The Systematic Sweep: Finding the Optimal partition_workers Setting for Phase 8's Dual-Worker GPU Interlock

A Single Benchmark in a Methodical Optimization Campaign

In the course of optimizing a high-performance Groth16 proof generation pipeline for Filecoin's Proof of Replication (PoRep) protocol, a single benchmark command executed at message index 2253 represents far more than a routine performance test. It is the first carefully calibrated data point in a systematic sweep across five candidate configurations, the culmination of a multi-phase engineering effort spanning C++ CUDA kernel refactoring, Rust FFI plumbing, and concurrent architecture design. This message—a bash invocation of the cuzk-bench tool with partition_workers=10—captures the precise moment when theory meets measurement, when architectural decisions are validated or refuted by empirical data.

The Message in Full

The assistant's message consists of a single bash command and its output:

[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 — 67.6s (prove=67043 ms, queue=260 ms)
  [2/5] COMPLETED — 105.4s (prove=67451 ms, queue=701 ms)
  [3/5] COMPLETED — 145.1s (prove=75177 ms, queue=1142 ms)
  [4/5] COMPLETED — 114.2s (prove=72003 ms, queue=293 ms)
  [5/5] COMPLETED — 112.0s (prove=68254 ms, queue=296 ms)

=== Batch Summary ===
total time:    217.5s
completed:     5
failed:        0
wall time:     avg=108.9s min=67.6s max=145.1s
prove time:    avg=70.0s min=67.0s max=75.2s
throughput:  ...

The output is truncated by tail -15, but the visible portion tells a rich story. Five proofs were executed with a concurrency of 3 (-j 3), meaning up to three proofs were in flight simultaneously. The individual proof times range from 67.0 to 75.2 seconds of actual prove time, with queue times (time spent waiting for an available worker slot) ranging from 260ms to 1142ms. The total wall time was 217.5 seconds, yielding an average throughput of 43.5 seconds per proof—a figure that would prove to be the best in the entire sweep.

WHY: The Motivation and Reasoning Behind the Sweep

To understand why this particular benchmark was run, one must trace the engineering narrative that led to it. The message is the first execution of a user request at message 2248: "sweep 10,12,15,18,20." This request came immediately after the assistant had committed Phase 8—the dual-worker GPU interlock—and reported its benchmark results.

The Phase 8 implementation ([msg 2245]) had already demonstrated impressive gains: a 13.2% throughput improvement over Phase 7 at partition_workers=20 (44.0s/proof vs 50.7s/proof), and a 17.2% improvement at lower concurrency. However, the assistant had only tested two partition_workers values: 20 (which worked well) and 30 (which regressed badly to 60.4s/proof due to CPU contention). The user, displaying sound experimental instincts, wanted a more thorough exploration of the parameter space. Why stop at 20? Perhaps lower values like 10 or 12 could squeeze out additional performance by reducing synthesis contention even further.

The assistant's response to the user's request reveals the methodology. In message 2249, the assistant created a todo list with five entries—one for each sweep point—and set pw=10 as "in_progress." Then in messages 2250-2252, the assistant: (1) killed any running daemon, (2) wrote a fresh configuration file with partition_workers = 10 using a heredoc, (3) launched the daemon in the background with nohup, and (4) waited for the SRS preload to complete before proceeding. This careful orchestration ensured that each benchmark would run against a clean, properly initialized daemon with no residual state from previous runs.

The motivation, therefore, is twofold. First, there is the immediate goal: satisfy the user's request for a sweep and produce the data they asked for. Second, there is the deeper engineering goal: empirically determine the optimal partition_workers setting for the Phase 8 architecture on this specific hardware (a 96-core AMD Zen4 machine with an RTX 5070 Ti GPU). The assistant had already hypothesized that partition_workers=20 was the sweet spot based on the pw=30 regression, but the user's sweep would test whether even lower values could improve throughput by reducing CPU contention among synthesis workers.## HOW: The Methodology of a Controlled Benchmark

The assistant's approach to this sweep reveals a disciplined experimental methodology. Each of the five sweep points (10, 12, 15, 18, 20) would be tested under identical conditions: the same C1 input file (/data/32gbench/c1.json), the same concurrency setting (-c 5 -j 3, meaning five proofs with three concurrent slots), the same daemon configuration except for the partition_workers value, and a freshly restarted daemon with a completed SRS preload. The use of nohup and log redirection ensured that the daemon would survive terminal disconnection and that logs would be preserved for post-hoc analysis.

The benchmark itself—cuzk-bench batch -t porep—is a purpose-built tool that submits proof requests to the daemon's HTTP API and reports completion times. The tail -15 filter in the command is a pragmatic choice: the full output would include verbose progress lines for each of the 10 partitions per proof, but the assistant only needs the summary statistics. The key metric is the throughput figure reported at the bottom, which for pw=10 would be 43.5s/proof—a result that would tie with pw=12 as the best in the entire sweep.

