The Final Data Point: Completing the Partition Workers Sweep in Phase 8

Introduction

In the course of optimizing the cuzk SNARK proving engine for Filecoin PoRep, a systematic parameter sweep was undertaken to determine the optimal partition_workers setting for the Phase 8 dual-worker GPU interlock architecture. The message at index 2293 represents the final data point in that sweep — the benchmark of partition_workers=20 — and with it, the completion of an empirical investigation that would define the production configuration for the proving pipeline. While the message itself appears to be a routine benchmark execution, it is in fact the culminating step in a multi-hour optimization process that spanned architecture design, implementation, debugging, and systematic measurement across five configuration points.

Context: The Phase 8 Dual-Worker GPU Interlock

To understand why this message was written, one must first understand what came before it. The cuzk proving engine had been the subject of an intensive optimization campaign spanning multiple phases. Phase 7 introduced per-partition dispatch, which improved throughput but revealed a critical bottleneck: a C++ static mutex in generate_groth16_proofs_c that caused GPU idle gaps. When one GPU worker held the mutex, other workers were forced to wait, leaving the GPU underutilized.

Phase 8 solved this by narrowing the mutex scope to cover only the CUDA kernel region (NTT+MSM operations), while CPU preprocessing and the b_g2_msm computation ran outside the lock. This allowed two GPU workers per device to interleave their work — one worker could perform CPU preprocessing while the other held the mutex for CUDA kernel execution. The implementation spanned seven files and approximately 195 lines of changes across C++, Rust FFI, and the engine orchestration layer.

Initial benchmarks of Phase 8 were promising: single-proof GPU efficiency reached 100.0%, and multi-proof throughput improved by 13-17% over Phase 7. However, a critical question remained unanswered: what was the optimal partition_workers setting? An earlier test with partition_workers=30 had regressed severely to 60.4s/proof, as the high partition count caused CPU contention that starved the GPU preprocessing threads. This established that there was a sweet spot somewhere below 30, but its exact location was unknown.

The User's Request: A Systematic Sweep

The user's request was concise: "sweep 10,12,15,18,20" ([msg 2248]). This directive set in motion a methodical benchmarking campaign. The assistant created a todo list with five items, each representing one configuration point, and began executing them in sequence. The methodology was straightforward: for each partition_workers value, restart the daemon with the updated configuration, wait for the SRS (Structured Reference String) to preload, run a standard benchmark of five proofs at concurrency=5 and jobs=3, and record the throughput.

The sweep was designed to produce a clear empirical answer. The values 10, 12, 15, 18, and 20 were chosen to cover the range between the default (20, which had been used in Phase 7) and the lower bound where diminishing returns might set in. The earlier finding that pw=30 caused CPU contention suggested that the optimal value would be in the teens or low twenties, but the exact shape of the performance curve was unknown.

Message 2293: The Final Benchmark

The subject message executes the benchmark for partition_workers=20:

[assistant] [bash] 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
concurrency: 3

  [1/5] COMPLETED — 115.8s (prove=78249 ms, queue=260 ms)
  [2/5] COMPLETED — 112.3s (prove=67521 ms, queue=715 ms)
  [3/5] COMPLETED — 156.7s (prove=74253 ms, queue=1161 ms)
  [4/5] COMPLETED — 108.4s (prove=72401 ms, queue=267 ms)
  [5/5] COMPLETED — 108.5s (prove=64586 ms, queue=267 ms)

=== Batch Summary ===
total time:    224.3s
completed:     5
failed:        0
wall time:     avg=120.3s min=108.4s max=156.7s
prove time:    avg=71.4s min=64.6s max=78.2s
throughput:...

The throughput line is truncated in the display, but from the total time of 224.3 seconds across 5 proofs, the throughput is approximately 44.9 seconds per proof. This result is slightly worse than the best performers in the sweep (pw=10 and pw=12 at 43.5s/proof) and comparable to pw=15 (44.8s/proof) and pw=18 (43.8s/proof).

The Reasoning Behind the Message

This message was written to complete the sweep. The assistant had already collected data for pw=10 (43.5s/proof), pw=12 (43.5s/proof), pw=15 (44.8s/proof), and pw=18 (43.8s/proof). Only pw=20 remained. The reasoning was that without this final data point, the sweep would be incomplete — the user would not have the full picture of how performance varied across the range. Moreover, pw=20 was the default value used in Phase 7, so establishing its performance under Phase 8 was essential for comparing the two architectures on equal footing.

