The Art of the Controlled Revert: Isolating Performance Regressions in GPU Proof Synthesis

In the high-stakes world of optimizing Groth16 proof generation for Filecoin's Proof-of-Replication (PoRep), every microsecond counts. The synthesis phase — where circuit constraints are evaluated to produce the A, B, and C polynomials — consumes tens of seconds even on modern hardware, and the optimization team at Curio had been systematically working through a pipeline of improvements. But when a carefully crafted set of three optimizations delivered only a 1% speedup instead of the expected 15–25%, the assistant faced a diagnostic puzzle. Message [msg 1157] captures the moment of scientific backtracking: the restoration of a function that had been replaced, as part of an A/B test to isolate the cause of a puzzling IPC regression.

The Message

The subject message is deceptively simple:

[assistant] And restore eval_with_trackers in bellperson's lc.rs: [edit] /home/theuser/curio/extern/bellperson/src/lc.rs Edit applied successfully.

This is the second half of a two-part revert. In the preceding message [msg 1156], the assistant had already modified prover/mod.rs to switch from the new eval_ab_interleaved function back to separate calls to eval_with_trackers. Now, in message [msg 1157], the assistant restores the eval_with_trackers function definition itself in bellperson/src/lc.rs — a function that had been removed or superseded when the interleaved eval was introduced. Together, these two edits constitute a controlled rollback of one optimization while preserving the other two.

The Backstory: Three Optimizations, Disappointing Results

To understand why this revert was necessary, we must trace the chain of reasoning that led to it. The assistant had previously identified, through perf stat profiling, that the synthesis hot loop spent approximately 34% of its runtime on jemalloc allocation and deallocation. This led to the design of three optimizations [msg 1129]:

  1. Vec recycling pool (arena allocator): Instead of creating fresh Vec buffers for every LinearCombination inside the enforce method, the assistant added a VecPool to ProvingAssignment that reuses six pre-allocated vectors per constraint evaluation. This required new methods on LinearCombination — zero_recycled, from_coeff_recycled, and recycle — implemented in bellpepper-core <msg id=1130–1132>.
  2. Interleaved A+B eval: The assistant hypothesized that evaluating the A and B linear combinations in a single interleaved loop would improve instruction-level parallelism (ILP) by keeping the CPU's execution units busier. This was implemented as eval_ab_interleaved in bellperson's lc.rs [msg 1144].
  3. Software prefetch: _mm_prefetch intrinsics were added to the inner loops of eval and eval_with_trackers to reduce cache miss latency <msg id=1140–1142>. The implementation compiled cleanly <msg id=1150–1152>, and the assistant ran a synth-only microbenchmark on a 32 GiB PoRep C1 circuit [msg 1154]. The result: an average of 55.0 seconds versus a baseline of 55.5 seconds — a mere 0.7–1.1% improvement. This was far below expectations.

The Diagnostic: perf stat Reveals the Culprit

The assistant did not accept the disappointing result at face value. Instead, they ran perf stat with a comprehensive set of hardware counters <msg id=1155–1156> to understand what was happening at the CPU level. The data told a nuanced story:

| Counter | Baseline | Optimized | Delta | |---|---|---|---| | Instructions | 593.8B | 569.6B | −4.1% | | Cycles | 228.4B | 225.5B | −1.3% | | IPC | 2.60 | 2.53 | −2.7% worse | | Branch instructions | 69.8B | 62.5B | −10.4% | | L3/CCX fills | 122.5M | 133.4M | +8.9% |

The recycling pool was working: instructions dropped by 4.1%, and branch instructions dropped by a dramatic 10.4% as jemalloc code paths were eliminated. But the instructions-per-cycle (IPC) ratio fell from 2.60 to 2.53, meaning each instruction took relatively more cycles to execute. The L3 cache fills also increased by 8.9%, suggesting worse memory access patterns.

The assistant's analysis was precise: "The IPC regression suggests the interleaved A+B eval might be hurting rather than helping — the interleaved loop has more complex control flow (min/max calculations, two sets of prefetch, etc.) that could be confusing the branch predictor and defeating the CPU's ability to keep its pipeline full."

The Scientific Method in Action

Message [msg 1157] represents the critical next step: isolating variables. The assistant had three changes in flight, and the perf stat data suggested that one of them — the interleaved eval — was actively harmful. But correlation is not causation. The only way to confirm was to revert the interleaved eval while keeping the recycling pool and prefetch intact, then measure again.

This is the essence of the scientific method applied to systems optimization: form a hypothesis, implement, measure, analyze, and iterate. The revert is not an admission of failure but a controlled experiment. By reverting only the interleaved eval, the assistant creates a clean A/B test: configuration A (recycling + prefetch + interleaved eval) versus configuration B (recycling + prefetch + sequential eval). If configuration B restores the IPC to 2.60 while keeping the instruction count reduction, the hypothesis is confirmed.

Assumptions Made and Challenged

The assistant made several assumptions during the design phase that were challenged by the data:

Assumption 1: Interleaving improves ILP. The intuition was sound — processing A and B terms in a combined loop should keep the CPU's arithmetic units busier. But the implementation introduced complex control flow (min/max calculations across two lists of terms, dual prefetch streams) that apparently confused the branch predictor more than it helped the execution units. The IPC drop from 2.60 to 2.53 suggests the CPU's pipeline was stalling more frequently.

Assumption 2: The recycling pool would dominate the speedup. The assistant expected the arena allocator to be the biggest win, and the instruction count reduction confirmed it was working. But the overall speedup was only 1%, suggesting that the allocation overhead, while significant at 34% of runtime, was not the bottleneck it appeared to be — or that the savings were being offset by regressions elsewhere.

Assumption 3: Prefetch would help unconditionally. Software prefetch is notoriously tricky: it helps only when the prefetch distance is correctly tuned and the memory access pattern is predictable. In this case, the prefetch may have been issuing hints too early or too late, or the hardware prefetcher was already doing a good enough job.

Input Knowledge Required

To understand this message, one needs knowledge of:

Output Knowledge Created

This message creates:

The Broader Significance

Message [msg 1157] is a small edit — a single file change restoring a function that had been replaced. But it represents something larger: the discipline of measurement-driven optimization. In a field where intuition often leads to wasted effort, the assistant's willingness to revert a promising-sounding optimization and isolate variables is a model of engineering rigor.

The story does not end here. In the subsequent chunk ([chunk 14.1]), the assistant would go deeper, using perf to discover that the true bottleneck was not the six Vecs per enforce call but the dozens of temporary LinearCombination objects created inside closures by Boolean::lc(), UInt32::addmany, and SHA-256 gadgets. This led to a more targeted optimization: adding add_to_lc and sub_from_lc methods to Boolean that eliminate temporary allocations at the hottest call sites. The revert in message [msg 1157] was the necessary precondition for that discovery — without isolating the interleaved eval as a confound, the true bottleneck might have remained hidden.

In the end, the controlled revert is not a step backward. It is a step sideways, clearing away a false lead so the real path forward becomes visible.