Assumptions Embedded in the Benchmark

Several assumptions underpin this test. The most critical is that the C1 input file is representative of production workloads. The file /data/32gbench/c1.json is a 32 GiB sector C1 output—a standard size for Filecoin's Proof of Replication. The assistant assumes that performance characteristics observed with this single input generalize to other sectors of the same size.

Another assumption is that five proofs provide a statistically meaningful sample. With only five measurements, individual outliers (like the 145.1s wall time for proof 3) can skew averages. The assistant implicitly trusts that the batch benchmark's averaging smooths out transient effects. The prove times are more stable (67.0-75.2s range) than wall times (67.6-145.1s), suggesting that queue scheduling effects dominate the wall time variance while the actual proof computation is consistent.

A third assumption is that the daemon's SRS preload is complete before benchmarking begins. The assistant explicitly waits for the "cuzk-daemon ready" log line before proceeding, ensuring that SRS parameter loading—a known expensive operation—does not contaminate the measurements.

Mistakes and Incorrect Assumptions

The benchmark run at message 2253 proceeded without visible errors, but the broader sweep encountered operational hiccups. As noted in the chunk summary, the assistant later faced "command timeouts and a sed substitution that failed to persist," requiring debugging and recovery by manually rewriting the config file and restarting the daemon in separate, robust steps. These issues did not affect the pw=10 data point specifically, but they highlight the fragility of the testing infrastructure.

A more subtle issue is the assumption that partition_workers is the only significant variable. The sweep holds gpu_workers_per_device=2 constant, but the optimal partition_workers value may interact with the number of GPU workers. A machine with different CPU/GPU ratios might show a different optimum. The assistant's conclusion that pw=10-12 is optimal is therefore specific to this 96-core, single-GPU configuration.

Input Knowledge Required

To fully understand this message, one needs substantial context about the cuzk proving engine. The partition_workers parameter controls how many Groth16 partitions are synthesized concurrently. Each partition corresponds to one of the 10 circuit partitions in the PoRep C2 proof, and synthesis involves CPU-intensive polynomial arithmetic. Too few workers underutilizes the CPU; too many causes contention that starves the GPU preprocessing threads.

The -c 5 -j 3 flags mean five proofs total with three concurrent slots. The daemon manages a queue of proof requests, and -j 3 limits how many are in flight simultaneously. The queue times (260-1142ms) indicate that proofs occasionally wait for a slot to open up, which is expected with concurrency-limited benchmarking.

The C1 file is the output of the first phase of proof generation (circuit-specific preprocessing). It contains the assignment vectors and circuit structure needed for the C2 phase (Groth16 proof proper). The daemon loads this JSON and dispatches partition work to GPU workers.

Output Knowledge Created

This single message produced a crucial data point: at partition_workers=10, the Phase 8 dual-worker GPU interlock achieves 43.5s/proof throughput. This would later be confirmed as the joint-best result (alongside pw=12) in the five-point sweep. The data validates the hypothesis that reducing partition workers below 20 can improve performance by reducing CPU contention, but only to a point—pw=15 and pw=18 showed slight regressions (44.8s and 43.8s/proof respectively), while pw=20 regressed further to 44.9s/proof.

The practical output is a clear recommendation: for this hardware configuration, set partition_workers = 10 or 12 for optimal throughput. This is not a trivial finding—it represents the difference between 43.5s and 44.9s per proof, a ~3% improvement that compounds across thousands of proofs in production.

The Thinking Process Revealed

The assistant's reasoning is visible in the sequence of actions leading to this message. Having just committed Phase 8 and demonstrated its effectiveness at pw=20, the assistant could have declared victory. Instead, when the user requested a sweep, the assistant immediately created a structured todo list and began executing methodically. The choice to write a fresh config file via heredoc (rather than using sed on an existing file) proved prescient—later sweep points would encounter sed failures, and the heredoc approach was more reliable.

The assistant also demonstrated awareness of the daemon's lifecycle. Killing the old daemon before starting a new one, waiting for SRS preload, and running the benchmark only after the "ready" signal—these steps show a mature understanding of the system's initialization requirements. The assistant knows that SRS loading is expensive and would distort measurements if included in benchmark time.

Conclusion

Message 2253 is a single benchmark command, but it is also a microcosm of the entire optimization campaign. It represents the intersection of careful engineering (Phase 8's dual-worker interlock), systematic experimentation (the five-point sweep), and empirical validation (measuring rather than guessing the optimal parameter). The result—43.5s/proof at pw=10—would stand as the best throughput achieved in the entire Phase 8 effort, a fitting reward for the discipline of measuring before optimizing.

The broader lesson is that performance engineering is not a single breakthrough but a series of incremental, evidence-based decisions. Phase 8 provided the architectural improvement; the sweep found the tuning that maximized its benefit. Without the sweep, the team might have settled for pw=20 and left a 3% improvement on the table. With it, they have a data-driven answer to the question: "What is the optimal setting?"