The 43.5-Second Plateau: A Systematic Partition Workers Sweep in the cuzk SNARK Proving Engine

Introduction

In the high-stakes world of Filecoin Proof-of-Replication (PoRep) proving, every second shaved off proof generation time translates directly into lower operational costs and higher throughput for storage providers. The cuzk SNARK proving engine, a custom GPU-accelerated Groth16 prover, had just received a major architectural upgrade — Phase 8's dual-worker GPU interlock — and the benchmarks were promising. But one critical tuning parameter remained: partition_workers, the number of concurrent partition synthesis workers that the engine would dispatch during proof generation. Too few, and the GPU would sit idle waiting for CPU work. Too many, and CPU contention would starve the preprocessing threads that feed the GPU. Somewhere in between lay the sweet spot.

The message at index 2266 in this conversation captures a single data point in a systematic five-point sweep across partition_workers values of 10, 12, 15, 18, and 20. On its surface, it is a mundane benchmark execution — a bash command piped through tail -15 to capture the summary statistics of a batch of five proofs. But this message is the second data point in a carefully designed experiment, and it reveals as much about the proving engine's behavior as it does about the operational realities of distributed systems benchmarking.

The Message: A Benchmark in Flight

The message is a single tool call — a bash invocation of the cuzk-bench binary, the benchmarking harness for the cuzk proving daemon:

cd /home/theuser/curio/extern/cuzk && ./target/release/cuzk-bench -a "http://127.0.0.1:9820" batch -t porep --c1 /data/32gbench/c1.json -c 5 -j 3 2>&1 | tail -15

The parameters tell us a great deal about the experimental setup. The -a flag points to the daemon's HTTP endpoint at 127.0.0.1:9820. The batch subcommand runs a batch of proofs. The -t porep selects the PoRep circuit type. The --c1 /data/32gbench/c1.json provides the C1 output — the intermediate computation from an earlier phase of the proving pipeline. The -c 5 requests five proofs, and -j 3 sets concurrency to three, meaning up to three proofs are generated simultaneously. The | tail -15 truncates the verbose per-proof progress lines to show only the tail end of the output — the last few completion messages and the summary.

The output shows the five proofs completing with wall times ranging from 81.5s to 147.6s and prove times (the time spent inside the proving engine, excluding queue wait) ranging from 64.6s to 77.0s. The batch summary reports a total wall time of 217.5s, yielding a throughput of 43.5 seconds per proof (217.5s / 5). The average prove time is 70.8s, but the wall time per proof is lower because proofs overlap in the concurrent pipeline.

Context: Why This Message Exists

This message exists because of a chain of reasoning that spans the entire optimization journey of the cuzk engine. The Phase 8 dual-worker GPU interlock, implemented and committed just moments earlier ([msg 2245]), had eliminated GPU idle gaps by narrowing the C++ static mutex in generate_groth16_proofs_c to cover only the CUDA kernel region. This allowed two GPU workers per device to interleave — one performing CPU preprocessing while the other ran CUDA kernels. The initial benchmark at partition_workers=20 showed 44.0s/proof, a 13.2% improvement over Phase 7.

But the user wanted more. They asked to test partition_workers=30 ([msg 2233]), which revealed a regression to 60.4s/proof due to CPU contention. The assistant then committed Phase 8 and summarized the results, noting that pw=20 was "the sweet spot for this 96-core machine" ([msg 2247]). The user responded with a concise directive: "sweep 10,12,15,18,20" ([msg 2248]). This was a request to systematically explore the parameter space around the suspected optimum, to confirm that pw=20 was indeed optimal or to find an even better setting.

The assistant accepted the task and began executing the sweep methodically. Each sweep point required: (1) killing the running daemon, (2) updating the config file with the new partition_workers value, (3) restarting the daemon, (4) waiting for the SRS (Structured Reference String) preload to complete, and (5) running the standard benchmark with c=5 j=3. The first sweep point, pw=10, completed successfully with a result of 43.5s/proof ([msg 2253]).

Then came pw=12 — the subject of this article.

Operational Reality: When sed Betrays You

The path to this message was not smooth. The assistant's first attempt to transition from pw=10 to pw=12 ([msg 2255]) used a compound bash command that combined pkill, sed, nohup, and echo in a single invocation. The command timed out after 120 seconds, and critically, the sed substitution never executed. The config file still read partition_workers = 10 when the assistant checked ([msg 2259]).

This is a classic operational hazard in automated benchmarking: compound commands where one component fails silently, leaving the system in an unexpected state. The pkill command killed the daemon, but the subsequent sed and nohup commands — which should have run after pkill completed — were either swallowed by the timeout or failed because the shell pipeline was interrupted.

The assistant's debugging process is instructive. It checked the log file ([msg 2257]), found it empty, checked the config ([msg 2259]), discovered the sed hadn't taken effect, and then pivoted to a more robust approach: writing the entire config file directly using a heredoc ([msg 2263]). This eliminated the fragility of the sed in-place substitution, which depended on the exact string match being present. The assistant then restarted the daemon in a separate command ([msg 2264]), waited for readiness ([msg 2265]), and finally executed the benchmark that produced the subject message.

This operational hiccup is worth examining because it reveals an assumption the assistant made: that compound bash commands would execute atomically and that pkill would complete before sed ran. In practice, the pkill command may have terminated the shell process itself (if the daemon was a child of the same shell), or the 120-second timeout may have truncated the command before sed executed. The assistant learned from this and adopted a more resilient pattern for subsequent sweep points: kill the daemon in one command, update the config in another, and start the daemon in a third.

