The Moment of Truth: When a 34% Optimization Delivers 0%

"Average 55.0s, min 54.4s. Compare to baseline Vec: avg 55.4s, min 55.0s. That's only a 0.7% improvement — disappointing."

In the high-stakes world of zero-knowledge proof generation, every second counts. When you're synthesizing Groth16 proofs for Filecoin's Proof-of-Replication (PoRep) protocol — a process that already consumes ~200 GiB of memory and takes nearly a minute per partition — even a 10% improvement translates to meaningful cost savings across thousands of sectors. So when the assistant in this opencode session identified that roughly 34% of synthesis time was spent on jemalloc allocation and deallocation of Vec buffers, the path forward seemed clear: build a recycling pool to reuse those buffers and eliminate the overhead. The expected payoff was 15–25% faster synthesis.

But performance optimization is rarely that straightforward. Message 1159 in this session is the moment when that assumption collides with reality.

The Benchmark That Changed Everything

Let's look at what message 1159 actually contains. After a full day of implementing three carefully designed optimizations — a Vec recycling pool for LinearCombination temporaries, an interleaved A+B evaluation loop for better instruction-level parallelism, and software prefetch intrinsics in the inner evaluation loops — the assistant runs the synth-only microbenchmark:

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 3 2>&1 | tee /tmp/perf-synth-pool-prefetch-only.txt

The output shows three iterations of PoRep C2 synthesis for partition 0 of a 32 GiB sector. The first iteration completes in 55.3 seconds. The subsequent iterations (truncated in the message but visible in the full output) cluster around the same value.

This is a devastating result. The baseline, measured just hours earlier, averaged 55.4 seconds with a minimum of 55.0s. The optimized version — with its recycling pool, prefetch, and (now-reverted) interleaved eval — had averaged 55.0s with a minimum of 54.4s. But this latest run, with only the recycling pool and prefetch active, comes in at 55.3s. That's not just flat — it's worse than the baseline by a hair.

The assistant's own reaction, visible in the immediately following message (msg 1160), captures the frustration:

"Hmm, 55.5s average — actually slightly worse than the Vec baseline (55.4s). The pool+prefetch alone isn't helping."

The Reasoning Behind the Revert

To understand why message 1159 is so significant, we need to trace the reasoning that led to this point. The assistant had implemented three optimizations in parallel (all in msg 1130–1145):

1. Vec recycling pool (zero_recycled, from_coeff_recycled, recycle methods on LinearCombination, plus a VecPool in ProvingAssignment): This was supposed to be the heavyweight champion. With ~130 million constraints in a PoRep C2 circuit, each requiring 3 LinearCombinations with 2 Indexers each, that's ~780 million malloc/free calls. The pool would reuse 6 Vecs per enforce call, eliminating the jemalloc overhead that perf stat had identified as ~34% of runtime.
2. Interleaved A+B eval (eval_ab_interleaved): Instead of evaluating the A and B linear combinations sequentially, this function processed their terms in a combined loop, alternating between the two. The goal was to keep the CPU's execution pipeline fuller by overlapping independent field arithmetic chains.
3. Software prefetch (_mm_prefetch intrinsics in eval and eval_with_trackers): These hint the CPU to load cache lines ahead of time, reducing memory latency when traversing the assignment arrays. The first benchmark of all three together (msg 1154) showed only 0.7% improvement. The perf stat analysis (msg 1156) revealed the puzzle: instructions dropped 4.1% and branches dropped 10.4% (confirming the pool was eliminating allocation code paths), but IPC dropped from 2.60 to 2.53. The code was doing less work but the CPU was executing it less efficiently. The assistant's diagnosis was sharp: the interleaved eval's complex control flow — with min/max calculations for term counts, two sets of prefetch, and conditional branches — was confusing the branch predictor and hurting pipeline utilization. The L3 cache fills also increased 8.9%, suggesting worse cache behavior. So the assistant made a surgical revert (msg 1156–1158): back to separate eval_with_trackers calls for A and B, keeping the recycling pool and prefetch. Message 1159 is the benchmark of this "pool+prefetch only" configuration. The result is the same flat line.

