The Revert That Proved a Hypothesis: Rolling Back SmallVec in the cuzk Proving Engine

Introduction

In the course of optimizing a high-performance Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep), a single edit command marks a pivotal moment of intellectual honesty and empirical rigor. The message in question is deceptively simple:

[assistant] Now let me revert to Vec: [edit] /home/theuser/curio/extern/bellpepper-core/src/lc.rs Edit applied successfully.

This is the moment where the assistant rolls back Optimization A1 — the replacement of Vec with SmallVec in the LinearCombination indexer — after a carefully designed perf stat experiment proved the change was counterproductive on the target AMD Zen4 architecture. The message is the culmination of a multi-step investigation that began with a hypothesis, proceeded through microbenchmarking, and concluded with hardware counter analysis that revealed a deeper truth about CPU microarchitecture and optimization strategy.

The Context: Phase 4 Optimization and the SmallVec Hypothesis

The cuzk proving engine is a sophisticated pipeline for generating Groth16 zk-SNARK proofs for Filecoin storage proofs. By Phase 4 of the optimization effort, the project had already achieved substantial throughput improvements through pipeline restructuring (Phase 2) and cross-sector batching (Phase 3). Phase 4 targeted compute-level micro-optimizations, guided by a detailed proposal document (c2-optimization-proposal-4.md) that identified several promising avenues.

Optimization A1 proposed replacing Vec<(usize, T)> with SmallVec<[(usize, T); INDEXER_INLINE_CAP]> in the LinearCombination (LC) indexer within bellpepper-core/src/lc.rs. The LinearCombination type is a core data structure in the constraint system synthesis — it represents a weighted sum of variables and is constructed and manipulated millions of times during circuit synthesis. Each LinearCombination contains a vector of (variable_index, coefficient) pairs.

The hypothesis was straightforward: most LinearCombination instances in practice contain only a small number of terms (often 1 or 2). By using SmallVec with an inline capacity of 2, the data would be stored on the stack rather than heap-allocated, reducing cache pressure, allocation overhead, and memory bandwidth. The SmallVec crate from the Rust ecosystem provides exactly this optimization — a Vec-like interface backed by a fixed-size inline array for small sizes, falling back to heap allocation only when the capacity is exceeded.

Early microbenchmarks using a synth-only subcommand (which runs only the CPU synthesis step without GPU or SRS loading) showed something puzzling: SmallVec was consistently slower than Vec across all configurations tested (cap=1, cap=2, cap=4). This contradicted the intuitive expectation that inline storage should be faster. The assistant resolved to gather definitive evidence using hardware performance counters via perf stat.

The Investigation: Designing a Controlled Experiment

The assistant designed a rigorous A/B test. The synth-only microbenchmark was configured to run a single partition of a PoRep C2 synthesis job (using --partition 0 -i 1), producing runs of approximately 55–57 seconds — long enough for stable measurement, short enough for iteration. The perf stat command was configured with a carefully chosen set of hardware events:

• Instructions and cycles — for IPC calculation
• Cache-references and cache-misses — for LLC (L3) behavior
• Branch-instructions and branch-misses — to detect front-end issues
• L2 data cache accesses and hits — for L2 locality
• Demand fills from L2, L3/CCX, and DRAM — to trace the memory hierarchy Notably, the assistant encountered difficulty with AMD-specific PMU events. The l3_cache_accesses and l3_misses events were not available on this kernel, requiring fallback to ls_dmnd_fills_from_sys events that track where cache line fills originate. This is a common challenge when profiling on AMD platforms — the event naming and availability differ from Intel, and kernel configuration affects which events are exposed. Two runs were collected per configuration (SmallVec cap=2 and Vec), providing a total of four data points. The SmallVec baseline runs yielded synthesis times of 55.84s and 57.10s (average ~56.5s), while the Vec runs yielded 55.43s and 55.51s (average ~55.5s). The raw timing already showed SmallVec trailing by about 1.8%.

The Counterintuitive Result: Fewer Cache Misses, Worse Performance

The perf stat data revealed a fascinating and counterintuitive story. SmallVec achieved fewer cache misses at every level of the hierarchy:

• LLC cache-misses: 107.6M (SmallVec) vs 148.4M (Vec) — a 27.5% reduction
• L1D demand fills from L2: 1.37B vs 2.23B — a 38.4% reduction
• L1D demand fills from L3/CCX: 50.7M vs 122.5M — a 58.6% reduction
• DRAM fills: 5.2M vs 6.4M — a 17.5% reduction By any memory-centric metric, SmallVec was winning. The inline storage was keeping data closer to the core, reducing pressure on the memory hierarchy exactly as intended. If cache misses were the primary bottleneck, SmallVec should have been faster. Yet it was slower. The key lay in two other numbers:
• Instructions executed: 552.0B (SmallVec) vs 593.8B (Vec) — SmallVec executed 7% fewer instructions
• Cycles: 232.1B (SmallVec) vs 228.4B (Vec) — SmallVec took 1.6% more cycles The arithmetic is damning: IPC (Instructions Per Cycle) dropped from 2.60 to 2.38 — an 8.5% regression. SmallVec's more complex code path — the enum discriminant checks, the size-dependent branching between inline and heap storage, the larger stack frames — was defeating Zen4's out-of-order execution engine. The CPU simply could not sustain the same degree of instruction-level parallelism when faced with SmallVec's control flow complexity.

