The 44.8-Second Data Point: Finding the Optimal partition_workers Setting Through Systematic Benchmarking

Introduction

In the middle of a methodical performance sweep across five configuration values, message <msg id=2277> captures a single, seemingly mundane benchmark result: partition_workers=15 yielding 44.8 seconds per proof for Filecoin PoRep Groth16 proving on a 96-core AMD Zen4 machine with an RTX 5070 Ti GPU. This message is the third data point in a five-point sweep ordered by the user with the terse command "sweep 10,12,15,18,20" ([msg 2248]). Yet behind this single number lies a rich story of engineering trade-offs, operational debugging, and the painstaking empirical process of tuning a high-performance SNARK proving engine.

Context: The Phase 8 Dual-Worker GPU Interlock

To understand why this message exists, we must understand what came before it. The assistant had just implemented and committed Phase 8: Dual-Worker GPU Interlock ([msg 2245]), a significant architectural change to the cuzk SNARK proving engine. The core insight was that the C++ static mutex in generate_groth16_proofs_c was causing GPU idle gaps: when one worker held the lock to run CUDA kernels, no other worker could perform CPU preprocessing, leaving the GPU underutilized. Phase 8 narrowed the mutex scope to cover only the CUDA kernel region (NTT+MSM, batch additions, tail MSMs), allowing CPU preprocessing and b_g2_msm to run outside the lock. This enabled two GPU workers per device to interleave — one does CPU work while the other runs CUDA kernels.

The initial benchmark results were impressive: single-proof GPU efficiency hit 100.0% (zero idle gaps), and multi-proof throughput improved 13.2% (44.0s/proof vs 50.7s for Phase 7 at c=5 j=3). However, these results used partition_workers=20, and a quick test with partition_workers=30 regressed badly to 60.4s/proof due to CPU contention from 30 simultaneous synthesis workers starving GPU preprocessing threads.

This raised an obvious question: what is the optimal partition_workers setting? The user wanted a systematic answer, not a guess.

The Subject Message: A Benchmark in Action

The message itself is a straightforward benchmark invocation 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 — 153.4s (prove=72248 ms, queue=838 ms) [2/5] COMPLETED — 98.1s (prove=71665 ms, queue=261 ms) [3/5] COMPLETED — 149.8s (prove=71043 ms, queue=723 ms) [4/5] COMPLETED — 92.7s (prove=71902 ms, queue=254 ms) [5/5] COMPLETED — 74.0s (prove=64494 ms, queue=260 ms)

>

=== Batch Summary === total time: 223.9s completed: 5 failed: 0 wall time: avg=113.6s min=74.0s max=153.4s prove time: avg=70.3s min=64.5s max=72.2s throughput: 1...

The benchmark runs 5 proofs (-c 5) with concurrency 3 (-j 3), using the standard PoRep C2 circuit with pre-computed C1 data from /data/32gbench/c1.json. The throughput calculation (which the truncated output doesn't fully show) yields 44.8s/proof — derived from total_time * concurrency / completed = 223.9 * 3 / 5 = 134.34s... wait, that doesn't match. Let me recalculate: the throughput is typically total_time / completed * concurrency or similar. The assistant's next message ([msg 2278]) states "pw=15: 44.8s/proof" so the calculation is consistent with the earlier methodology.

The Reasoning: Why This Message Exists

This message exists because of a deliberate, methodical experimental design. The assistant had already established that partition_workers=20 gave 44.0s/proof and partition_workers=30 gave 60.4s/proof. The user's request to sweep 10, 12, 15, 18, 20 was a request to explore the lower end of the range — specifically to see if reducing partition workers below 20 could improve throughput by reducing CPU contention, or if it would hurt throughput by reducing parallelism.

The assistant's approach was systematic:

1. pw=10: Restart daemon with new config, wait for SRS preload, run benchmark → 43.5s/proof
2. pw=12: Same procedure → 43.5s/proof (tied with pw=10)
3. pw=15: Same procedure → 44.8s/proof (this message)
4. pw=18: Next in sequence
5. pw=20: Final point (already had a baseline from earlier testing) Each data point required: killing the running daemon, updating the config file, restarting the daemon, waiting for the SRS (Structured Reference String) to preload from disk (a ~30-60 second operation), then running the 5-proof benchmark. The entire sweep took roughly 30-40 minutes of wall time.

Operational Challenges and Debugging

What makes this message particularly interesting is what doesn't appear in it — the operational struggles that preceded it. Looking at the context messages leading up to <msg id=2277>, we see a series of failures and recoveries:

• Timeout at pw=12 ([msg 2256]): The daemon startup command timed out after 120 seconds, and the log file was empty. The assistant discovered that the sed substitution to change partition_workers = 10 to partition_workers = 12 had failed because the previous write had already set it to 10 (the sed pattern matched "10" within "12" ambiguously, or the command was part of the terminated batch).
• Config persistence failure ([msg 2259]): After restarting, the assistant checked the config and found partition_workers = 10 still — the sed hadn't persisted. This forced a switch to writing the entire config file directly via echo rather than relying on sed -i.
• Timeout at pw=15 ([msg 2269]): Again the daemon startup timed out, and this time the log file didn't even exist — the entire pkill + nohup chain had been in a single command that got terminated mid-execution.
• Recovery (<msg id=2272-2276>): The assistant recovered by killing any remaining daemon, checking the config (which still showed partition_workers = 12 from the previous partial update), applying sed, and starting the daemon in a separate command with a sleep 5 before checking the log. These operational hiccups reveal an important truth about automated benchmarking in real environments: even seemingly simple operations like "change a config value and restart" can fail in subtle ways. The assistant's ability to detect and recover from these failures — checking log files, verifying config values, retrying with more robust command sequences — was essential to completing the sweep.

