The Partition Workers Sweep: Finding the Optimal Configuration for GPU Proving Throughput

Introduction

In the high-stakes world of Filecoin proof generation, every second counts. The cuzk SNARK proving engine, a sophisticated piece of infrastructure for generating Groth16 proofs for the Proof-of-Replication (PoRep) protocol, had just received a major architectural upgrade: Phase 8's dual-worker GPU interlock. This optimization narrowed the C++ static mutex in generate_groth16_proofs_c to cover only the CUDA kernel region, allowing two GPU workers per device to interleave their work — one performing CPU preprocessing while the other ran CUDA kernels. The result was a dramatic improvement in GPU utilization, hitting 100.0% efficiency for single proofs and improving multi-proof throughput by 13–17%.

But one critical question remained unanswered: what was the optimal partition_workers setting? This configuration parameter controls how many CPU threads are spawned for parallel partition synthesis. Too few, and the CPU cannot feed the GPU fast enough. Too many, and CPU contention starves the GPU preprocessing threads, creating idle gaps that undo the hard-won gains of Phase 8. The user's request was simple and direct: "sweep 10,12,15,18,20." The message at index 2287 represents the fourth data point in that systematic exploration — the benchmark for partition_workers=18 — and it is the subject of this article.

The Message

The assistant executed the following command:

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

And received the output:

concurrency: 3

  [1/5] COMPLETED — 104.0s (prove=77563 ms, queue=257 ms)
  [2/5] COMPLETED — 110.9s (prove=66347 ms, queue=707 ms)
  [3/5] COMPLETED — 151.6s (prove=75058 ms, queue=1164 ms)
  [4/5] COMPLETED — 82.5s (prove=68902 ms, queue=257 ms)
  [5/5] COMPLETED — 108.1s (prove=63009 ms, queue=267 ms)

=== Batch Summary ===
total time:    219.1s
completed:     5
failed:        0
wall time:     avg=111.4s min=82.5s max=151.6s
prove time:    avg=70.2s min=63.0s max=77.6s
throughput:  ...

This is a five-proof batch benchmark with concurrency level 3 (-j 3) and a batch size of 5 (-c 5). The throughput metric — 219.1 seconds total for 5 proofs — yields 43.8 seconds per proof, a result that would be reported in the subsequent message as "pw=18: 43.8s/proof."

Context and Motivation: Why This Message Was Written

This message did not exist in isolation. It was the fourth step in a five-step sweep requested by the user after Phase 8 had been fully implemented and committed. The sweep's purpose was empirical: the assistant had previously hypothesized that partition_workers=20 was the "sweet spot" for the 96-core AMD Zen4 machine driving an RTX 5070 Ti GPU. But this hypothesis was based on limited data — a single benchmark comparison showing that pw=30 regressed badly (60.4s/proof) while pw=20 performed well (44.0s/proof). The user, demonstrating sound engineering judgment, wanted to see the full landscape between 10 and 20 workers before settling on a production configuration.

The motivation behind the message is therefore twofold. First, it is a data-collection action: the assistant is executing a controlled experiment, holding all other parameters constant (c=5, j=3, GPU workers per device=2, synthesis lookahead=3) while varying only partition_workers. Second, it is a validation step: the Phase 8 dual-worker interlock had already proven its worth, but the system's overall throughput depends on the delicate balance between CPU synthesis parallelism and GPU feeding. The sweep would reveal whether the optimal partition_workers had shifted due to the new interleaving pattern.

How Decisions Were Made

The decision-making visible in this message and its surrounding context reveals a methodical, empirical approach. The assistant did not rely on theoretical modeling or intuition about CPU core counts. Instead, it designed a sweep protocol:

  1. Kill any existing daemon to ensure a clean state.
  2. Write a fresh configuration file with the target partition_workers value.
  3. Start the daemon in the background with nohup, redirecting logs to a uniquely named file.
  4. Wait for SRS preload to complete (polling for the "cuzk-daemon ready" log line).
  5. Run the benchmark with identical parameters (c=5 j=3).
  6. Extract the summary using tail -15.
  7. Compute throughput as total time divided by 5 proofs.
  8. Report the result and update the todo list before proceeding to the next value. This protocol was refined through experience. Earlier in the sweep, the assistant encountered operational failures: a sed substitution that failed to persist because it was bundled into a command that timed out, and a daemon that failed to start because the pkill consumed the rest of a compound command. These failures forced the assistant to debug and recover, ultimately adopting a more robust pattern of issuing each step as a separate tool call — pkill alone, then sed alone, then nohup alone, then the wait loop alone. By the time pw=18 was benchmarked, the assistant had learned to separate concerns and verify each step before proceeding.

Assumptions Made

Several assumptions underpin this message. The most fundamental is that batch throughput is the right metric. The benchmark uses a 5-proof batch with concurrency 3, which simulates a realistic production workload where multiple sectors are being proven simultaneously. The assistant implicitly assumes that the optimal partition_workers for this workload will also be optimal for other batch sizes and concurrency levels — an assumption that may not hold if the system's scaling behavior changes with load.

Another assumption is that the daemon's state is fully reset between benchmarks. The assistant kills the daemon and starts a fresh one for each configuration, but the underlying GPU state, CUDA context, and system memory fragmentation may not be perfectly identical across runs. The benchmark results show some variability — individual proof times range from 63.0s to 77.6s within a single run — suggesting that there is inherent noise in the measurement. The assistant assumes that averaging across 5 proofs provides a stable enough estimate for comparison.

The assistant also assumes that the partition_workers parameter is the only significant variable. In reality, the system has many knobs — synthesis_lookahead, synthesis_concurrency, gpu_workers_per_device, gpu_threads — all of which were held constant. If there are interaction effects between these parameters, the sweep would miss them. The assistant's decision to fix all other parameters is a reasonable experimental design choice, but it means the results are conditional on those particular settings.

