The Final Benchmark: Validating Memory Backpressure in a GPU Proving Pipeline

Introduction

In the high-stakes world of Filecoin storage proof generation, every second and every gigabyte counts. The SUPRASEAL_C2 Groth16 proving pipeline, a critical component of the Curio storage mining system, must generate proofs for 32 GiB sectors under tight memory constraints. This article examines a single message from an opencode coding session — message 3211 — which represents a pivotal moment in a multi-week optimization effort: the final validation benchmark of Phase 12's memory backpressure mechanism.

The message itself is deceptively simple: a bash command launching the cuzk-bench benchmark tool with 20 proofs at concurrency 20, targeting the PoRep (Proof of Replication) circuit type. But behind this single command lies a complex chain of reasoning, months of optimization work, and a critical design decision that transformed a pipeline that was OOM-killing at 668 GiB into one that runs stably at under 400 GiB peak RSS.

The Message in Full

FIL_PROOFS_PARAMETER_CACHE=/data/zk/params /home/theuser/curio/extern/cuzk/target/release/cuzk-bench batch --type porep --c1 /data/32gbench/c1.json --count 20 --concurrency 20 2>&1

The output shows the first four proofs completing with prove times ranging from 61.9s to 81.3s, and queue times growing from 300ms to 1.7s as the pipeline fills up. The output is truncated after the fourth proof, but the assistant will later compute the aggregate throughput.

Why This Message Was Written: The Reasoning and Context

To understand why this particular benchmark was run, we must trace the reasoning chain through the preceding messages. The assistant had just implemented three critical memory backpressure interventions for Phase 12 of the cuzk optimization roadmap:

  1. Early a/b/c free: Immediately after prove_start returns from the GPU, the ~12 GiB per partition of evaluation vectors (a, b, c) are deallocated. The GPU no longer needs them since it has already consumed the data, but the original code held them until finalization.
  2. Channel capacity auto-scaling: The synthesis-to-GPU channel capacity was increased from a hardcoded 1 to max(synthesis_lookahead, partition_workers). This prevented completed syntheses from blocking on send() while holding large allocations in memory.
  3. Partition permit held through send: The semaphore permit that bounds in-flight partitions is now released only after the channel send succeeds, not immediately after synthesis completes. This ensures the total number of in-flight outputs is bounded to partition_workers without adding latency, since the channel has room for all of them. These changes had already been validated with pw=10 (partition_workers=10), achieving 38.9s/proof with 314.7 GiB peak RSS — a dramatic improvement from the 668 GiB OOM that previously occurred at pw=12. The assistant then tested pw=12 and found it ran successfully at 38.4s/proof with 383.8 GiB peak RSS, whereas it had previously OOM'd at 668 GiB. However, a nagging question remained: the Phase 12 baseline benchmark (before the memory backpressure changes) had achieved 37.1s/proof. The current runs were consistently around 38.4–38.9s/proof — a ~1.5–1.8s regression. The assistant hypothesized several possible causes: - The eprintln! calls added for buffer instrumentation (150+ synchronous stderr writes per run) could be causing contention on the tokio runtime. - The early a/b/c free added Vec::new() assignments and deallocation work (~120 GiB total across 10 partitions) inside the GPU hot path. - Memory fragmentation from the new allocation patterns could be slowing GPU operations. - The Phase 12 baseline might have simply been a lucky run with favorable memory layout. The assistant first tested the eprintln! hypothesis by converting all buffer counters to tracing::debug ([msg 3189] through [msg 3194]). The subsequent benchmark ([msg 3199]) showed 38.8s/proof — identical to before — ruling out stderr contention as the cause. Next, the assistant examined GPU timing data. The Phase 12 baseline had 200 partitions (20 proofs × 10 partitions) with a mean GPU time of 6.76s per partition. The current run had 150 partitions (15 proofs × 10 partitions) with a mean GPU time of 7.28s — about 0.5s slower per partition. With two GPU workers running in parallel, that accounts for roughly 5s per proof, which would explain the 1.7s throughput difference. This led to a critical realization: the comparison was apples-to-oranges. The Phase 12 baseline used 20 proofs, while the current memory-backpressure benchmarks used 15 proofs. Different sample sizes could affect amortization of startup costs, tail effects, and memory pressure patterns. Message 3211 was the assistant's attempt to run a fair comparison by matching the 20-proof count of the baseline.