The assistant's thinking process, visible in the progression of messages, shows a methodical approach. Each sweep point followed the same pattern: kill the daemon, update the config, restart, wait for readiness, run the benchmark, record the result, update the todo list. This consistency was important for ensuring that the results were comparable — any variation in methodology could have introduced confounding factors.

Operational Challenges and Mistakes

The sweep was not without its difficulties. Several operational hiccups occurred that reveal the challenges of automated benchmarking in a real environment. When transitioning from pw=12 to pw=15 ([msg 2269]), the daemon failed to start because the pkill and nohup commands were combined in a single bash invocation that timed out. The assistant had to debug this by checking for the log file, discovering it didn't exist, and restarting the process with separate, robust steps.

Similarly, the sed substitution for updating the config file failed multiple times. In one instance ([msg 2258]), the sed command was part of a compound command that was terminated by a timeout, leaving the config unchanged. The assistant discovered this when checking the config and finding partition_workers = 10 instead of the expected 12. The fix required manually rewriting the entire config file using a heredoc rather than relying on sed.

These operational failures are instructive. They highlight the brittleness of compound bash commands when one component (like pkill) can cause the entire invocation to behave unexpectedly. The assistant's debugging process — checking log files, verifying config contents, and restarting with isolated commands — demonstrates a systematic troubleshooting approach.

Assumptions Embedded in the Benchmark

The benchmark methodology embodied several assumptions worth examining. First, it assumed that five proofs at concurrency=5 and jobs=3 provided a representative sample of throughput. This is a reasonable assumption given that the proving pipeline is deterministic for a given input, but it does not capture long-term effects like memory fragmentation or thermal throttling.

Second, the sweep assumed that the SRS preload time was not a significant factor. The assistant waited for the "cuzk-daemon ready" message before running the benchmark, ensuring that the SRS was fully loaded. This eliminated preload time from the measurement, which is appropriate for a steady-state throughput benchmark.

Third, the benchmark assumed that the daemon's HTTP API introduced negligible overhead. The queue times reported in the individual proof results (ranging from 251ms to 1161ms) suggest that queueing delay was minimal relative to the 64-78 second prove times.

Input Knowledge Required

To understand this message, one needs knowledge of several domains. The concept of partition_workers refers to the number of parallel synthesis threads that process circuit partitions during Groth16 proof generation. In the cuzk engine, the circuit is divided into partitions, each of which can be synthesized independently. More partitions means more parallelism, but also more overhead and potential for CPU contention.

The SRS (Structured Reference String) is a large cryptographic parameter set (multiple gigabytes) that must be loaded into GPU memory before proof generation can begin. The preload step is essential but time-consuming, which is why the assistant waited for it before benchmarking.

The benchmark parameters -c 5 -j 3 specify concurrency (number of concurrent proofs in flight) and jobs (number of parallel proof generation tasks). These parameters interact with partition_workers in complex ways — higher concurrency increases GPU utilization but also increases CPU contention for synthesis threads.

Output Knowledge Created

This message produced the final data point in the sweep, enabling the assistant to draw conclusions about the optimal partition_workers setting. The complete results were:

| partition_workers | 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 data reveals a narrow performance band with pw=10 and pw=12 tied for best at 43.5s/proof, and pw=20 performing worst at 44.9s/proof. This is a remarkably tight spread — less than 4% variation across the entire range — which itself is a valuable finding. It suggests that the Phase 8 dual-worker interlock is relatively robust to partition count, at least within the 10-20 range.

The confirmation that pw=10-12 is optimal validates the earlier hypothesis that higher partition counts cause CPU contention. At pw=20, the additional synthesis threads compete with GPU preprocessing threads for CPU cores, causing the slight regression observed. At pw=30, this effect becomes severe (60.4s/proof), confirming that the performance cliff is somewhere between 20 and 30.

The Broader Significance

This message, while seemingly mundane, represents the completion of a systematic optimization cycle that began with architectural design (Phase 7 per-partition dispatch), continued through implementation and debugging (Phase 8 dual-worker interlock), and culminated in empirical validation (the partition_workers sweep). The sweep results provide a clear, data-driven answer to a question that could not have been answered theoretically: what is the optimal partition count for this specific hardware configuration (96-core machine with dual-GPU workers)?

The answer — pw=10-12 — is not generalizable to all systems, but the methodology is. The sweep demonstrates how to systematically explore a configuration parameter space, handle operational failures gracefully, and extract actionable conclusions from noisy benchmark data. For the cuzk project, this means the production configuration can be set with confidence, and the optimization effort can move on to the next bottleneck.