The Hypothesis That Failed: When Pre-Allocation Met Zero Measurable Impact

In any serious performance engineering effort, there comes a moment when a beautifully reasoned optimization collides with the cold reality of measurement. Message [msg 1341] of this opencode session captures exactly such a moment. The assistant, having just completed a sophisticated infrastructure change to wire up SynthesisCapacityHint pre-allocation across all six synthesis call sites in the cuzk proving pipeline, runs a benchmark to validate the optimization. The result is not a triumph but a humbling lesson: the benchmark shows virtually identical synthesis times with and without pre-allocation, forcing a deep re-examination of what actually dominates the cost of Groth16 proof synthesis.

The Context: A Chain of Optimizations

To understand why this message matters, one must appreciate the journey that led here. The broader session (Segment 15) is the culmination of Phase 4 of a multi-phase optimization campaign for the SUPRASEAL_C2 Groth16 proof generation pipeline. Earlier in this phase, the assistant had achieved a genuine breakthrough: by identifying that synchronous destructor overhead from freeing ~37 GB of C++ vectors and ~130 GB of Rust Vecs was blocking the GPU wrapper, and implementing async deallocation via detached threads, the total end-to-end proof time dropped from 88.9s to 77.2s — a 13.2% improvement ([chunk 15.0]).

That win came from the deallocation side. The user then posed a natural question: if freeing memory was a bottleneck, could allocation during synthesis also be a bottleneck? The ProvingAssignment structs (a, b, c, aux_assignment) grow via Vec::push() through approximately 27 reallocation cycles each, each cycle copying the entire existing buffer to a new allocation. For a 10-circuit batch, the theoretical total of redundant memory copies reaches an astonishing ~265 GB. Surely eliminating that waste would yield a significant speedup.

The Implementation: Wiring Up a Dormant API

The assistant traced through the bellperson library and discovered that a SynthesisCapacityHint API already existed — it was simply never called by the pipeline code ([msg 1290] through [msg 1299]). The API allows callers to provide the expected number of constraints, auxiliary variables, and input variables before synthesis begins, so the internal Vecs can be pre-allocated to their final capacity with a single allocation per vector.

Over the course of messages [msg 1300] through [msg 1339], the assistant implemented a global hint cache system. The design is pragmatic: the first synthesis for a given CircuitId (e.g., Porep32G, WinningPost32G) grows organically, recording the final sizes of the Vecs. These sizes are cached, and subsequent syntheses use them to pre-allocate via synthesize_circuits_batch_with_hint. All six synthesis call sites in pipeline.rs were modified — covering PoRep multi-sector, PoRep partition, PoRep batch, Winning PoSt, Window PoSt, and SnapDeals circuits. The infrastructure compiled cleanly and was committed as a defensive optimization.

The Benchmark: Message [msg 1341]

The subject message executes the critical test:

FIL_PROOFS_PARAMETER_CACHE=/data/zk/params extern/cuzk/target/release/cuzk-bench synth-only \
  --c1 /data/32gbench/c1.json --partition 0 -i 4 2>&1

This runs the synth-only microbenchmark subcommand, which performs Groth16 synthesis for a single partition of a 32 GiB PoRep sector, repeated for 4 iterations. The key design: iteration 1 has no cached hint and grows organically; iterations 2–4 use the cached hint from iteration 1 and should pre-allocate.

The output confirms the mechanism works:

no capacity hint cached — first synthesis will grow organically circuit_id=porep-32g

The subsequent iterations silently use the cached hint (visible in the log output as "using cached capacity hint" lines).

The Result: Zero Impact

The next message ([msg 1342]) reveals the outcome:

Why the Intuition Was Wrong

The assistant's analysis in [msg 1342] is a masterclass in understanding why an optimization failed. The key insight is the asymmetry of allocation versus deallocation.

For a single circuit, the total wasted copy from geometric Vec growth is approximately 26.5 GB. With modern Zen4 memory bandwidth (~40 GB/s), copying 26.5 GB takes about 0.66 seconds. Spread across 50.7 seconds of synthesis, each reallocation costs roughly 25 milliseconds and is interleaved with actual computation. The push() with geometric doubling amortizes to O(1) average cost per element — the expensive copies happen early when Vecs are small, and by the time Vecs reach gigabytes in size, doublings are infrequent.

The previous deallocation win, by contrast, came from a fundamentally different bottleneck: synchronous munmap of multi-gigabyte buffers at the end of GPU proving. That was a serial, blocking operation that held up the entire pipeline. Moving it to a background thread freed the main thread to proceed immediately.

The assistant also correctly notes that the single-partition benchmark (1 circuit) may understate the potential benefit. With 10 parallel circuits (the full E2E batch), all 10 Vecs reallocate independently and concurrently via rayon, potentially causing memory pressure and TLB thrashing from 10× the mmap/munmap activity. The follow-up test with the daemon (full 10-circuit synthesis) is already queued.

The Deeper Lesson

This message embodies a principle that separates effective optimization from wishful thinking: measure, don't speculate. The assistant invested significant effort — tracing code paths, implementing a global hint cache, modifying six call sites, handling edge cases like 64 GiB circuit variants — all based on a plausible hypothesis. But when the benchmark ran, the data spoke clearly.

The infrastructure was not wasted. The SynthesisCapacityHint wiring remains committed as a defensive optimization. It costs nothing at runtime (the cache is populated lazily) and could become relevant if synthesis is ever restructured to be more allocation-sensitive. More importantly, the investigation conclusively ruled out allocation overhead as a bottleneck, narrowing the search for the next optimization target.

What This Means for Phase 5

The confirmation that synthesis time is purely computational — not allocation-bound — reinforces the necessity of Phase 5's planned approach: Partial Circuit Evaluation (PCE). If the CPU is spending 50 seconds doing real work (constraint evaluation, field arithmetic, LinearCombination construction), not waiting on memory allocation, then the only path to significant gains is reducing the computational work itself. PCE aims to pre-evaluate circuit components that are constant across proofs, eliminating redundant computation at the cost of increased memory.

Message [msg 1341] thus marks a turning point. It closes the book on allocation optimization and redirects focus to the computational heart of the proving pipeline. In the ongoing narrative of this optimization campaign, it is the moment when a plausible shortcut was tested, found wanting, and set aside — leaving the harder but more impactful path ahead.