The Benchmark That Changed Everything: How Empirical Data Drove a Pipeline Architecture Rethink

In the middle of a deep optimization session for the cuzk SNARK proving engine, a single benchmark run — message <msg id=1978> — delivered a quiet but devastating verdict. The command was unremarkable: a standard invocation of cuzk-bench targeting a running daemon with a specific thread configuration. The output showed five proofs completing in 100.4s, 68.4s, 78.6s, 82.0s, and 77.9s respectively. On its surface, this looked like another data point in a series of experiments. But this particular run was the third in a controlled sequence testing thread isolation strategies, and its results would force a fundamental reconsideration of how the entire proving pipeline should be architected.

The Question Being Asked

To understand why this message matters, we must step back into the reasoning that produced it. The assistant had been wrestling with a structural problem in the cuzk proving engine: the GPU was idle roughly 30% of the time. Earlier benchmarks (see <msg id=1962>) had shown GPU utilization hovering around 70.9% in the baseline configuration. The root cause appeared to be contention on the rayon global thread pool. Both CPU-bound synthesis work and GPU-bound work (specifically the b_g2_msm operation) were competing for the same pool of 192 logical cores. When the GPU kicked off its multi-threaded MSM computation, it would steal threads from ongoing synthesis work, slowing it down. This created a vicious cycle: slower synthesis meant the GPU waited longer for new work, which meant more idle time.

The assistant's hypothesis was clean and testable: isolate the GPU threads from the synthesis threads. By setting CUZK_GPU_THREADS to cap the GPU's thread consumption and using RAYON_NUM_THREADS to limit synthesis to a dedicated subset, the two workloads would stop interfering. The GPU would get its dedicated threads, synthesis would run unimpeded on its own pool, and overall throughput would improve.

The Experimental Sequence

Message <msg id=1978> was the third experiment in this sequence. The first run (message <msg id=1962>) used 64 rayon threads and 32 GPU threads — a severe restriction on synthesis. The results were disappointing: synthesis time ballooned from 39s to 46s because synthesis is highly parallel and needs all available cores. The second run (message <msg id=1971>) tried 96 rayon threads and 32 GPU threads, giving synthesis access to all physical cores but not the hyperthreads. Again, synthesis was slower at 47-48s. The GPU utilization improved marginally to 78.1%, but throughput barely budged from 46.1s/proof to 45.4s/proof.

Message <msg id=1978> represents the third configuration: no limit on rayon (all 192 logical cores), GPU threads capped at 32. The reasoning was that if synthesis got all its cores back, it would return to its baseline 39s speed, and the GPU would still have enough threads for its MSM computation. The benchmark was run with --count 5 --concurrency 2, meaning two proofs were submitted concurrently to the daemon.

The Output: What the Numbers Revealed

The raw output shows:

[1/5] COMPLETED — 100.4s (prove=31982 ms, queue=20529 ms)
[2/5] COMPLETED — 68.4s (prove=29254 ms, queue=258 ms)
[3/5] COMPLETED — 78.6s (prove=27200 ms, queue=2330 ms)
[4/5] COMPLETED — 82.0s (prove=28492 ms, queue=1222 ms)
[5/5] COMPLETED — 77.9s (prove=25865 ms, ...)

The first proof took 100.4s with a 20.5s queue wait — this is the PCE (Prover Cache Engine) preloading penalty, a one-time cost. The subsequent proofs averaged around 77s, with prove times (GPU work) around 26-29s and queue times (waiting for synthesis) around 1-2s. The first proof's queue time is inflated by PCE loading; the later proofs show the steady-state behavior.

But the real story emerged when the assistant parsed the timeline events in the following message (<msg id=1979>). The Python analysis script revealed:

| Job | Synth (ms) | GPU (ms) | Idle Gap (ms) | |-----|-----------|---------|--------------| | P1 | 38,930 | 29,254 | 0 | | P2 | 47,950 | 31,982 | 15 | | P3 | 44,416 | 27,201 | 16,569 | | P4 | ... | ... | ... |

The synthesis times were back in the 39-48s range — good, the cores helped. But the GPU idle gaps were still around 16.5 seconds between proofs. The GPU utilization was approximately 74.8%, barely better than the 70.9% baseline. The throughput worked out to roughly 45-50s per proof in steady state.

The Assumption That Broke

This is where the message becomes pivotal. The assistant had assumed that thread contention was the primary bottleneck. The data proved otherwise. Even with all 192 cores dedicated to synthesis and the GPU capped at 32 threads, the GPU was still idle for 16.5 seconds between each proof. The contention hypothesis was incomplete.

