The Systematic Partition Workers Sweep: Finding the Optimal Configuration for Phase 8's Dual-Worker GPU Interlock

Introduction

In the high-stakes world of Filecoin proof generation, every second shaved off proof 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 initial benchmarks were promising. But one critical tuning parameter remained: partition_workers, the number of concurrent partition synthesis workers dispatched 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 chunk documented in this article captures a systematic five-point sweep across partition_workers values of 10, 12, 15, 18, and 20 — an empirical investigation that would define the production configuration for the proving pipeline. Over the course of roughly 45 messages spanning multiple hours of wall time, the assistant methodically benchmarked each configuration, encountered and recovered from operational failures, and ultimately produced a clear, data-driven answer: the optimal setting is 10–12 partition workers, yielding 43.5 seconds per proof — a 13–17% improvement over the Phase 7 baseline.

This article synthesizes the entire sweep, examining the methodology, the operational challenges, the results, and the broader significance of this work within the optimization campaign.

Context: The Phase 8 Foundation

To understand why this sweep was necessary, one must appreciate the engineering context that preceded it. The cuzk proving engine had been through seven prior phases of optimization, each targeting a specific bottleneck in a pipeline that consumes ~200 GiB of peak memory and spans Go, Rust, and CUDA code. Phase 7 had introduced per-partition dispatch, which improved throughput but revealed a critical structural inefficiency: a C++ static mutex in generate_groth16_proofs_c was guarding the entire GPU function, including CPU preprocessing work that did not need exclusive access to the GPU.

Phase 8 solved this by narrowing the mutex scope to cover only the CUDA kernel region (NTT+MSM, batch additions, tail MSMs), while CPU preprocessing and b_g2_msm ran outside the lock. This allowed two GPU workers per device to interleave — one performing CPU preprocessing while the other ran CUDA kernels. The implementation spanned seven files and approximately 195 lines of changes across C++, Rust FFI, and the engine orchestration layer [1].

Initial benchmarks of Phase 8 were striking: single-proof GPU efficiency reached 100.0% (zero idle gaps between partitions), and multi-proof throughput improved by 13.2% (44.0s/proof vs 50.7s for Phase 7 at c=5 j=3) and 17.2% (49.5s vs 59.8s at c=5 j=2) [1]. However, these results were obtained with partition_workers=20, the value that had been optimal in Phase 7. A quick test with partition_workers=30 regressed severely to 60.4s/proof due to CPU contention from 30 simultaneous synthesis workers starving GPU preprocessing threads.

The user, exercising proper scientific skepticism, wanted more data. At message 2248, the user issued a concise directive: "sweep 10,12,15,18,20" [1]. This five-word command launched a systematic parameter exploration that would occupy the next 45 messages of the conversation.

The Sweep Methodology

The sweep protocol, established in the opening message of the chunk ([msg 2251]), followed a careful, repeatable procedure for each configuration point:

Kill the running daemon — terminate any existing cuzk-daemon process to ensure a clean state.
Write a fresh configuration file — a complete TOML file at /tmp/cuzk-sweep.toml with the target partition_workers value, keeping all other parameters constant (gpu_workers_per_device=2, synthesis_lookahead=3, synthesis_concurrency=1, devices=[] for auto-detection).
Start the daemon — launch cuzk-daemon in the background via nohup, redirecting output to a per-configuration log file (e.g., /tmp/cuzk-sweep-pw10.log).
Wait for SRS preload — poll the log file for the "cuzk-daemon ready" message, indicating that the multi-gigabyte Structured Reference String had been loaded into GPU memory.
Run the benchmark — execute cuzk-bench batch -t porep --c1 /data/32gbench/c1.json -c 5 -j 3, the standard throughput benchmark used throughout the optimization campaign.
Extract the throughput — compute seconds per proof from the batch summary.
Record and proceed — update the todo list and move to the next configuration. This protocol ensured that each measurement was taken under identical conditions, with the only variable being the number of partition workers. The use of five proofs with three concurrent jobs (c=5 j=3) stressed the system with enough work to measure steady-state throughput while being short enough to run repeatedly [4].

The Five Data Points

The sweep produced a remarkably tight cluster of results. Here is the complete dataset:

| partition_workers | Throughput (s/proof) | Total Time (s) | |---|---|---| | 10 | 43.5 | 217.5 | | 12 | 43.5 | 217.5 | | 15 | 44.8 | 223.9 | | 18 | 43.8 | 219.1 | | 20 | 44.9 | 224.3 |

pw=10 was the first data point, establishing a baseline of 43.5s/proof [4]. The individual proof times ranged from 67.0s to 75.2s (prove time), with queue wait times of 261–1167ms. The assistant recorded this result and immediately proceeded to pw=12.

pw=12 produced an identical result: 43.5s/proof [16]. The prove times (64.6s to 77.0s) were consistent with the pw=10 run, confirming that the GPU kernel execution time was the bottleneck, not CPU-side partition synthesis. The assistant noted this plateau with the comment "Same as pw=10" and moved on.

pw=15 yielded 44.8s/proof — a slight regression of about 3% [27]. The total time was 223.9s, with individual proof wall times ranging from 74.0s to 153.4s. This was the first indication that increasing partition workers beyond 12 was not beneficial.

pw=18 came in at 43.8s/proof, nearly matching the pw=10/12 results [37]. The total time was 219.1s, with prove times averaging 70.2s. This result was close enough to the best performers that it could be considered within the noise, but it still favored the lower settings.

pw=20, the final data point, produced 44.9s/proof — the worst result in the sweep [43]. The total time was 224.3s, with prove times averaging 71.4s. This confirmed that the earlier assumption of pw=20 being the "sweet spot" was slightly off: pw=20 was actually 1.4s/proof slower than pw=12.