Assumptions Embedded in the Message

The benchmark command in message 3211 carries several implicit assumptions that are worth examining. First, the assistant assumes that running 20 proofs at concurrency 20 will produce a throughput figure comparable to the Phase 12 baseline of 37.1s/proof. This assumes that the only variable that changed between runs is the memory backpressure code, and that any throughput difference must be attributable to the code changes rather than environmental factors.

Second, the assistant assumes that the c1.json input file — the C1 output from a prior phase of proof generation — is identical across all benchmark runs. If the C1 data differs between runs (e.g., due to different sector data or randomness), the synthesis and proving costs could vary independently of the pipeline changes.

Third, the assistant assumes that the benchmark's concurrency level of 20 (matching the proof count) is sufficient to saturate the pipeline and measure steady-state throughput. If the pipeline has startup transients that extend beyond the first few proofs, the aggregate throughput calculation could be skewed. The assistant's earlier analysis of queue times (growing from 300ms to 1.7s across the first four proofs) suggests the pipeline is still filling up during this period.

Fourth, there is an assumption that the FIL_PROOFS_PARAMETER_CACHE environment variable pointing to /data/zk/params provides a sufficiently warm cache. If parameter loading dominates the first proof's wall time, the aggregate throughput would be penalized relative to a steady-state measurement.

The Thinking Process Visible in the Message

Although message 3211 contains only a bash command and its output, the thinking process is visible through the context of the surrounding messages. The assistant is engaged in a systematic debugging process that follows a clear pattern:

  1. Measure the baseline: Establish the Phase 12 baseline at 37.1s/proof.
  2. Implement a change: Add memory backpressure with early a/b/c free, channel capacity scaling, and semaphore fix.
  3. Measure the result: 38.4–38.9s/proof — a regression of ~1.5–1.8s.
  4. Hypothesize the cause: The eprintln! instrumentation might be causing contention.
  5. Test the hypothesis: Convert eprintln! to tracing::debug and re-benchmark — 38.8s/proof. Hypothesis rejected.
  6. Form a new hypothesis: The regression might be due to different sample sizes (15 proofs vs 20 proofs).
  7. Test the new hypothesis: Run 20 proofs at pw=12 to match the baseline sample size — this is message 3211. This is classic scientific method applied to performance engineering: observe, hypothesize, predict, experiment, analyze. The assistant is not merely running benchmarks; it is systematically isolating variables to identify the root cause of a performance regression. The assistant's reasoning also reveals a sophisticated understanding of the system's memory dynamics. The decision to hold the partition permit through the channel send (rather than releasing it after synthesis) is a nuanced application of backpressure theory. The channel capacity is the natural throttle — if the channel has room for all partition_workers outputs, then holding the permit through the send adds no latency. But if the channel were smaller, the permit would create unnecessary blocking. This insight — that the semaphore and the channel should be coordinated rather than treated as independent throttles — is the key architectural contribution of Phase 12.

Input Knowledge Required

To fully understand this message, one needs knowledge spanning several domains:

Groth16 proof generation: The benchmark generates Groth16 zk-SNARK proofs for the Filecoin Proof of Replication (PoRep) circuit. Each proof requires synthesizing R1CS constraints (the "synthesis" phase) and then computing a multi-scalar multiplication (MSM) and number-theoretic transform (NTT) on the GPU (the "proving" phase). The circuit is split into 10 partitions, each processed independently.

The cuzk architecture: The cuzk system is a Go-Rust-CUDA pipeline where Curio (Go) orchestrates tasks, the cuzk-daemon (Rust) manages a pipeline of synthesis tasks feeding GPU workers, and the actual GPU computation happens in C++/CUDA kernels. The "split API" (Phase 12) decouples the GPU worker's critical path from CPU post-processing by allowing the GPU to start on a new partition while the CPU finishes finalizing the previous one.

Memory accounting: Each partition's evaluation vectors (a, b, c) consume approximately 12 GiB. With 10 partitions per proof and multiple proofs in flight, memory can balloon rapidly. The system has 755 GiB of RAM, but the original code could OOM at 668 GiB because the kernel's OOM killer triggers before swap is fully utilized.

Benchmark methodology: The cuzk-bench batch command runs multiple proofs sequentially (or concurrently) and reports per-proof wall times. The "prove" time excludes queue wait, while the total elapsed time includes it. Throughput is calculated as total elapsed time divided by proof count.

Output Knowledge Created

Message 3211 produced several pieces of actionable knowledge, even though the output is truncated. The assistant will later compute the aggregate throughput from this run and compare it against the Phase 12 baseline. The key outputs are:

Per-proof timing data: The first proof completes in 82.5s (prove=80.6s, queue=0.3s), the second in 125.7s (prove=61.9s, queue=0.8s), the third in 156.0s (prove=81.3s, queue=1.3s), and the fourth in 184.9s (prove=71.0s, queue=1.7s). The prove times vary significantly (62–81s), which is characteristic of a system where GPU work is pipelined and individual proof latencies depend on queue depth and memory state.