The real culprit, which the user would correct in the following chunk, was architectural. The PoRep C2 pipeline doesn't treat each of its 10 partitions as independent work units that can be streamed to the GPU one-by-one. Instead, all 10 partitions are synthesized in parallel via rayon, finishing in a "thundering herd" around the same time. Only when all 10 are ready does the batch get submitted to the GPU. The GPU then processes them sequentially, taking ~27s total. Meanwhile, the CPU sits idle because all synthesis is already done. The 16.5s idle gap is the time between the GPU finishing one proof's partitions and the next proof's synthesis completing — because synthesis of the next proof can't start until the GPU finishes the current proof's partitions (since they share the same thread pool and memory).

This insight — that the problem is not thread contention but batch-level synchronization — would directly motivate the Phase 7 architecture: per-partition dispatch where individual partitions are synthesized and immediately dispatched to the GPU, enabling cross-sector pipelining.

Input Knowledge Required

To fully understand this message, one needs to know:

• The cuzk proving pipeline architecture: How C1 output feeds into C2 proof generation, the role of the 10 PoRep partitions, and the distinction between synthesis (CPU-bound witness generation and SpMV evaluation) and GPU proving (MSM, NTT, and other elliptic curve operations).
• The thread pool model: How rayon's global thread pool works, how CUZK_GPU_THREADS limits GPU-side thread consumption, and why thread contention between CPU and GPU workloads is a concern.
• The benchmark methodology: What --count 5 --concurrency 2 means (5 proofs submitted in batches of 2 concurrent requests), how the prove vs queue timing breakdown works, and what the TIMELINE events capture.
• The PCE (Prover Cache Engine): Why the first proof has a 20s queue penalty (loading the 25.7 GiB PCE data from disk) and why subsequent proofs don't.

Output Knowledge Created

This message produced several critical pieces of knowledge:

1. Empirical refutation of the thread isolation hypothesis: The data showed that even with ideal thread separation, GPU utilization remained stuck at ~75%. This forced a search for deeper architectural causes.
2. Quantified GPU idle gap: The 16.5s gap between proofs became a concrete optimization target. Any proposal that could reduce or eliminate this gap would directly improve throughput.
3. Validation of synthesis scaling: Synthesis times returned to ~39-48s when given all 192 cores, confirming that synthesis is highly parallel and benefits from full core count. This ruled out CPU power as a limiting factor.
4. Steady-state throughput baseline: The ~77s per proof (excluding first-proof penalty) became the reference point for evaluating future optimizations.
5. Evidence for the "thundering herd" diagnosis: The pattern of all partitions finishing synthesis simultaneously (visible in the timeline data) and then the GPU processing them sequentially became the key observation driving the architectural rethink.

The Thinking Process Visible

The assistant's reasoning is visible in the experimental design. Each configuration tested a different point in the trade-off space:

• Config 1 (rayon=64, gpu=32): Heavy synthesis restriction. Hypothesis: if synthesis uses fewer threads, it leaves more for GPU. Result: synthesis slowed to 46s, negating any benefit.
• Config 2 (rayon=96, gpu=32): Moderate synthesis restriction. Hypothesis: physical cores only, leave hyperthreads for GPU. Result: synthesis still 47-48s. No improvement.
• Config 3 (rayon=192, gpu=32): No synthesis restriction. Hypothesis: give synthesis everything, GPU still gets enough. Result: synthesis back to 39-48s, but GPU idle gap persists. This is textbook scientific method: formulate a hypothesis, design an experiment that isolates the variable, run it, analyze the results, and iterate. The assistant systematically eliminated variables until the true bottleneck became clear.

Why This Message Matters

In the arc of the conversation, message <msg id=1978> is the moment when the thread isolation approach conclusively failed — and in failing, pointed toward the correct solution. The benchmark data showed that even with optimal thread configuration, the fundamental pipeline architecture was the limiting factor. The "thundering herd" of 10 partitions finishing simultaneously and blocking the GPU created an irreducible idle gap.

This realization directly enabled the Phase 7 design that followed. The user's correction about partition timing (each partition takes 32-37s, not 4s) combined with this empirical data to produce a new architecture: per-partition dispatch with cross-sector pipelining. Instead of batching all 10 partitions and submitting them as a unit, each partition would be dispatched to the GPU independently as soon as its synthesis completed. This would eliminate the vertical handoff stall, reduce memory pressure from 10 partitions to 1-2, and naturally pipeline synthesis across sectors.

The benchmark in message <msg id=1978> didn't just measure performance — it revealed the shape of the problem. And in doing so, it set the stage for the most significant architectural change in the entire optimization campaign.