The Pivot Point: How a Single `perf stat` Measurement Decisively Cancelled a Misguided Optimization

Introduction

In the high-stakes world of cryptographic proof generation optimization, not every well-intentioned improvement survives contact with reality. Message [msg 1075] captures a pivotal moment in the optimization of the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep). In this message, the assistant performs a single perf stat measurement after reverting a failed optimization—the SmallVec (A1) change—and collects the baseline Vec data that will definitively prove why the optimization was wrong. This message is the fulcrum on which the entire Phase 4 optimization strategy pivots, transforming a misguided cache-miss hypothesis into a deeper understanding of instruction-level parallelism on AMD Zen4.

The Message

The message reads in full:

[assistant] Build succeeded. Now run perf stat with Vec — two runs:

``bash perf stat -e instructions,cycles,cache-references,cache-misses,branch-instructions,branch-misses,l2_cache_req_stat.dc_access_in_l2,l2_cache_req_stat.dc_hit_in_l2,ls_dmnd_fills_from_sys.local_l2,ls_dmnd_fills_from_sys.local_ccx,ls_any_fills_from_sys.dram_io_all -- env FIL_PROOFS_PARAMETER_CACHE=/data/zk/params /home/theuser/curio/extern/cuzk/target/release/cuzk-bench synth-only --c1 /data/32gbench/c1.json --partition 0 -i 1 2>&1 | tee /tmp/perf-vec-run1.txt ``

``` === Synthesis Microbenchmark === c1: /data/32gbench/c1.json sector: 1 (miner 1000) partition: 0 iterations: 1

c1 loaded: 51510727 bytes synthesis time: 55.427027023s ```

At first glance, this appears to be a routine benchmark run. But in context, it is the decisive measurement that will expose the flaw in an entire optimization strategy and redirect the project's focus toward the true bottleneck.

Context: The SmallVec Regression Investigation

To understand why this message matters, one must trace back through the preceding conversation. The assistant had been working through a list of optimizations codified in a document called c2-optimization-proposal-4.md. One of these, labeled A1, proposed replacing Vec<LinearCombinationTerm> with SmallVec<[LinearCombinationTerm; 2]> in the LinearCombination data structure used throughout the bellpepper-core constraint system library. The rationale was intuitive: most linear combinations in the circuit synthesis contain only 1–2 terms, so storing them inline on the stack (SmallVec) rather than heap-allocating (Vec) should reduce cache misses and allocation overhead.

The initial microbenchmarks (see [msg 1050]) told a different story: SmallVec was slower than Vec on Zen4 in all configurations. The assistant hypothesized that Zen4's aggressive out-of-order execution and large caches might make Vec's simpler code path win despite its heap traffic. But the hypothesis needed hardware-counter evidence to be convincing—hence the perf stat comparison.

Messages [msg 1064] and [msg 1065] captured the SmallVec cap=2 baseline under perf stat, showing synthesis times of ~55.8s and ~57.1s. Then, in messages [msg 1066] through [msg 1073], the assistant meticulously reverted the SmallVec changes: editing lc.rs to replace SmallVec with Vec, removing the smallvec dependency from Cargo.toml, and rebuilding the cuzk-bench binary. Message [msg 1074] confirms the rebuild succeeded.

Why This Message Was Written

Message [msg 1075] was written to collect the Vec baseline under identical perf stat conditions. The reasoning is methodical and scientific:

Controlled comparison: The assistant had already collected two SmallVec runs. Now it needed two Vec runs under the exact same benchmark command, on the same machine, with the same C1 input file, to produce a fair A/B comparison.
Hardware counter validation: The previous microbenchmarks (which only measured wall-clock time) showed SmallVec was slower, but the assistant wanted to understand why at the microarchitectural level. The carefully selected perf stat events—instructions, cycles, cache misses, L2 data cache accesses, L1D fill sources—were chosen to test the cache-miss hypothesis directly.
Evidence-based decision making: Rather than simply accepting the wall-clock regression and moving on, the assistant sought to build a rigorous understanding. If SmallVec reduced cache misses but still lost on wall time, that would reveal something fundamental about the workload's behavior on Zen4—knowledge that would inform future optimization decisions.
Scientific rigor: Running two measurements per configuration (SmallVec runs 1 and 2, Vec runs 1 and 2) provides some defense against measurement noise. The assistant even notes the consistency: SmallVec at 55.8s and 57.1s, Vec at 55.4s.

Assumptions Being Tested

This message implicitly tests several assumptions:

Assumption 1: Cache misses are the primary bottleneck. The entire SmallVec optimization was predicated on the idea that reducing cache pressure would improve performance. The perf stat events were chosen specifically to measure L2 hits, L1D fills from L2 vs L3/CCX vs DRAM—all cache hierarchy metrics.

Assumption 2: SmallVec's inline storage reduces cache misses. This is SmallVec's core value proposition: by storing small arrays on the stack rather than heap, you avoid the cache misses associated with chasing heap pointers.

Assumption 3: Reduced cache misses translates to faster execution. Even if Assumption 2 holds, the question is whether the cache miss reduction is large enough to offset any downsides of SmallVec's more complex code paths (discriminant checks, size-dependent branching, potential stack frame growth).

Assumption 4: Zen4 behaves similarly to other architectures. The optimization proposal was likely written with Intel architectures in mind, where cache miss penalties are higher and SmallVec often wins. Zen4's large L3 cache and aggressive prefetchers change the tradeoff.

Input Knowledge Required

To fully understand this message, one needs:

