The Smoking Gun: How `perf` Revealed the True Bottleneck in Groth16 Synthesis

"This is the smoking gun. The recycling pool didn't help because..."

In the high-stakes world of Filecoin proof-of-replication (PoRep) proving, every second counts. The cuzk proving engine — a custom fork of the supraseal-c2 Groth16 prover — was being optimized to reduce the ~200 GiB peak memory footprint and improve throughput for Filecoin storage miners. After weeks of architectural work implementing a batch-mode pipeline, async overlap between synthesis and GPU proving, and cross-sector batching, the team had turned their attention to compute-level micro-optimizations. Message 1173 represents a critical inflection point in that journey: the moment when a carefully crafted optimization strategy based on a flawed mental model was decisively refuted by empirical data, forcing a fundamental rethinking of where the CPU cycles were actually going.

The Context: A Three-Pronged Optimization Attack

The story begins in the previous chunk (chunk 0 of segment 14), where the assistant implemented three synthesis optimizations derived from earlier perf stat analysis. The first was a Vec recycling pool — an arena allocator designed to eliminate the estimated ~34% of runtime spent on jemalloc allocation and deallocation in the enforce hot loop. The reasoning was sound on paper: each enforce call creates three LinearCombination objects, each containing two Indexer vectors (one for inputs, one for aux), totaling six heap-allocated Vecs per constraint. With approximately 130 million constraints in a Filecoin PoRep C2 proof, that's roughly 780 million malloc/free calls. The recycling pool would reuse these six Vecs across consecutive enforce calls, replacing heap operations with cheap Vec::clear() and Vec::pop() calls.

The second optimization was interleaved A+B evaluation — a batched eval loop that processed the A and B linear combination terms together in a single pass, theoretically improving instruction-level parallelism by keeping the CPU pipeline fuller. The third was software prefetch via _mm_prefetch intrinsics inserted into the inner loops of eval and eval_with_trackers, intended to reduce cache miss latency by hinting to the CPU about upcoming memory accesses.

The assistant's confidence was high. The estimated savings from the recycling pool alone were 15–25% of synthesis time. The interleaved eval and prefetch would add further gains. The stage was set for a decisive victory.

The Disappointment: When Theory Meets Reality

The microbenchmark results were sobering. The optimized code averaged 55.0 seconds versus a baseline of 55.4 seconds — a mere 0.7% improvement. The recycling pool, expected to save 7–8 seconds, had delivered virtually nothing. Even more puzzling, the perf stat counters showed a contradictory picture: instructions dropped 4.1% (confirming the pool was eliminating allocation code paths), branches dropped 10.4% (fewer jemalloc code paths), but IPC (instructions per cycle) fell from 2.60 to 2.53. The code was doing less work but taking almost the same amount of time.

The assistant hypothesized that the interleaved A+B eval was the culprit — its more complex control flow (min/max calculations, two sets of prefetch) might be confusing the branch predictor and hurting pipeline utilization. To isolate the effect, the assistant reverted the interleaved eval back to separate eval_with_trackers calls while keeping the recycling pool and prefetch. The result was even worse: 55.5 seconds, slightly slower than the original baseline.

This was the moment of crisis. The recycling pool — the cornerstone of the optimization strategy — was not working. But why?

The Investigation: `perf record` and the Subagent Analysis

Rather than continuing to guess, the assistant deployed the heavy artillery: a full perf record with call-graph profiling, capturing 229,841 samples across the 55-second synthesis run. The resulting perf.data was 3.6 GB. The assistant then spawned a subagent via the task tool to analyze the profile systematically.

The subagent's report (msg 1171) delivered the first clues. The top function by self-time was ProvingAssignment::enforce at 11.14%, followed by Montgomery multiplication (__mulx_mont_sparse_256) at 8.52%. But the critical finding was in the next tier: UInt32::addmany at 6.82% and Boolean::lc() at 6.51%. Crucially, there were zero samples for any pool-related functions (pool, take, give, recycle, zero_recycled). The recycling pool was not even appearing in the profile.

The subagent's analysis revealed the truth: the pool was being bypassed. The circuit code wasn't building on the recycled lc argument passed into the closures — it was creating entirely new LinearCombination objects inside the closures via Boolean::lc(), UInt32::addmany, and SHA-256 gadget code. The six Vecs per enforce call that the pool was designed to recycle were a drop in the bucket compared to the dozens of temporary LCs created per constraint inside the closure bodies.