The Thinking Process Visible in the Message

The message itself is a tool call — a bash command execution. But the reasoning behind it is visible in the surrounding messages. The assistant is operating with a clear mental model:

1. Each pw value is an independent experiment requiring a clean daemon restart to ensure no state leakage between configurations.
2. The benchmark must be identical across runs — same binary (cuzk-bench), same endpoint (127.0.0.1:9820), same circuit type (porep), same C1 data, same concurrency (-j 3), same proof count (-c 5).
3. The throughput metric is the key comparison point — the assistant immediately computes and reports the per-proof time (44.8s) in the next message ([msg 2278]), updating the todo list with the result.
4. The sweep is sequential, not parallel — each configuration is tested one at a time, ensuring clean isolation. This is the correct approach for benchmarking, as running multiple daemons simultaneously would compete for GPU and CPU resources.

Assumptions and Potential Pitfalls

Several assumptions underpin this benchmark:

• Reproducibility: The assistant assumes that a single 5-proof batch is sufficient to characterize throughput for a given configuration. With only 5 proofs and concurrency 3, the batch completes in ~224 seconds wall time. Longer runs might reveal different behavior, but the assistant is balancing thoroughness against time.
• Warm cache: The benchmark assumes that SRS preloading and daemon initialization are complete before the benchmark starts. The assistant explicitly waits for the "cuzk-daemon ready" log message before proceeding.
• No external interference: The benchmark assumes the machine is otherwise idle. On a 96-core shared machine, other processes could interfere, but the assistant has no way to control for this.
• Linear scaling: The throughput calculation assumes that throughput scales linearly with concurrency, which is approximately true for this system.
• The tail -15 truncation: The assistant uses tail -15 to capture the benchmark output, which cuts off the full throughput line. The next message ([msg 2278]) shows the assistant computed 44.8s/proof from the visible data, but the raw throughput value is lost. This is a minor methodological weakness — capturing the full output would be more robust.

Input Knowledge Required

To fully understand this message, one needs:

1. The Phase 8 architecture: Knowledge that the dual-worker GPU interlock allows two GPU workers to share a device, with one running CUDA kernels while the other does CPU preprocessing.
2. The partition_workers parameter: This controls how many partition synthesis tasks can run concurrently. Each partition requires significant CPU and memory resources. Too few leaves GPU preprocessing starved; too many causes CPU contention that also starves GPU preprocessing.
3. The benchmark methodology: -c 5 -j 3 means 5 proofs with concurrency 3 — up to 3 proofs in flight simultaneously. The throughput is computed as total_time / completed * concurrency or equivalently from the individual proof times.
4. The hardware context: A 96-core AMD Zen4 machine with an RTX 5070 Ti GPU. The optimal partition_workers value is hardware-dependent — a machine with fewer cores would need a lower setting.
5. The SRS preload mechanism: The daemon loads ~10+ GiB of Structured Reference String parameters from disk on startup, which takes 30-60 seconds. The assistant must wait for this before benchmarking.

Output Knowledge Created

This message creates several pieces of knowledge:

1. pw=15 yields 44.8s/proof — slightly worse than pw=10 and pw=12 (both 43.5s/proof), confirming that the optimal range is 10-12, not 15.
2. The relationship is non-monotonic: Throughput doesn't improve linearly with partition_workers. It peaks at 10-12, degrades slightly at 15, recovers somewhat at 18 (43.8s/proof per the chunk summary), and degrades again at 20 (44.9s/proof). This U-shaped curve is characteristic of resource contention: too few workers underutilize the GPU, too many workers cause CPU thrashing.
3. Empirical validation of the CPU contention model: The regression at pw=15 (compared to pw=10/12) confirms the assistant's earlier diagnosis that excessive partition workers starve GPU preprocessing threads. Even at pw=15, the contention is measurable.
4. A production recommendation: The sweep ultimately identifies pw=10-12 as optimal for this hardware, giving operators a clear setting for deployment.

The Broader Significance

This message is a microcosm of the engineering process behind high-performance computing optimization. It demonstrates that:

• Optimization is empirical, not theoretical: The optimal partition_workers setting couldn't be predicted from first principles. It required systematic measurement across multiple values.
• Small differences matter: The difference between 43.5s/proof (pw=10) and 44.8s/proof (pw=15) 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 benchmark (timeouts, config persistence bugs) are as much a part of the story as the benchmark itself. 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

Message <msg id=2277> appears at first glance to be a routine benchmark execution — a bash command, some output, a number. But in context, it represents the culmination of a multi-week optimization effort spanning seven files, 195 lines of code changes, and architectural decisions about mutex scoping, GPU worker interleaving, and CPU-GPU coordination. The 44.8 seconds per proof at partition_workers=15 is not just a number; it is a data point in a carefully designed experiment, a validation of the assistant's contention model, and a stepping stone toward the final production recommendation of pw=10-12. It is a reminder that in systems engineering, the path to optimal performance is paved with systematic measurement, operational debugging, and the willingness to let data — not intuition — drive decisions.