Chasing Cache Misses: Diagnosing a SmallVec Performance Regression on AMD Zen4

Introduction

In the high-stakes world of zero-knowledge proof generation, every microsecond counts. When the team behind cuzk — a pipelined Groth16 proving engine for Filecoin's Proof-of-Replication (PoRep) — set out to optimize CPU synthesis performance, they expected straightforward wins from well-known techniques like using SmallVec instead of Vec for small inline allocations. Instead, they encountered a puzzling regression: the SmallVec optimization (dubbed "A1") was slower than the original Vec implementation on their AMD Zen4 target hardware. Message 1064 captures the moment when the assistant pivots from assumption-driven optimization to data-driven diagnosis, deploying hardware performance counters via perf stat to uncover the root cause.

The Context: Phase 4 Optimizations Hit a Wall

The broader session had been systematically working through a series of optimization proposals for the cuzk proving engine. Phase 1 and 2 had already delivered impressive throughput improvements through pipelining and batch-mode synthesis. Phase 3 added cross-sector batching, achieving a 1.46× throughput improvement. Now in Phase 4, the focus had shifted to compute-level micro-optimizations.

Four optimizations were on the table:

• A1: Replace Vec with SmallVec in bellpepper-core's LinearCombination indexer, using an inline capacity of 2
• A2: Pre-size proving assignments to avoid reallocation
• A4: Parallelize B_G2 CPU MSMs
• D4: Per-MSM window size tuning
• B1: Pin a/b/c vectors with cudaHostRegister Early microbenchmarks had already shown something alarming: the SmallVec optimization (A1) was consistently slower than plain Vec across all tested configurations (capacities 1, 2, and 4). This was counterintuitive — SmallVec is designed precisely for small collections where heap allocation overhead dominates. On a modern Zen4 server, the conventional wisdom said this should be a win. The assistant's hypothesis was that Zen4's aggressive prefetching and cache hierarchy might interact poorly with SmallVec's indirection pattern. SmallVec stores a small inline buffer but still needs to check at runtime whether it's using the inline storage or spilled to heap — a branch that Vec doesn't need. Perhaps the extra branch mispredictions and cache pressure from the inline storage were negating any allocation savings.

The Message: Designing a Hardware Counter Experiment

Message 1064 shows the assistant executing the first step of this diagnostic investigation. After several failed attempts to find working perf events on the AMD Zen4 system (messages 1055–1063 show the trial-and-error process of discovering which PMU events are available), the assistant settles on a viable set of counters:

Good — ls_dmnd_fills_from_sys and ls_any_fills_from_sys track where demand/any fills come from. I'll use those along with basic hardware counters. Let me stick to events that actually work:

The reasoning here reveals a deep understanding of both the hardware and the diagnostic tools. The assistant knows that to explain why SmallVec is slower, it needs to look beyond wall-clock time and examine microarchitectural behavior. The chosen event set is carefully constructed:

• instructions and cycles: To compute Instructions Per Cycle (IPC), the fundamental metric of pipeline efficiency
• cache-references and cache-misses: Generic last-level cache metrics
• branch-instructions and branch-misses: To test the hypothesis that SmallVec's inline-vs-heap branch is mispredicting
• l2_cache_req_stat.dc_access_in_l2 and l2_cache_req_stat.dc_hit_in_l2: Zen4-specific L2 data cache access/hit counters, giving L2 hit rate
• ls_dmnd_fills_from_sys.local_l2 and ls_dmnd_fills_from_sys.local_ccx: Demand fills from L2 and local CCX (the Zen4 chiplet complex), indicating how often data must be fetched from further away in the cache hierarchy
• ls_any_fills_from_sys.dram_io_all: Any fills from DRAM — the ultimate cache miss penalty This is not a random grab-bag of events. Each counter targets a specific hypothesis about where SmallVec might be hurting. The L2 counters test whether SmallVec's inline storage is causing more L2 accesses (because the inline data is stored in the SmallVec struct itself, which lives on the stack or in another data structure, potentially increasing cache footprint). The branch counters test the control-flow overhead. The DRAM fill counter tests whether SmallVec is causing more expensive off-chip memory traffic.

The Execution: Running the Benchmark

The assistant then executes the benchmark command:

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

The benchmark uses the synth-only subcommand, which runs only the CPU synthesis step (circuit construction + R1CS witness generation) without GPU involvement, SRS loading, or daemon overhead. This isolates the effect of the A1 optimization from other variables. The --partition 0 -i 1 flags run a single partition once, producing a ~55-second run — long enough for stable measurements but short enough for rapid iteration.

The output shows a synthesis time of 55.839716055 seconds for the SmallVec cap=2 configuration. This is the baseline measurement against which the Vec configuration will be compared after reverting A1.

The Significance: Beyond Wall-Clock Time

What makes message 1064 significant is not the raw number — 55.84 seconds is just a data point. The significance lies in the methodology. The assistant has recognized that optimization is not a linear process of "apply change, measure speedup, commit." It is an iterative cycle of hypothesis formation, experiment design, measurement, and refinement.

The assistant's thinking process, visible in the reasoning sections, shows several key insights:

1. Hardware-aware diagnosis: The assistant understands that software performance cannot be divorced from microarchitecture. A change that reduces allocations might increase branch mispredictions or cache pressure, and on a specific CPU like AMD Zen4 with its large caches and aggressive prefetchers, the tradeoffs can differ from what benchmarks on other architectures suggest.
2. Event selection as experimental design: Choosing which perf events to measure is an act of experimental design. The assistant doesn't just measure "cache-misses" generically — it targets L2 data cache behavior, demand fill sources, and branch prediction, each testing a specific theory about SmallVec's overhead.
3. Iterative refinement: The message follows a trail of failed event syntax attempts (messages 1055–1063), showing the assistant adapting to the constraints of the actual hardware. The AMD Zen4 PMU has specific event naming conventions, and not all events are available. The assistant works through these constraints systematically rather than giving up.

The Broader Narrative: Optimization as Detective Work

This message sits at a turning point in the Phase 4 optimization effort. The team had assumed that SmallVec would be a clear win — it's a well-known optimization, recommended by the Rust performance community, and it reduces heap allocations. But the data contradicted the assumption. Message 1064 represents the shift from implementing optimizations based on general principles to investigating why a specific optimization fails on a specific target.

The assistant's approach embodies a crucial lesson for performance engineering: measure, don't guess. When an optimization that should work doesn't, the response should not be to double down or discard it arbitrarily, but to gather detailed diagnostic data. The perf counters chosen here will reveal whether the regression is due to branch mispredictions, cache misses, or some other microarchitectural effect — and that knowledge will guide not just the decision about A1, but future optimization decisions for this codebase on this hardware.

Conclusion

Message 1064 captures a moment of disciplined performance engineering. Faced with a counterintuitive regression, the assistant designs a targeted hardware counter experiment, selects events that test specific hypotheses, and executes a clean benchmark. The 55.84-second result is not an answer — it's a question, framed in the language of cache hierarchies and branch predictors. The subsequent messages in the session will reveal what the perf data showed and how the team ultimately resolved the SmallVec mystery, but this message stands as a model of how to approach optimization setbacks with rigor and curiosity.