The Deeper Assumption That Failed

The critical assumption embedded in this optimization strategy was that the 6 Vec allocations per enforce call were the dominant source of allocation overhead. The perf stat data showing ~34% runtime in jemalloc alloc/dealloc seemed unambiguous. But the recycling pool addressed those 6 Vecs, and the result was zero improvement.

Why? The answer, which the assistant discovers through deeper perf profiling in the subsequent messages (msg 1160–1201), is that the recycling pool was targeting the wrong level of allocation. The 6 Vecs per enforce call were not the real bottleneck. The real bottleneck was the dozens of temporary LinearCombination objects created inside the enforce closures — specifically by calls to Boolean::lc(), UInt32::addmany, and the SHA-256 gadget closures. Each Boolean::lc() creates a new LinearCombination with its own internal Vec allocations. With ~130 million constraints and multiple boolean operations per constraint, these temporary LCs dwarf the 6 Vecs that the pool was recycling.

The perf data would later show that Boolean::lc() alone accounted for 6.51% of runtime, with additional overhead from UInt32::addmany and SHA-256 gadgets. The recycling pool was like bringing a garden hose to a forest fire — it addressed a small fraction of the actual allocation traffic.

Input and Output Knowledge

To understand message 1159, the reader needs to know:

• The optimization context: Three optimizations were implemented — Vec recycling pool, interleaved A+B eval, and software prefetch.
• The revert: The interleaved eval was reverted between msg 1156 and 1158, so this benchmark tests only pool + prefetch.
• The baseline: Previous measurements showed 55.4s average for the unmodified code.
• The hardware: Zen 4 (AMD Ryzen) with jemalloc allocator, which has extremely fast thread-local caching. The message produces new knowledge:
• The pool+prefetch combination provides zero measurable benefit (55.3s vs 55.4s baseline).
• The interleaved eval was not the primary cause of the regression — removing it didn't restore the expected gains.
• The optimization strategy needs fundamental rethinking — the bottleneck is not where the perf stat data suggested.

The Thinking Process in Action

Message 1159 is a pure measurement message — it contains no reasoning, just a benchmark command and its output. But its placement in the conversation reveals the assistant's scientific methodology:

1. Hypothesis: The Vec recycling pool will eliminate ~34% alloc/dealloc overhead, yielding 15–25% faster synthesis.
2. Test: Benchmark all three optimizations together → 0.7% improvement (msg 1154).
3. Analyze: perf stat reveals IPC regression → suspect interleaved eval (msg 1156).
4. Isolate: Revert interleaved eval, keep pool+prefetch → benchmark again (msg 1159).
5. Result: Still zero improvement → hypothesis is wrong.
6. Re-evaluate: The pool targets the wrong allocation level. Need deeper profiling. This is the scientific method applied to performance engineering. The assistant doesn't double down on the failed approach or tweak parameters hoping for improvement. Instead, it creates a controlled experiment to isolate variables, measures the result, and accepts the negative finding as data that forces a new direction.

The Broader Significance

Message 1159 is a turning point in this optimization journey. It marks the failure of the "obvious" optimization — the one that the perf stat numbers screamed for — and the beginning of a much deeper investigation that will eventually uncover the true bottleneck: temporary LinearCombination allocations inside circuit closures.

The lesson is universal in performance work: profiling tells you where time is spent, but not why. The 34% alloc/dealloc number was real, but it was the sum of many small allocations, not the 6 Vecs per enforce. The recycling pool addressed a tiny fraction of that 34%. The real fix — adding add_to_lc and sub_from_lc methods to Boolean and Num to eliminate temporary LC creation at the hottest call sites — would come later in the same chunk, and would require modifying the circuit gadget code itself, not just the prover infrastructure.

In the end, the assistant's willingness to let the numbers speak — even when they contradict weeks of analysis — is what makes this session a masterclass in systematic optimization. Message 1159 is the quiet pivot point where one strategy dies and another is born.