Message 1173: The Smoking Gun

This brings us to the subject message — message 1173 — where the assistant synthesizes the findings into a clear diagnosis. The message begins with the emphatic declaration: "This is the smoking gun." It then enumerates three key findings:

84% of closures USE the recycled lc — the pool works for these cases, confirming the basic mechanism is functional.
BUT Boolean::lc() is called INSIDE those closures and creates fresh LinearCombination::zero() allocations that the pool cannot recycle. This single function accounts for 6.51% of runtime by itself.
16% of closures IGNORE the passed-in lc entirely (using |_| patterns), wasting the recycled buffer entirely. The assistant then articulates the corrected mental model: "The allocation overhead isn't in the 6 Vecs per enforce call — it's in the dozens of temporary LCs created by Boolean::lc() inside the circuit code. Each UInt32::addmany calls Boolean::lc() 32× per operand. For SHA-256, that's thousands of temporary LC allocations inside a single enforce." This is the critical insight. The original optimization targeted the wrong level of the allocation hierarchy. The six Vecs per enforce call were visible at the enforce level, but the real allocation storm was happening one level deeper, inside the gadget code that builds the linear combinations. The recycling pool was like putting a bucket under a faucet while ignoring the fire hose spraying water everywhere else.

The Pivot: From Recycling to In-Place Operations

The assistant's response to this revelation was swift and decisive. Rather than trying to extend the recycling pool to cover these inner allocations — which would require threading pool references through the entire gadget hierarchy — the assistant proposed a fundamentally different approach: eliminate the temporary allocations entirely by adding methods that operate directly on existing LinearCombination objects.

The plan was to add add_to_lc and sub_from_lc methods to Boolean that directly add or subtract a boolean's term(s) to an existing LinearCombination without creating a temporary LC. Similarly, add_lc would be added to Num. The hottest call sites — UInt32::addmany, Num::add_bool_with_coeff, sha256_ch, sha256_maj, and lookup gadget closures — would be patched to use these new in-place methods.

This represents a significant shift in optimization philosophy. The original approach was reactive: allocate memory, then recycle it. The new approach is proactive: don't allocate in the first place. By adding methods that mutate an existing LC rather than creating a new one, the assistant could eliminate the allocation overhead at its source rather than trying to clean up after it.

Assumptions Made and Lessons Learned

The journey to message 1173 reveals several assumptions that proved incorrect:

Assumption 1: The dominant allocation cost is the six Vecs per enforce call. This was based on a straightforward count: three LCs × two Indexers = six Vecs, times 130M constraints = 780M allocations. But this count missed the allocations happening inside the closures — the temporary LCs created by Boolean::lc(), the intermediate LCs created by UInt32::addmany, and the allocations in SHA-256 gadget code. These inner allocations dwarfed the outer six.

Assumption 2: The circuit closures build on the recycled lc argument. The enforce signature passes a LinearCombination::zero() as the starting point for each closure. The assistant assumed that most closures would use this starting point, building up their constraint by adding terms to it. In reality, 16% of closures ignored the argument entirely (using |_|), and even among the 84% that used it, the closures called Boolean::lc() internally, creating temporary LCs that the pool couldn't recycle.

Assumption 3: jemalloc's thread-local cache is slow enough that recycling saves significant time. On AMD Zen4, jemalloc's tcache can service small allocations in 5–10 nanoseconds. For a Vec with a single element (40 bytes), the allocation cost is dominated by the tcache hit, not the actual heap management. The recycling pool's operations (Vec::pop(), Vec::clear(), Vec::push()) each take ~5 ns, so the savings per operation was much smaller than estimated.

Assumption 4: The interleaved A+B eval would improve IPC. This was a reasonable microarchitectural hypothesis — combining two loops into one should improve cache locality and pipeline utilization. But the more complex control flow (tracking two sets of terms, min/max calculations, dual prefetch) actually hurt IPC, dropping it from 2.60 to 2.53. The branch predictor struggled with the more complex patterns, and the increased L3 fills (up 8.9%) indicated worse cache behavior.

Input Knowledge Required

To fully understand message 1173, the reader needs familiarity with several domains:

Groth16 SNARK proving pipeline: Understanding that enforce is the core constraint-addition method in a Rank-1 Constraint System (RICS), that LinearCombination represents a weighted sum of variables, and that synthesis is the process of converting a circuit description into concrete constraint assignments.

Rust memory management: Understanding Vec allocation, jemalloc's thread-local caching, the cost of malloc/free versus Vec::clear()/Vec::pop(), and how closures capture and mutate state.

CPU microarchitecture: Understanding IPC (instructions per cycle), branch prediction, cache hierarchies (L1, L2, L3), prefetch intrinsics, and how control flow complexity affects pipeline utilization.

