Benchmarking the Baseline: Measuring Throughput Before Thread Isolation

In the long and intricate process of optimizing the cuzk SNARK proving engine for Filecoin's Proof-of-Replication (PoRep), there comes a moment when theory must yield to measurement. Message [msg 1927] captures precisely such a moment: the assistant, having just implemented a thread pool partitioning scheme to isolate CPU synthesis work from GPU proving threads, pauses to run a baseline benchmark. This message is deceptively simple — a single bash command and its output — but it represents the culmination of dozens of preceding messages of analysis, code modification, and debugging, and it sets the stage for the comparative measurements that will validate (or invalidate) the optimization hypothesis.

The Context: Thread Isolation as a Response to CPU Contention

To understand why this benchmark matters, one must understand the problem it was designed to measure. The cuzk proving engine, as documented throughout this coding session, suffers from a structural performance bottleneck: CPU resource contention between synthesis work (the CPU-heavy circuit evaluation that produces proving assignments) and the GPU proving pipeline itself. Earlier in the session ([msg 1896] through [msg 1926]), the assistant had diagnosed that when multiple proofs are synthesized in parallel using rayon's global thread pool, the synthesis threads compete with the C++ groth16_pool threads (used for MSM computations like b_g2_msm) for CPU cores. This contention manifests as slowdowns in both synthesis and GPU-side computation, reducing overall throughput.

The assistant's proposed fix was twofold. First, configure rayon's global thread pool to a subset of available cores (controlled by a new synthesis.threads configuration parameter), reserving the remaining cores for the C++ thread pool. Second, add a CUZK_GPU_THREADS environment variable that the C++ groth16_cuda.cu code reads at startup to limit its own thread count. This partitioning, the theory went, would allow synthesis and GPU proving to proceed on dedicated cores without interfering with each other.

But before applying this optimization, the assistant needed a baseline — a measurement of the current, un-isolated performance. Message [msg 1927] is that baseline measurement.

What the Message Contains

The message is structured as a single tool invocation: a bash command that runs the cuzk-bench benchmark tool against a running daemon instance. The command and its parameters reveal the experimental design:

/home/theuser/curio/extern/cuzk/target/release/cuzk-bench \
  -a http://127.0.0.1:9820 \
  batch -t porep --c1 /data/32gbench/c1.json \
  --count 5 --concurrency 2 2>&1

The parameters encode several deliberate choices. The --count 5 specifies five proofs to generate, a number large enough to observe steady-state behavior after any warmup effects. The --concurrency 2 limits the daemon to processing two proofs simultaneously, matching the configuration used in earlier benchmarks and providing a consistent comparison point. The proof type is porep (Proof-of-Replication), the most computationally demanding of the Filecoin proof types. The C1 input data is loaded from /data/32gbench/c1.json, a pre-computed C1 output representing a 32 GiB sector — the standard sector size for Filecoin mainnet.

Interpreting the Results

The benchmark output reveals a system with complex dynamics. Each proof reports two timing components: prove (the actual GPU proving time) and queue (the time spent waiting in the job queue before synthesis begins). The results show:

• Proof 1: 64.3s total (prove=27,350ms, queue=271ms)
• Proof 2: 106.9s total (prove=27,233ms, queue=37,060ms)
• Proof 3: 83.7s total (prove=26,780ms, queue=15,633ms)
• Proof 4: 83.1s total (prove=25,847ms, queue=255ms)
• Proof 5: 82.5s total (prove=28,298ms, queue=...) The prove time is remarkably consistent across all five proofs, hovering around 26–28 seconds. This is the pure GPU computation time — the Groth16 proof generation once all synthesis is complete. Its stability suggests that the GPU is not the bottleneck; it can process each proof in a predictable window regardless of system load. The queue time tells a very different story. Proof 2 waited over 37 seconds in the queue — longer than its entire GPU proving phase. Proof 3 waited over 15 seconds. This queue time represents the period during which the daemon's job scheduler holds a proof request before dispatching it for synthesis, typically because the maximum concurrency limit has been reached and earlier proofs have not yet completed their GPU phase. This pattern reveals the pipeline's fundamental rhythm. With concurrency=2, the daemon allows two proofs to be in flight simultaneously. Proof 1 starts immediately (queue=271ms, essentially zero), enters synthesis, then GPU proving. Proof 2 enters the queue and waits until proof 1's GPU work completes (hence the ~37s queue time, which closely matches proof 1's total turnaround). Proof 3 then waits for proof 2's GPU slot, but with a shorter queue time (15.6s) because by then the pipeline has partially filled and the overlap is more favorable. By proofs 4 and 5, the system has reached steady state: queue times drop to near zero because a proof finishes its GPU work just as the next one needs the resource.

The Significance of the Baseline

The assistant's comment in the following message ([msg 1928]) summarizes the baseline as "46.1s/proof, avg prove=27.1s." The 46.1s average throughput is significantly worse than the 27.1s pure prove time because of the queue-induced idle time. This gap — the difference between raw GPU capability and realized throughput — is precisely what the thread isolation optimization aims to close.

The baseline also reveals an important subtlety: the first proof (64.3s) is slower than the steady-state proofs (82-83s... wait, actually proof 1 at 64.3s is faster than proofs 3-5 at 82-83s). This is because proof 1 has no queue time and benefits from a cold-start effect where the SRS is already loaded by the preload phase. Proofs 3-5 have longer total times because their queue times, while smaller than proof 2's, still add to the wall clock.

Assumptions and Limitations

The benchmark makes several assumptions worth examining. It assumes the daemon has completed SRS preloading (the 30-second sleep before the benchmark ensures this). It assumes the C1 input file is representative of production workloads. It assumes that five proofs are sufficient to reach steady state — and the data supports this, as proofs 4 and 5 show consistent timing.

A notable limitation is that the output is truncated (ending with "..."), so the summary line showing the average throughput is not visible in this message. The assistant presumably saw the full output in the terminal and extracted the 46.1s/proof figure reported in the next message.

The Knowledge Flow

This message both consumes and produces knowledge. It consumes the knowledge that the daemon is running and responsive (verified by the "Good, still running" preamble), that the benchmark tool is correctly built and configured, and that the C1 data is accessible. It produces quantitative knowledge about the baseline performance: the 27.1s average prove time, the queue dynamics, and the 46.1s/proof effective throughput.

This output knowledge is immediately actionable. In [msg 1928], the assistant uses this baseline to design the comparative experiment, stopping the baseline daemon and starting a new instance with the thread isolation configuration. The benchmark in message [msg 1927] is thus the control experiment in a scientific comparison — without it, the assistant would have no way to determine whether the thread isolation changes actually improve throughput or merely shift the bottleneck elsewhere.

Broader Implications

Beyond its immediate role in the thread isolation investigation, this benchmark exemplifies a disciplined approach to systems optimization. The assistant does not simply apply a fix and hope for improvement; it first measures the current state, establishes a baseline, and only then introduces the change. This methodology is essential when optimizing complex, stateful systems like the cuzk proving engine, where interactions between components (synthesis, GPU proving, job scheduling, memory allocation) create emergent behaviors that cannot be predicted from first principles alone.

The benchmark also reveals the inherent challenge of pipeline optimization: the queue time pattern shows that the system is fundamentally throughput-limited by the GPU's serialization of proof processing. Even with perfect thread isolation, the 27.1s GPU proving window per proof sets a hard floor on latency. The real gains from thread isolation will come not from reducing the prove time, but from reducing the queue-induced idle time by allowing synthesis to complete faster and keep the GPU pipeline fuller — a subtle but important distinction that the baseline data makes visible.