The Moment of Truth: Benchmarking Phase 11's First Intervention

In the high-stakes world of Groth16 proof generation optimization, few moments carry as much weight as the first benchmark of a new intervention. Message [msg 2759] captures precisely such a moment: the assistant, having just implemented Intervention 1 of Phase 11 — serializing the async deallocation path with a static mutex — prepares to measure whether the change actually improves throughput. The message is deceptively brief, but it sits at a critical juncture in a multi-week optimization campaign targeting the SUPRASEAL_C2 proving engine for Filecoin's PoRep (Proof-of-Replication) protocol.

The Context: Phase 11's Three Interventions

To understand what hangs in the balance, we must step back. The optimization journey had reached Phase 9, which achieved 32.1 seconds per proof in isolation but degraded to 38.0 seconds per proof under realistic load (concurrency=20, jobs=15). Waterfall analysis revealed a system that had shifted from GPU-bound to CPU memory-bandwidth-bound, with DDR5 bandwidth contention, L3 cache thrashing, and TLB shootdown storms all contributing to the degradation. Phase 11 was designed as a three-pronged response:

  1. Intervention 1: Serialize async_dealloc with a static mutex to bound TLB shootdown storms
  2. Intervention 2: Reduce groth16_pool from 192 to 32 threads to cut L3 thrashing
  3. Intervention 3: A global atomic throttle flag to reduce memory bandwidth contention during b_g2_msm The assistant had just committed the Phase 10 post-mortem and Phase 11 design spec ([msg 2748]), implemented Intervention 1 in both C++ and Rust code ([msg 2750], [msg 2753], [msg 2754]), and verified the binaries compiled successfully ([msg 2756], [msg 2757]). Message [msg 2759] is the first step in putting those changes to the test.

Reading the Config: A Systematic Approach

The message opens with the assistant reviewing the benchmark configuration:

Good — gpu_workers_per_device = 2, partition_workers = 10, no gpu_threads set (defaults to all CPUs). Let me start the daemon and run c=20 j=15 benchmark:

This is not idle commentary. The assistant is performing a critical verification step. The config file /tmp/cuzk-p9-gw2.toml (read in the previous message, [msg 2758]) specifies:

The Benchmark Plan: c=20 j=15

The assistant announces the plan to "run c=20 j=15 benchmark." In the language of the cuzk proving engine, c is the number of proofs to generate (concurrency count) and j is the number of concurrent benchmark jobs. The standard benchmark configuration of 20 proofs at concurrency 15 was established during earlier phases as the representative production workload. It simulates the load pattern that a Curio storage miner would experience during real Filecoin proving operations.

This specific configuration was chosen because it exposes the memory-bandwidth contention that Phase 11 aims to mitigate. At lower concurrency, the system performs well — Phase 9 achieved 32.1 s/proof in isolation. But at c=20 j=15, contention inflates GPU per-partition time from 4.9 s to 7.5 s, prep_msm from 1.7 s to 2.7 s, and synthesis from 35 s to 54 s. The benchmark is designed to stress-test the memory subsystem and reveal whether the interventions actually reduce contention.## The Daemon Restart: A Necessary Ritual

The assistant then executes:

[bash] pkill -f cuzk-daemon 2>/dev/null; sleep 1; echo "daemon killed"

This is a mundane but essential step. The cuzk-daemon is the long-running server process that loads SRS parameters into GPU memory, manages the synthesis dispatcher, and orchestrates GPU workers. Each benchmark run requires a fresh daemon process to ensure clean state — no leftover GPU allocations, no stale timing instrumentation, no accumulated memory fragmentation. The pkill -f cuzk-daemon terminates any existing instance, sleep 1 gives the OS time to release GPU resources, and the echo confirms completion.

What follows this message (visible in [msg 2760] through [msg 2766]) is the actual benchmark execution: the daemon is restarted, the cuzk-bench batch command is issued (after a brief CLI discovery process — the assistant initially used the wrong flag syntax), and the result comes back as 37.9 s/proof — essentially identical to the Phase 9 baseline of 38.0 s/proof. Intervention 1 alone produced no measurable improvement.

Assumptions Under the Microscope

The message reveals several assumptions that the assistant is operating under:

Assumption 1: The config is correct. The assistant assumes that /tmp/cuzk-p9-gw2.toml still reflects the intended benchmark configuration. This is a reasonable assumption — the file was written during Phase 9 benchmarking and hasn't been modified since. However, the assistant does not verify that the daemon binary being launched corresponds to the newly compiled code (with Intervention 1). The build step in [msg 2756] and [msg 2757] rebuilt both cuzk-daemon and cuzk-bench, so the binary at target/release/cuzk-daemon should include the dealloc_mtx changes. But the assistant does not explicitly confirm this.

