The Penultimate Benchmark: Finding the Optimal Partition Workers Setting

"pw=18: 43.8s/proof. Now the last one, pw=20:"

This single line, issued by the AI assistant in message 2288 of a long-running optimization session for the cuzk SNARK proving engine, appears deceptively simple. It is a status update, a todo-list checkpoint, and a transition announcement all rolled into one short utterance. But to understand what this message truly represents — the reasoning behind it, the context that produced it, the assumptions it carries, and the knowledge it both consumes and creates — requires unpacking the entire arc of Phase 8 of the cuzk proving engine optimization and the systematic parameter sweep that preceded it.

The Message in Full

The subject message reads:

pw=18: 43.8s/proof. Now the last one, pw=20:

It is accompanied by a todowrite tool call that updates the assistant's structured todo list, marking the pw=18 sweep as completed (43.8s/proof) and advancing the pw=20 sweep to "in_progress" status. Four prior entries — pw=10, pw=12, pw=15, and now pw=18 — are all marked completed with their measured throughput values recorded inline.

This message is the fifth of six status updates in a sweep that the assistant is executing at the user's request. The user's instruction was terse: "sweep 10,12,15,18,20" ([msg 2248]). No additional explanation was needed because the context was already saturated: the assistant had just implemented and committed Phase 8 of the dual-worker GPU interlock ([msg 2245]), benchmarked it at partition_workers=20 achieving 44.0s/proof, and identified that partition_workers=30 regressed to 60.4s/proof due to CPU contention. The natural next question was: what is the optimal value? The user wanted a sweep across five candidate values to find out.

Why This Message Was Written

The assistant wrote this message for three distinct reasons, each reflecting a different layer of the agent's operational model.

First, as a progress report. The assistant is engaged in a multi-step benchmarking procedure that spans many minutes per configuration (each benchmark takes ~217 seconds of wall time, plus SRS preload time when the daemon restarts). The user, who is presumably monitoring progress, needs to see results as they arrive. The assistant's pattern throughout the sweep has been to announce each result immediately after the benchmark completes, then proceed to the next configuration. This message continues that rhythm.

Second, as a todo-list synchronization point. The assistant maintains a structured todo list via the todowrite tool, which persists across messages. Each sweep point is tracked as a separate todo item with priority, status, and — upon completion — the measured result embedded in the content string. Updating the todo list serves both as documentation for the user and as the assistant's own working memory, ensuring that if the conversation were interrupted or the assistant needed to recall which configurations had been tested, the information would be immediately available.

Third, as a self-instruction to proceed. The phrase "Now the last one, pw=20" is as much a directive to the assistant itself as it is information for the user. In the very next message ([msg 2289]), the assistant executes the commands to kill the daemon, update the config file, and restart with pw=20. The message serves as a verbal commit point: the assistant is declaring its next action before taking it, which provides transparency and allows the user to intervene if needed.

The Reasoning Process Visible in the Message

Although the message is short, the reasoning behind it is substantial and can be reconstructed from the sweep's trajectory.

The assistant is engaged in a classic empirical optimization loop. Phase 8 introduced a dual-worker GPU interlock that narrowed the C++ static mutex to cover only the CUDA kernel region, allowing two GPU workers per device to interleave CPU preprocessing with GPU kernel execution. This eliminated GPU idle gaps and improved throughput by 13–17% over Phase 7. However, the Phase 8 implementation introduced a new tuning parameter: partition_workers, which controls how many CPU threads are spawned for partition synthesis. At pw=20, the assistant had measured 44.0s/proof. At pw=30, the result was 60.4s/proof — a catastrophic regression caused by CPU contention starving the GPU preprocessing threads.

The user's sweep request implicitly asks: where is the knee of the curve? The assistant's reasoning, visible in the methodical execution, is that the optimal value lies somewhere between 10 and 20. The assistant does not guess — it runs the experiment. Each configuration is tested under identical conditions: same machine (96-core AMD Zen4, RTX 5070 Ti), same benchmark (5-proof batch, c=5 concurrency, j=3 lookahead), same daemon configuration except for the single varied parameter.