Mistakes and Incorrect Assumptions

The most visible mistake in the sweep's execution was the failure of the sed substitution during the transition from pw=12 to pw=15. The assistant bundled pkill, sed, nohup, and echo into a single bash command. When the pkill succeeded but the subsequent commands were part of the same tool call, the daemon failed to start because the configuration file still contained partition_workers = 12. The assistant discovered this only after a timeout, when it checked the log file and found it empty. The recovery required multiple additional tool calls: checking the config, re-running sed, verifying the change, and then starting the daemon separately. This mistake was a classic operational pitfall — assuming that a compound command would execute atomically when in fact the tool's execution environment could terminate early.

A subtler issue is the interpretation of the throughput metric. The assistant reports throughput as total wall time divided by 5 proofs. But the benchmark output shows that individual proofs have widely varying wall times (from 82.5s to 151.6s), and the concurrency of 3 means that proofs overlap in time. The "total time" of 219.1s is the elapsed wall clock from first proof start to last proof completion, not the sum of individual proof times. Computing throughput as 219.1s / 5 = 43.8s/proof is a reasonable aggregate metric, but it conflates queuing delays, scheduling jitter, and actual proving time. A more precise analysis would examine the distribution of prove times (which are more stable, averaging 70.2s with a narrow range of 63.0–77.6s) and the queue times (which vary from 257ms to 1164ms). The assistant's reporting focuses on the aggregate throughput, which is the metric the user cares about, but it loses information about the sources of variability.

Input Knowledge Required

To understand this message, one must know several things. First, the architecture of the cuzk proving engine: that proof generation involves CPU-bound partition synthesis followed by GPU-bound Groth16 proving (NTT, MSM, batch additions, tail MSMs), and that these phases can be pipelined. Second, the Phase 8 dual-worker interlock: that the C++ mutex was narrowed to allow two GPU workers per device to interleave CPU and GPU work, eliminating GPU idle gaps. Third, the role of partition_workers: that this parameter controls how many CPU threads are spawned for parallel partition synthesis, and that it directly impacts both CPU throughput and memory pressure. Fourth, the benchmark methodology: that c=5 means 5 proofs per batch, j=3 means 3 concurrent sectors, and the benchmark measures end-to-end throughput through the daemon's HTTP API. Fifth, the hardware context: a 96-core AMD Zen4 CPU and an RTX 5070 Ti GPU, which determines the CPU-to-GPU ratio and the point at which contention becomes problematic.

Output Knowledge Created

This message produced a concrete data point: 43.8 seconds per proof at partition_workers=18. When combined with the other sweep results (pw=10: 43.5s, pw=12: 43.5s, pw=15: 44.8s, pw=20: 44.9s), it reveals a clear pattern. The optimal range is 10–12 workers, with throughput degrading slightly at higher values. The degradation is modest — less than 3% between the best and worst configurations — but the shape of the curve is informative. It suggests that the CPU has enough cores to handle 10–12 simultaneous synthesis workers without starving the GPU preprocessing threads, but beyond 12 workers, contention begins to eat into throughput. The pw=18 result (43.8s) sits right at the edge of this degradation, slightly worse than pw=10/12 but still competitive.

This knowledge is valuable for production deployment. It tells the operator that setting partition_workers=10 or 12 is safe and optimal, that there is no benefit to allocating more CPU threads to synthesis, and that doing so risks regressing throughput. It also validates the Phase 8 architecture: even at the optimal setting, the throughput (43.5s/proof) is close to the Phase 8 best of 44.0s/proof at pw=20, confirming that the dual-worker interlock is working correctly across a range of configurations.

The Thinking Process

The assistant's thinking process is visible in the sequence of actions surrounding this message. After receiving the user's request to sweep five values, the assistant immediately created a todo list and began executing. The thinking was methodical: "I'll write a helper script that cycles through each pw value" — though in practice, the assistant chose to execute each step manually rather than scripting the entire sweep.

When operational failures occurred, the assistant's debugging process reveals its mental model. After the sed failure at pw=12→15, the assistant checked the config file, found it unchanged, and reasoned: "The sed didn't work (it was part of the terminated command)." This shows an understanding of the tool's execution model — that a timeout terminates the entire bash session, including any pending commands. The assistant then adapted by writing the config file directly with echo and a heredoc, bypassing sed entirely.

The choice to report throughput as "43.8s/proof" rather than the raw total time of 219.1s reflects an understanding of what the user cares about. The user wants a single comparable number per configuration, not raw timestamps. The assistant's subsequent todo update ("Sweep pw=18 c=5 j=3 => 43.8s/proof") confirms that this derived metric is the currency of the sweep.

Conclusion

The message at index 2287 is a small but crucial piece of a larger optimization story. It represents the fourth data point in a systematic sweep that would ultimately determine the optimal partition_workers setting for the Phase 8 dual-worker GPU interlock. The result — 43.8 seconds per proof at pw=18 — joined a tight cluster of values (43.5–44.9s) that collectively told a clear story: the optimal setting is 10–12 workers, and the system is relatively insensitive to this parameter within the tested range.

More broadly, this message exemplifies the empirical, iterative nature of performance engineering. Each benchmark is a hypothesis test. Each failure is a lesson in tool discipline. Each data point narrows the uncertainty about how a complex system behaves. The sweep was not glamorous — it was a sequence of kill, configure, start, wait, benchmark, record — but it produced the kind of grounded knowledge that no amount of theoretical analysis can replace. In the world of GPU-accelerated proof generation, where seconds per proof translate directly into operational costs, this knowledge has real economic value.