std::chrono timing instrumentation to the CUDA code. The thinking is explicit: "Let me think about this. The first proof showed a regression, and we need to understand exactly which optimization is helping and which is hurting. Rather than reverting everything, let me set up proper instrumentation to isolate each change."

This is followed by a systematic instrumentation campaign across multiple messages ([msg 870] through [msg 883]). The assistant reads the CUDA source file groth16_cuda.cu and inserts printf("CUZK_TIMING: ...") calls at key phases: pin_abc, prep_msm, b_g2_msm, ntt_msm_h, batch_add, tail_msm, and proof assembly. Each timing point measures a specific phase of the GPU proving pipeline, enabling precise attribution of where time is spent.

The assistant also creates a structured todo list ([msg 869]) with specific test configurations: Test A (baseline with all Phase 4 reverted), Test B (SmallVec only), Test C (SmallVec + Pre-size), and so on. This is the direct operationalization of the user's request — the "microbenchmarks" are now a concrete A/B testing plan.

Input Knowledge Required

To understand this message, one needs substantial context about the cuzk project. The reader must know that this is a SNARK proving engine for Filecoin proof generation, that it uses Groth16 proofs over BLS12-381, that the pipeline involves a synthesis phase (constraint generation on CPU) and a GPU proving phase (MSM and NTT operations). One must understand the Phase 3 architecture: a batch collector that groups multiple sectors' circuits, a bounded channel between synthesis and GPU workers, and the async overlap pipeline.

Critically, one must understand the dependency chain: cuzk-core depends on bellperson (forked), which depends on bellpepper-core (forked) and supraseal-c2 (forked). The Phase 4 optimizations span all three forks, making the regression analysis complex — the regression could originate in any of the three repositories.

The reader must also understand the performance baseline: 88.9s for a single 32 GiB PoRep proof on an RTX 5070 Ti, with 54.7s synthesis and 34.0s GPU time. And the Phase 3 batch-mode result: 55.3s for 20 circuits (two sectors), demonstrating full synthesis amortization.

Output Knowledge Created

This message produces several concrete outcomes. First, it generates a detailed CUDA timing instrumentation that will allow future benchmarks to attribute time to specific GPU phases: prep_msm, NTT+MSM_H, batch addition, tail MSMs, B_G2, proof assembly, and pin/unpin overhead. This instrumentation is the foundation for all subsequent A/B testing.

Second, it establishes a structured testing methodology: a series of test configurations (A through E) that isolate individual optimizations. This enables the assistant to determine which changes are beneficial, which are harmful, and which are neutral.

Third, it creates a documented theory of the regression — the assistant's analysis of the 328 GiB upfront allocation causing page-fault storms, and the 30 cudaHostRegister calls causing pinning overhead — which can now be verified or refuted by measurement.

Assumptions and Potential Mistakes

The user's message makes an implicit assumption: that the assistant has the tools and capability to run microbenchmarks and add detailed logging. This is correct — the assistant can edit CUDA source files, rebuild with cargo build --features cuda-supraseal, and run the benchmark daemon.

However, the message also assumes that the regression is systematic and measurable — that running more benchmarks with better instrumentation will yield actionable data. This is a reasonable assumption, but it's worth noting that the regression could have been caused by environmental factors (system load, thermal throttling, memory pressure from other processes) rather than the code changes themselves. The microbenchmarks would need to control for these factors.

The assistant's initial analysis in [msg 864] contains a potential mistake: attributing the entire 8.6-second GPU timer gap to cudaHostRegister overhead. While pinning 120 GiB of memory is indeed expensive, the gap could also include other overheads like the new per-MSM window objects (D4) or changes in GPU kernel launch patterns. The instrumentation will resolve this ambiguity.

Conclusion

Message [msg 867] is a methodological turning point in the cuzk optimization campaign. The user's simple request — "Run microbenchmarks and maybe log in detail" — redirects the session from speculation to measurement, from inference to evidence. It is a reminder that in performance engineering, data must precede conclusions. The assistant's response — stopping the daemon, adding detailed CUDA timing instrumentation, and planning a systematic A/B testing campaign — demonstrates the correct execution of this principle. The regression that triggered this pivot remains unresolved at this point in the conversation, but the foundation for resolving it has been laid: precise instrumentation, structured testing, and a commitment to letting the data speak.