Knowledge of the SUPRASEAL_C2 pipeline: The synth-only subcommand runs only the CPU synthesis step—circuit construction and R1CS witness generation—without GPU, daemon, or SRS loading. This isolates the CPU-bound portion of the proof generation.
Understanding of LinearCombination and SmallVec: The A1 optimization targeted LinearCombination, which represents a weighted sum of variables in the constraint system. Most LCs have 1–2 terms, making SmallVec seem attractive. The SmallVec<[T; 2]> type stores up to 2 elements inline on the stack and spills to heap only for larger collections.
Familiarity with perf stat and AMD Zen4 PMU events: The selected events are not generic Linux counters but AMD-specific Performance Monitoring Unit events. l2_cache_req_stat.dc_access_in_l2 tracks L2 data cache accesses; ls_dmnd_fills_from_sys.local_l2 tracks L1D demand fills sourced from L2; ls_dmnd_fills_from_sys.local_ccx tracks fills from L3 within the same CCX (Core Complex); ls_any_fills_from_sys.dram_io_all tracks DRAM fills. These fine-grained counters allow pinpointing exactly where data is being sourced from in the cache hierarchy.
Context of the broader optimization effort: This is Phase 4 of a multi-phase project to reduce memory and improve throughput of the SUPRASEAL_C2 Groth16 prover. Previous phases implemented pipelined synthesis, async overlap, and cross-sector batching. Phase 4 targets compute-level micro-optimizations.

Output Knowledge Created

This message produces several forms of knowledge:

Immediate output: The raw perf stat data for Vec run 1, saved to /tmp/perf-vec-run1.txt. This includes synthesis time (55.43s) and all hardware counter values.

Derived knowledge (in subsequent messages): When combined with the SmallVec data and the second Vec run ([msg 1076]), the assistant will produce a comparison table ([msg 1078]) that reveals the counterintuitive truth: SmallVec reduces cache misses at every level (L2, L3, DRAM) and executes 7% fewer instructions, yet takes 1.6% more cycles because IPC drops from 2.60 to 2.38—an 8.5% regression in instruction-level parallelism. The root cause is SmallVec's more complex control flow (enum discriminant checks, size-dependent branching) defeating Zen4's out-of-order execution window.

Methodological knowledge: The assistant learns that on Zen4 with jemalloc, Vec's simpler instruction stream yields higher IPC that more than compensates for its extra cache misses. This is a valuable architectural insight that will inform future optimization choices—not just for this project but for any high-performance Rust workload on AMD Zen4.

Decision knowledge: The A1 optimization is definitively cancelled. The assistant updates the todo list to mark the SmallVec investigation as complete and moves on to the final E2E test with only the beneficial changes (A4 parallel B_G2, D4 per-MSM window tuning, max_num_circuits=30).

The Thinking Process Visible in the Message

The assistant's reasoning is visible in several dimensions:

Experimental design: The assistant runs two measurements per configuration, uses the same binary and input, and captures the same set of hardware counters. This shows deliberate scientific methodology.

Event selection: The choice of AMD-specific PMU events reveals deep knowledge of the Zen4 microarchitecture. The assistant had to probe available events (messages [msg 1053], <msg id=1055-1063>) to find working counter names, then selected events that trace the full cache hierarchy path: L2 accesses → L2 hits → L1D fills from L2 → L1D fills from L3/CCX → DRAM fills. This is a sophisticated diagnostic setup.

Tool mastery: The assistant uses perf stat with a complex event list, pipes output to tee for persistence, and sets environment variables (FIL_PROOFS_PARAMETER_CACHE) correctly. The command is carefully constructed to avoid event multiplexing issues.

Sequencing: The assistant first collects SmallVec data (messages <msg id=1064-1065>), then reverts the code (<msg id=1066-1073>), rebuilds ([msg 1074]), and immediately collects Vec data ([msg 1075]). This minimizes the time between measurements, reducing the chance of confounding factors like system thermal state or background process activity.

Mistakes and Incorrect Assumptions

The primary mistake visible in this message is not in the message itself but in the optimization it is measuring. The SmallVec (A1) optimization was based on an incorrect assumption about the workload's bottleneck. The assistant assumed that cache misses from heap-allocated Vecs were a significant performance limiter, when in fact the true bottleneck was elsewhere—temporary LinearCombination allocations inside closures (as the subsequent chunk analysis reveals).

However, the assistant's response to this mistake is exemplary: rather than forcing the optimization to work or ignoring the regression, the assistant invests in understanding the root cause through detailed hardware counter analysis. This turns a failed optimization into a valuable architectural insight.

One could argue the assistant should have profiled the workload before proposing optimizations, rather than after. But in practice, optimization work often proceeds in parallel—propose, implement, measure, and the measurement phase reveals which proposals survive.

Conclusion

Message [msg 1075] is a quiet but crucial moment in a larger optimization narrative. On its surface, it is a routine benchmark command and its output. In context, it is the decisive measurement that proves a hypothesis wrong, cancels an optimization, and deepens the team's understanding of their workload's behavior on modern hardware. The message exemplifies the scientific method in systems optimization: form a hypothesis, implement a change, measure rigorously, and let the data speak—even when it contradicts your expectations. The SmallVec regression, far from being a wasted effort, produced knowledge about Zen4's instruction-level parallelism characteristics that will inform every subsequent optimization decision in this project and beyond.

The Pivot Point: How a Single perf stat Measurement Decisively Cancelled a Misguided Optimization