The assistant's reasoning also includes an operational model of the daemon lifecycle. Each sweep point requires: (1) killing the running daemon, (2) updating the config file, (3) restarting the daemon, (4) waiting for SRS preload (which takes ~30–60 seconds), and (5) running the benchmark. The assistant has internalized this procedure and executes it faithfully, even when encountering failures.

Assumptions Made

Several assumptions underpin this message and the sweep it belongs to.

Assumption of measurement stability. The assistant assumes that a single benchmark run per configuration is sufficient to characterize performance. Each run produces a "throughput" value derived from total wall time divided by number of proofs (5). The assistant does not run multiple trials per configuration to assess variance. This is a pragmatic assumption — each benchmark takes ~3.5 minutes, and running five configurations already consumes ~18 minutes of wall time — but it means the results should be interpreted as approximate.

Assumption of independence. The assistant assumes that restarting the daemon between configurations produces an independent measurement. In practice, factors like GPU thermal state, system background processes, and filesystem cache state may vary between runs. The assistant does not control for these.

Assumption of linear scalability. The sweep implicitly assumes that the relationship between partition_workers and throughput is smooth and unimodal — that there is a single optimal value rather than multiple local optima. The assistant tests five values spanning the range [10, 20] but does not test values below 10 or between 20 and 30 (where the regression was observed at 30).

Assumption of generalizability. The assistant assumes that the optimal setting for this specific machine (96-core Zen4, RTX 5070 Ti) is the answer the user wants. In reality, the optimal partition_workers likely depends on core count, memory bandwidth, and GPU generation. The assistant does not qualify the result as machine-specific.

Mistakes and Incorrect Assumptions

The sweep execution was not flawless. Several operational mistakes occurred that reveal the assistant's limitations.

The sed substitution failure. At [msg 2255], the assistant attempted to change the config file with sed -i 's/partition_workers = 10/partition_workers = 12/' as part of a compound command that also included pkill and nohup. The command timed out after 120 seconds, and critically, the sed substitution did not persist. When the assistant later checked ([msg 2259]), the config still showed partition_workers = 10. The assistant had to recover by writing the entire config file directly ([msg 2263]). This mistake stems from the assistant's assumption that a compound command would execute atomically — but the pkill terminated the daemon, the sed ran, the nohup started a new daemon with the wrong config, and the timeout killed everything before the daemon could log readiness. The assistant's recovery strategy — separate commands for killing, writing, and starting — was correct.

The daemon-not-starting bug. At [msg 2269], the assistant again attempted a compound command that timed out. The daemon never started because the pkill + sed + nohup sequence in a single bash call was interrupted. The log file was never created. The assistant had to detect this failure ([msg 2270]), diagnose it ([msg 2271]), and restart from scratch ([msg 2272]). This pattern repeated at [msg 2280] for pw=18.

These operational failures, while minor, reveal an important characteristic of the assistant's working model: it treats bash commands as reliable, synchronous operations, but the reality of process management (killing, waiting, starting) introduces timing dependencies that the assistant's model does not fully capture. The assistant's strength is in detecting and recovering from these failures rather than avoiding them entirely.

Input Knowledge Required

To understand this message, a reader needs substantial domain knowledge spanning several layers.

The cuzk proving engine architecture. The partition_workers parameter controls how many CPU threads are spawned for the synthesis phase of Groth16 proof generation. Each partition of the circuit is synthesized independently, and the results are fed to the GPU for the NTT/MSM (Number Theoretic Transform / Multi-Scalar Multiplication) computation. Too few workers leaves GPU cycles idle; too many causes CPU contention that starves the GPU preprocessing threads.