Queue time growth: The queue time increases monotonically from 0.3s to 1.7s across the first four proofs, indicating that the pipeline is still filling up during this period. This is expected behavior for a pipeline with 20 concurrent proofs — the first proof experiences minimal queue delay, while later proofs wait for earlier ones to clear the GPU.

Validation of pw=12 stability: The most important implicit output is that the benchmark completes without OOM. Earlier attempts at pw=12 without the memory backpressure changes resulted in OOM at 668 GiB. The fact that the benchmark is running at all confirms that the three interventions (early a/b/c free, channel capacity scaling, permit held through send) are working correctly.

Baseline for throughput comparison: The assistant will use this run to determine whether the ~1.5–1.8s regression from the Phase 12 baseline is attributable to the memory backpressure changes or to the different sample size. If the 20-proof run matches the 37.1s/proof baseline, then the regression was an artifact of the smaller sample. If it still shows ~38.5s/proof, then the memory backpressure changes themselves introduced a small throughput cost.

Mistakes and Incorrect Assumptions

Several assumptions in this message and its surrounding context deserve critical examination:

The "lucky run" hypothesis: The assistant repeatedly suggests that the Phase 12 baseline of 37.1s/proof might have been a "lucky run" with favorable memory layout. While this is possible, it is equally plausible that the baseline was accurate and the memory backpressure changes introduced real overhead. The early a/b/c free, while beneficial for memory, requires deallocating ~120 GiB of Vec allocations inside the spawn_blocking thread that also handles GPU work. Even though munmap is relatively fast, the kernel overhead for 10 × 12 GiB deallocations per proof could add measurable latency.

The eprintln hypothesis: The assistant spent significant effort testing whether eprintln! calls were causing the regression, converting them to tracing::debug. The result (38.8s/proof, unchanged) definitively ruled this out. However, this hypothesis was always somewhat unlikely — 150 synchronous stderr writes over a 38-second proof would add at most a few hundred milliseconds, not 1.8 seconds. The assistant's willingness to test this hypothesis anyway reflects a thorough debugging methodology but also a slight over-investment in a low-probability explanation.

The sample size hypothesis: The assistant assumes that running 20 proofs instead of 15 will explain the throughput difference. However, the earlier GPU timing analysis showed that the mean GPU time per partition was 6.76s in the baseline vs 7.28s in the current run — a 7.7% increase. This difference is unlikely to be explained by sample size alone. A more likely explanation is that the memory backpressure changes, while reducing peak RSS, have increased memory fragmentation or changed allocation patterns in ways that slow GPU memory operations. The cudaHostRegister calls used for pinned memory may be slower when the allocator has more fragmented regions to work with.

The assumption of fair comparison: The assistant is comparing a run with pw=12 (partition_workers=12) against a baseline that may have used a different configuration. The Phase 12 baseline was run with the original code (channel capacity 1, no semaphore fix, no early a/b/c free) and likely with different daemon configuration. Without knowing the exact configuration of the baseline, the comparison is inherently uncertain.

Conclusion

Message 3211 represents a moment of disciplined scientific inquiry in the midst of a complex optimization effort. The assistant, having achieved a dramatic memory improvement (eliminating OOM at pw=12, reducing peak RSS from 668 GiB to under 400 GiB), refuses to accept the result at face value. Instead, it notices a small throughput regression and systematically investigates its cause — ruling out instrumentation overhead, examining GPU timing data, and finally designing a controlled experiment to isolate the effect of sample size.

This message is a microcosm of the broader optimization philosophy visible throughout the cuzk project: every change must be measured, every regression must be explained, and every hypothesis must be tested. The assistant's willingness to question its own assumptions, to run additional benchmarks even after achieving the primary goal, and to engage in methodical root-cause analysis rather than accepting superficial explanations — these are the hallmarks of rigorous performance engineering.

The benchmark launched in message 3211 would go on to reveal that pw=12 with the memory backpressure changes achieves 37.7s/proof — essentially matching the Phase 12 baseline and proving that the memory improvements came at negligible throughput cost. The optimal configuration of pw=12, gw=2, gt=32 would be committed as the production recommendation, delivering the best throughput-to-memory ratio the system had ever achieved. But the real achievement visible in this message is not the final number — it is the process of getting there.