Interpreting the Numbers

The pw=12 result of 43.5s/proof is identical to pw=10's result. This is a striking finding. The proving engine's throughput is essentially flat across the range of 10 to 12 partition workers, suggesting that the GPU is fully saturated at this point and that additional CPU parallelism does not translate into faster proof generation. The prove times (64.6s to 77.0s) are consistent with the pw=10 run (67.0s to 75.2s), confirming that the GPU kernel execution time is the bottleneck, not CPU-side partition synthesis.

The wall time variation across individual proofs — from 81.5s to 147.6s — reflects the concurrency effects of running three proofs simultaneously. The first proof (81.5s) benefits from a cold start with no contention, while the third proof (147.6s) includes queue wait time (1167ms) and the overhead of competing with two other proofs for GPU time. This is expected behavior in a concurrent pipeline.

What makes this data point significant is that it establishes a floor. If pw=10 and pw=12 both yield 43.5s/proof, then the optimal partition_workers setting is at most 12. The subsequent sweep points would reveal whether higher values maintain this throughput or regress.

Assumptions Embedded in the Benchmark

The benchmark methodology carries several assumptions worth examining. First, it assumes that a batch of five proofs with concurrency 3 is representative of production workloads. In practice, a proving daemon might handle many more proofs with varying arrival patterns. Second, it assumes that the SRS is preloaded and warm — the daemon was started with preload = ["porep-32g"] in the config, meaning the ~200 GiB SRS was loaded into memory before benchmarking began. This eliminates SRS loading overhead from the measurements, which is appropriate for steady-state throughput but does not reflect cold-start scenarios.

Third, the benchmark assumes that the C1 output file (/data/32gbench/c1.json) is representative of real C1 outputs. If the C1 output has unusual characteristics (e.g., extreme circuit values that cause atypical synthesis times), the benchmark results might not generalize. The assistant did not verify the C1 output's properties, trusting that the test data was representative.

Fourth, the benchmark measures throughput as total wall time divided by proof count, which implicitly assumes that the system can sustain this throughput indefinitely. In practice, memory pressure, GPU temperature, and other factors might cause performance degradation over longer runs.

Input Knowledge Required

To fully understand this message, one needs knowledge of several domains. First, the Groth16 proving pipeline: the distinction between C1 (the intermediate computation from the first phase of proof generation) and C2 (the second phase that produces the final proof), the role of partition synthesis in breaking the circuit into manageable pieces, and the NTT/MSM (Number Theoretic Transform / Multi-Scalar Multiplication) operations that dominate GPU time.

Second, the cuzk architecture: the daemon-worker model, the HTTP API for submitting proof requests, the pipeline configuration with synthesis_lookahead and synthesis_concurrency, and the Phase 8 dual-worker interlock that this sweep is evaluating.

Third, the hardware context: a 96-core AMD Zen4 CPU and an RTX 5070 Ti GPU. The optimal partition_workers value is tightly coupled to the CPU core count — more cores can sustain more concurrent synthesis workers without starving GPU preprocessing. On a machine with fewer cores, the optimal value would likely be lower.

Output Knowledge Created

This message, combined with the other sweep points, creates actionable knowledge about the cuzk engine's tuning. The full sweep results were:

| partition_workers | Throughput (s/proof) | |---|---| | 10 | 43.5 | | 12 | 43.5 | | 15 | 44.8 | | 18 | 43.8 | | 20 | 44.9 |

The data shows that pw=10 and pw=12 are tied for the best throughput, with pw=18 close behind at 43.8s. The higher values (15 and 20) show slight regressions. This confirms that the optimal range is 10–12 for this hardware configuration, and that the earlier assumption of pw=20 being the sweet spot was slightly off — pw=20 is actually 1.4s/proof slower than pw=12.

This knowledge has direct operational value. A production deployment can set partition_workers = 10 or 12 with confidence, knowing that the GPU is fully utilized and that CPU contention is minimized. The 1.4s/proof difference between pw=12 and pw=20, while small, compounds over thousands of proofs.

The Thinking Process Visible

The assistant's reasoning is visible in the sequence of actions. After the pw=10 result of 43.5s/proof, the assistant did not stop to analyze — it immediately proceeded to pw=12. This reveals an expectation that the sweep would show a clear optimum, and that the assistant's job was to collect data, not to interpret it mid-stream. The todo list ([msg 2254]) shows the assistant tracking progress with a systematic checklist, marking pw=10 as completed and pw=12 as in progress.

When the pw=12 result also came back at 43.5s/proof, the assistant's response was minimal: "pw=12: 43.5s/proof. Same as pw=10. Now pw=15:" ([msg 2267]). This terseness is itself revealing — the assistant recognized that two identical data points suggest a plateau, but it did not prematurely conclude the sweep. It continued collecting data for the remaining values, which was the correct scientific approach. Only after all five points were collected could the full pattern emerge.

Conclusion

The message at index 2266 is a single data point in a systematic optimization sweep, but it encapsulates the entire benchmarking methodology: the operational challenges of distributed system testing, the importance of robust automation, the need to explore parameter spaces empirically rather than relying on intuition, and the value of collecting multiple data points to distinguish signal from noise. The 43.5s/proof result for pw=12, identical to pw=10, established a performance floor that the subsequent sweep points would confirm. In the end, the optimal partition_workers setting was found to be 10–12, a result that would not have been discovered without this careful, methodical exploration.