Assumption 2: The benchmark will work. The assistant assumes that cuzk-bench accepts the same flags as before. This assumption is immediately falsified in the next message ([msg 2762]), where --server is rejected as an unexpected argument. The CLI interface has changed between benchmark runs, requiring the assistant to discover the new batch subcommand syntax. This is a common friction point in long-running optimization campaigns where tooling evolves alongside the codebase.

Assumption 3: TLB shootdown reduction matters. The entire rationale for Intervention 1 was that serializing async deallocation would reduce TLB shootdown storms, which occur when multiple threads concurrently unmap large memory regions (the synthesis vectors are ~200 GiB total). The assumption was that these shootdowns were contributing significantly to the 38.0 s/proof baseline. The benchmark result of 37.9 s/proof effectively refutes this assumption — the TLB shootdown cost was not a major factor in the observed degradation.

The Reasoning Process: What the Message Reveals

The assistant's reasoning is visible in the careful sequencing of actions. Rather than implementing all three interventions at once and benchmarking the combined result, the assistant chooses to implement and benchmark each intervention independently. This is a deliberate methodological choice that reveals the assistant's engineering philosophy: isolate variables, measure individual impact, and only combine interventions that independently prove their worth.

The message also reveals the assistant's awareness of the broader optimization landscape. The comment "no gpu_threads set (defaults to all CPUs)" shows that the assistant is already thinking ahead to Intervention 2, which will reduce the thread pool size. The assistant knows that the current configuration uses all 192 CPU threads for the groth16_pool, and that this over-provisioning is suspected of causing L3 cache thrashing. By noting the default behavior explicitly, the assistant is setting up the contrast for Intervention 2's benchmark.

Input Knowledge Required

To fully understand this message, one needs:

  1. The Phase 11 design spec: Understanding that Intervention 1 targets TLB shootdowns from concurrent async deallocation
  2. The Phase 9 baseline: Knowing that 38.0 s/proof at c=20 j=15 is the reference point for improvement
  3. The cuzk architecture: Understanding the role of gpu_workers_per_device (GPU worker parallelism), partition_workers (synthesis parallelism), and gpu_threads (CPU thread pool for GPU-adjacent computation)
  4. The daemon lifecycle: Knowing that the daemon must be restarted between benchmarks for clean state
  5. The benchmark methodology: Understanding that c=20 j=15 is the standard stress-test configuration

Output Knowledge Created

This message, combined with its follow-ups, produces:

  1. A benchmark result: Intervention 1 alone yields 37.9 s/proof — essentially no improvement
  2. A validated hypothesis: TLB shootdown storms are not a primary bottleneck at this configuration
  3. A decision point: The assistant can move to Intervention 2 with confidence that the dealloc serialization is not masking other effects
  4. Timing data: The daemon logs show async_dealloc_ms values ranging from 125 ms to 3,564 ms, confirming that serialization is working (some deallocs now wait for previous ones) but not improving throughput

The Broader Significance

Message [msg 2759] is a reminder that optimization work is fundamentally experimental. Each intervention is a hypothesis: "If we reduce X, throughput will improve by Y%." The hypothesis must be tested, measured, and either validated or refuted. Intervention 1's hypothesis was refuted — TLB shootdown serialization did not improve throughput. But this negative result is itself valuable knowledge. It tells the team where not to invest further effort, and it clears the path for the next hypothesis.

The message also illustrates the discipline required for systematic optimization. The assistant could have implemented all three interventions at once, benchmarked the combined result, and attributed any improvement to the whole package. But that approach risks conflating effects — what if Intervention 2 helps and Intervention 3 hurts? The independent benchmarking approach, while slower, produces cleaner causal evidence.

In the messages that follow, Intervention 2 (reducing groth16_pool to 32 threads) delivers a 3.4% improvement to 36.7 s/proof, confirming that L3 cache thrashing was indeed a significant factor. Intervention 3 then adds negligible further benefit. The split-API design that emerges from this analysis (Phase 12) represents a more ambitious architectural change to hide b_g2_msm latency — a direct consequence of the Phase 11 benchmarking results showing that CPU-side post-processing was becoming the next bottleneck.

Message [msg 2759] stands at the threshold of this discovery. It is the calm before the data arrives, the moment of anticipation when the assistant has done everything right — committed the code, verified the build, checked the config — and now must simply run the experiment and see what the machine reveals.