The sweep revealed a narrow performance band with less than 4% variation across the entire range. This itself was a valuable finding: it suggested that the Phase 8 dual-worker interlock was relatively robust to partition count, at least within the 10–20 range. But the data clearly favored the lower end, with pw=10 and pw=12 tied for the best throughput.

Operational Challenges: When Automation Meets Reality

The sweep was not executed flawlessly. Several operational hiccups occurred that reveal the challenges of automated benchmarking in real environments.

The Sed That Didn't Stick. During the transition from pw=10 to pw=12, the assistant attempted to use a compound bash command combining pkill, sed, nohup, and echo in a single invocation ([msg 2255]). 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]) [5]. This was a classic operational hazard: compound commands where one component fails silently, leaving the system in an unexpected state.

The assistant's debugging process was 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]) [7][8][9]. This eliminated the fragility of the sed in-place substitution, which depended on the exact string match being present.

The Silent Timeout. At multiple points during the sweep ([msg 2256], [msg 2269], [msg 2280]), the daemon startup command timed out after 120 seconds. The log file was empty because the daemon had never started — the nohup command was part of a previous timed-out invocation that never completed [6][19][30]. The assistant recovered by checking for the log file, verifying daemon processes, and restarting with separate, individually-timed bash calls.

The Diagnostic Pivot. Each operational failure forced the assistant to pivot into debugging mode. The assistant would check for running processes (pgrep -f cuzk-daemon), inspect the config file (cat /tmp/cuzk-sweep.toml | grep partition_workers), verify the log file exists, and then restart from a known state [20][21][22]. This systematic debugging approach — checking file contents, verifying process state, and decomposing complex commands into simpler steps — was essential to completing the sweep.

The key lesson the assistant learned was to separate concerns: kill the daemon in one tool call, update the config in another, and start the daemon in a third. This pattern, adopted after the pw=10→12 transition failure, was used successfully for all subsequent sweep points [13][14].

Analysis: Why pw=10–12 Is Optimal

The sweep results tell a clear story about the resource dynamics of the cuzk proving engine. The partition_workers parameter controls how many CPU threads are spawned for parallel partition synthesis. Each synthesis thread processes a circuit partition independently, producing intermediate results that are fed to the GPU for the final Groth16 proving steps.

With too few partition workers (below 10), the CPU cannot produce partitions fast enough to keep the GPU busy. The GPU sits idle waiting for work, and throughput suffers. With too many partition workers (above 12), CPU contention begins to starve the GPU preprocessing threads — the threads that prepare data for CUDA kernel execution. These preprocessing threads need CPU time to copy data, format structures, and manage memory, but they compete with the synthesis threads for CPU cores. At pw=20, this contention causes a measurable 3% regression. At pw=30, the effect becomes catastrophic (60.4s/proof, a 37% regression).

The optimal range of 10–12 represents the point where the CPU has enough cores (96 on this AMD Zen4 machine) to handle both synthesis and preprocessing without contention. This is consistent with the hardware: 10–12 synthesis threads plus 2 GPU workers' preprocessing threads plus the daemon's overhead leaves room for OS scheduling without saturating the CPU.

It is worth noting that this result is hardware-dependent. A machine with fewer cores would have a lower sweet spot, while a machine with more cores or faster memory bandwidth might sustain higher partition counts. The sweep's value is precisely that it provides an empirical answer for this specific hardware configuration.

The Broader Significance

This sweep represents the culmination of a multi-phase optimization campaign. Phase 8's dual-worker GPU interlock had already proven its architectural value, but the sweep answered the final practical question: what knob setting should production use? The answer — pw=10–12 — provides a clear, data-driven configuration for operators.

Beyond the specific result, the sweep demonstrates several important engineering principles:

Optimization is empirical, not theoretical. The optimal partition_workers setting could not have been predicted from first principles. It required systematic measurement across multiple values. The assistant's earlier hypothesis that pw=20 was the sweet spot was close but slightly off — the sweep revealed that pw=10–12 was actually better.

Small differences matter. The difference between 43.5s/proof (pw=10) and 44.9s/proof (pw=20) is only 3%, but in a production system generating thousands of proofs daily, that compounds into significant time and cost savings.

Operational robustness is part of engineering. The failures and recoveries around this sweep — timeouts, config persistence bugs, compound command fragility — are as much a part of the story as the benchmark results. Real systems require debugging and resilience.

The right tool for the right question. The user's request "sweep 10,12,15,18,20" was a precise, well-framed question that the assistant could answer definitively. The resulting data provides clear guidance for production configuration.

Conclusion

The partition workers sweep documented in this chunk is a model of systematic performance engineering. Over the course of 45 messages, the assistant executed a controlled experiment across five configuration points, encountered and recovered from operational failures, and produced a clear empirical answer: the optimal partition_workers setting for the Phase 8 dual-worker GPU interlock on this 96-core machine is 10–12, yielding 43.5 seconds per proof.

The sweep confirmed that the Phase 8 architecture is relatively robust to partition count within the 10–20 range, but it clearly identified the optimal operating point. The result — a 13–17% improvement over Phase 7 — provides a solid foundation for production deployment of the cuzk proving engine.

After the final benchmark completed, the assistant killed the daemon and the user sent an empty message — a silent signal that the task was complete [45]. The assistant responded with a comprehensive summary document capturing all findings, design decisions, and suggestions for future work. The sweep was done. The data was clear. And the cuzk proving engine had its optimal configuration.