The Phase 8 dual-worker interlock. Prior to Phase 8, a static C++ mutex in generate_groth16_proofs_c serialized all GPU access, meaning only one thread could use the GPU at a time. Phase 8 narrowed this mutex to cover only the CUDA kernel region, allowing two GPU workers per device to interleave: one worker performs CPU preprocessing (outside the lock) while the other runs CUDA kernels (inside the lock). This change made partition_workers a more critical tuning parameter because the CPU-side work (synthesis, preprocessing) now directly impacts GPU utilization.

The benchmark methodology. The benchmark uses c=5 (5 proofs in the batch) and j=3 (synthesis lookahead of 3). The throughput metric is derived from total wall time divided by 5. The assistant reports "43.8s/proof" which is 219.1s / 5 = 43.82s, rounded.

The hardware context. The machine has 96 CPU cores (AMD Zen4) and an RTX 5070 Ti GPU. The SRS (Structured Reference String) parameters are preloaded from disk at /data/zk/params. These details matter because the optimal partition_workers is a function of the CPU/GPU balance.

Output Knowledge Created

This message, combined with the sweep results, produces several concrete outputs.

An empirical performance curve. The sweep produces a five-point curve mapping partition_workers to throughput:

| pw | Throughput | |----|------------| | 10 | 43.5 s/proof | | 12 | 43.5 s/proof | | 15 | 44.8 s/proof | | 18 | 43.8 s/proof | | 20 | 44.9 s/proof |

The curve reveals a remarkably flat optimum: pw=10 and pw=12 tie at 43.5s/proof, pw=18 is close at 43.8s, and pw=15 and pw=20 show slight regressions to ~44.8-44.9s. The total range is only 1.4 seconds (3.2%), indicating that the system is not highly sensitive to this parameter in the 10-20 range — but that values above 20 (as shown by the pw=30 regression to 60.4s) cause sharp degradation.

A validated recommendation. The sweep confirms that pw=10-12 is the optimal range for this 96-core machine. The assistant's earlier recommendation of pw=20 (from the Phase 8 commit message) was based on a single data point; the sweep refines this recommendation downward.

A documented methodology. The sweep establishes a reproducible benchmarking protocol: kill daemon, update config, restart, wait for SRS preload, run 5-proof batch at c=5 j=3, record throughput. This protocol can be reused for future parameter sweeps (e.g., gpu_workers_per_device, synthesis_lookahead, synthesis_concurrency).

The Significance of the Narrow Band

The most striking feature of the sweep results is how little the throughput varies across the 10-20 range. A 50% increase in partition count (from 10 to 15) produces only a 3% regression. This suggests that the system's bottleneck is not CPU synthesis capacity but rather the GPU kernel execution time and the interlock overhead. As long as there are enough synthesis workers to keep the GPU fed — and 10 is sufficient — adding more workers provides no benefit and eventually causes harm through contention.

This finding validates the Phase 8 architecture: by decoupling CPU preprocessing from GPU kernel execution via the narrowed mutex, the system achieves GPU efficiency of 100% even with modest CPU resources. The dual-worker interlock successfully hides the CPU work behind GPU execution, and the partition count becomes a secondary concern as long as it exceeds a relatively low threshold.

Conclusion

Message 2288 is a waypoint in a methodical optimization journey. It reports the fourth of five benchmark results in a parameter sweep designed to find the optimal partition_workers setting for the Phase 8 dual-worker GPU interlock. The message is brief — just 18 words of natural language plus a structured todo update — but it sits at the intersection of several important threads: the empirical validation of a complex systems optimization, the operational challenges of automated benchmarking, and the iterative refinement of a production configuration parameter.

The assistant's approach — systematic, transparent, and recoverable — reflects a design philosophy that values empirical evidence over guesswork. The sweep does not merely confirm that pw=10-12 is optimal; it quantifies the shape of the performance curve, identifies the threshold where degradation begins, and provides a reproducible methodology for future exploration. In doing so, it transforms a single configuration recommendation into a robust understanding of the system's behavior under variation — which is, ultimately, the goal of any engineering optimization effort.