Performance profiling with perf: Understanding how perf record with call-graph profiling works, how to interpret flat profiles versus call-chain profiles, and how to identify allocation hotspots.

The Filecoin PoRep circuit structure: Understanding that PoRep C2 synthesis involves ~130M constraints built from SHA-256 gadgets, UInt32::addmany operations, and lookup tables — all of which create temporary LCs.

Output Knowledge Created

Message 1173 creates several valuable pieces of knowledge:

A corrected bottleneck model: The true source of allocation overhead in Groth16 synthesis is not the six Vecs per enforce call but the dozens of temporary LCs created inside gadget closures. This reframes the optimization problem from "recycle the outer LCs" to "eliminate inner LC creations."

A prioritized optimization target: Boolean::lc() at 6.51% of runtime is the single hottest allocation function, followed by the SHA-256 gadget chain. These are now the primary targets for in-place optimization.

A validated methodology: The combination of perf record with call-graph profiling, followed by systematic analysis via a subagent, proved capable of identifying the true bottleneck even when it contradicted the intuitive model. The assistant's willingness to trust the data over their own assumptions is a methodological lesson in itself.

A concrete implementation plan: The add_to_lc/sub_from_lc methods on Boolean and add_lc on Num, plus the list of call sites to patch, form a clear action plan for the next optimization iteration.

The Thinking Process: A Case Study in Debugging

The reasoning visible in message 1173 and the preceding messages (1155–1172) is a masterclass in systematic debugging. The assistant follows a clear scientific method:

Form a hypothesis: The recycling pool will save 15–25% of synthesis time.
Test the hypothesis: Run the microbenchmark. Result: 0.7% improvement.
Gather more data: Run perf stat to compare instruction counts, IPC, cache behavior.
Refine the hypothesis: Maybe the interleaved eval is hurting IPC. Revert it and retest.
Hypothesis fails again: Pool+prefetch alone is still not helping (55.5s vs 55.4s).
Deploy deeper instrumentation: Run perf record with call-graph profiling to see where time is actually going.
Analyze the data: The subagent report reveals zero samples for pool functions and high samples for Boolean::lc().
Form a new hypothesis: The pool is being bypassed because closures create their own LCs internally.
Verify the new hypothesis: The subagent's analysis of closure patterns confirms that Boolean::lc() is called extensively inside closures, creating temporary LCs that the pool cannot reach.
Synthesize the insight: Message 1173 articulates the corrected understanding and proposes a new approach. This cycle — hypothesize, test, gather data, refine, retest, deploy deeper instrumentation — is the essence of performance debugging. The assistant's willingness to abandon a carefully crafted optimization (the interleaved eval) and pivot to a completely different approach (in-place methods) when the data demanded it is a hallmark of effective engineering.

The Broader Significance

Message 1173 is more than just a single debugging insight. It represents a turning point in the optimization of the cuzk proving engine. The previous phases (Phase 1: batch-mode pipeline, Phase 2: async overlap, Phase 3: cross-sector batching) had all delivered significant throughput improvements — 1.27x, 1.46x, and so on. Phase 4 was supposed to be the "easy" micro-optimization phase, where known bottlenecks were fixed with straightforward code changes. Instead, it turned into a deep investigation that revealed the optimization problem was more complex than anticipated.

The lesson is general: in sufficiently complex systems, intuition about where time is spent is often wrong. The only reliable guide is measurement — and even then, the right measurement (a flat profile showing function-level self-time) can contradict the wrong measurement (a coarse perf stat showing instruction counts). The assistant's journey from perf stat to perf record to subagent analysis to the final insight is a testament to the importance of using the right tool for the right question.

Conclusion

Message 1173 captures the moment when a performance optimization effort pivots from a failing strategy to a promising new direction. The Vec recycling pool, the interleaved A+B eval, and the software prefetch were all reasonable optimizations based on the available data. But the data was incomplete. Only when the assistant deployed call-graph profiling and systematically analyzed the results did the true bottleneck emerge: not the six Vecs per enforce call, but the dozens of temporary LinearCombination objects created by Boolean::lc() inside the closures.

The new approach — adding in-place methods to Boolean and Num to eliminate temporary allocations at their source — is a direct consequence of this corrected understanding. Whether it delivers the expected 15–25% improvement remains to be measured in the next iteration. But regardless of the outcome, the methodological lesson stands: trust the profiler, not your intuition. The smoking gun was there all along, hidden in the perf data. It just took the right question to find it.

The Smoking Gun: How perf Revealed the True Bottleneck in Groth16 Synthesis