The Root Cause: Microarchitecture Trumps Memory Hierarchy

The assistant's analysis correctly identified the root cause: "SmallVec's enum discriminant checks, size-dependent branching, and larger stack frames defeat Zen4's out-of-order execution window. Vec's simple pointer-chasing code is highly predictable and the OOO engine handles the resulting cache misses efficiently by overlapping them."

This is a profound observation about modern CPU microarchitecture. AMD's Zen4 core has a large reorder buffer and sophisticated out-of-order execution capabilities. When faced with Vec's straightforward pointer-chasing pattern — load a pointer, dereference it, process the data — the CPU can overlap multiple memory accesses, hide latency, and keep its execution units busy. The cache misses Vec incurs are tolerable because they are predictable and the CPU can work around them.

SmallVec, by contrast, introduces branchy code. Every access to a SmallVec element requires checking whether the data is stored inline or on the heap, then branching to the appropriate load path. These branches consume front-end bandwidth, occupy branch predictor resources, and create control flow dependencies that constrain the out-of-order engine. The result is lower IPC despite fewer cache misses.

This finding aligns with a known principle in performance engineering: optimizing for cache behavior is not always the right approach when the CPU has strong out-of-order capabilities. Sometimes a simpler instruction stream that generates more cache misses can outperform a more complex one that generates fewer, because the CPU can hide memory latency through parallelism while it cannot hide control flow dependencies.

The Revert: What the Edit Represents

The target message — "Now let me revert to Vec" followed by the edit command — is the practical conclusion of this analysis. It is not an admission of failure but a demonstration of disciplined optimization methodology. The assistant:

1. Formulated a testable hypothesis: SmallVec's inline storage will reduce cache pressure and improve synthesis throughput.
2. Built measurement infrastructure: The synth-only microbenchmark and perf stat command setup.
3. Collected empirical data: Four controlled runs with hardware counters.
4. Analyzed the results: Identified the IPC regression as the root cause.
5. Acted on the evidence: Reverted the change. The edit itself touches bellpepper-core/src/lc.rs, the core data structure file for the constraint system's linear combination type. This file is in a forked dependency (extern/bellpepper-core/) that was created specifically to enable these kinds of optimizations. The revert restores the original Vec-based implementation, removing the smallvec dependency and the SmallVec type alias.

Broader Implications for Optimization Methodology

This episode illustrates several important principles for systems optimization:

First, always measure at multiple levels. A microbenchmark that only reports wall-clock time can tell you that something is slower but not why. Hardware performance counters provide the diagnostic depth needed to understand root causes.

Second, cache misses are not the only metric. The conventional wisdom that "fewer cache misses = faster" is an oversimplification. Modern CPUs are remarkably good at hiding memory latency through prefetching, out-of-order execution, and simultaneous multithreading. Control flow complexity can be a more insidious performance killer because it attacks the front-end of the pipeline where there is less redundancy.

Third, architecture-specific optimization requires architecture-specific testing. SmallVec might well be faster on Intel cores with different branch predictor designs or smaller reorder buffers. The fact that it regressed on Zen4 does not mean it is universally bad — it means it is wrong for this target.

Fourth, the courage to revert is as important as the creativity to try. The assistant had invested effort in implementing A1, running microbenchmarks, and collecting perf stat data. Reverting that work requires intellectual honesty and a commitment to data-driven decision-making. The message "Now let me revert to Vec" is spoken without hesitation or defensiveness — the data has spoken, and the response is action.

Conclusion

The edit that reverted SmallVec back to Vec in bellpepper-core/src/lc.rs is a small change in terms of lines of code but a significant one in terms of what it represents. It is the moment when a carefully constructed hypothesis met empirical reality and was found wanting. The investigation that led to this revert — spanning microbenchmark construction, perf event discovery, AMD PMU troubleshooting, and IPC analysis — demonstrates the depth of analysis required for serious performance engineering.

The cuzk proving engine would go on to incorporate other Phase 4 optimizations (A4's parallel B_G2 MSM, D4's per-MSM window tuning, and the max_num_circuits=30 adjustment), but A1 was definitively cancelled. The lesson endures: in optimization, what you learn from a failed experiment is often more valuable than what you gain from a successful one.