The Hypothesis That Failed: Why Pre-Allocation Didn't Save a Single Millisecond

In the high-stakes world of Groth16 proof generation for Filecoin's Proof-of-Replication (PoRep), every millisecond counts. The SUPRASEAL_C2 pipeline, a sprawling chain of Go, Rust, and CUDA code, consumes ~200 GiB of peak memory and takes nearly 90 seconds to produce a single proof. After a series of successful optimizations — Boolean::add_to_lc shaving 8.3% off synthesis, async deallocation eliminating GPU wrapper overhead — the team had achieved a 13.2% end-to-end improvement. But one hypothesis remained untested: could pre-allocating the massive ProvingAssignment vectors eliminate the hidden cost of organic growth?

Message <msg id=1344> captures the moment this hypothesis was put to the test. It is a single bash command, but it represents the culmination of a deep investigation into memory allocation patterns, Rust's Vec growth mechanics, and the fundamental asymmetry between allocation and deallocation in high-performance computing.

The Context: A Pipeline Built on Push

The ProvingAssignment struct in bellperson holds three giant vectors — a, b, and c — each containing 130 million field elements (32 bytes each, totaling ~4.17 GB per vector), plus an aux_assignment of ~740 MB. During synthesis, these vectors grow organically via push() as constraints are added to the circuit. With Rust's default geometric doubling strategy, each vector undergoes approximately 27 reallocation cycles, each time copying the existing data to a new, larger buffer.

The theoretical cost is staggering. Across 10 parallel circuits (the standard batch size for a PoRep C2 proof), the total redundant copy volume reaches approximately 265 GB. Every byte is copied at least once more than necessary, and with modern Zen4 memory bandwidth of ~40 GB/s, that represents roughly 6.6 seconds of pure data movement — a significant fraction of the ~50-second synthesis phase.

The user, drawing a natural parallel to the recently fixed deallocation bottleneck (where synchronous destructor calls for ~37 GB of C++ vectors and ~130 GB of Rust Vecs were blocking the GPU wrapper for 10 seconds), hypothesized that allocation overhead during synthesis might be a similar hidden tax. The assistant agreed that the hypothesis was worth investigating.

Discovery: An API Waiting to Be Wired

Tracing through the bellperson codebase, the assistant discovered something surprising: a SynthesisCapacityHint API already existed. The synthesize_circuits_batch_with_hint function accepted pre-computed capacity estimates for the a, b, c, and aux_assignment vectors, allowing them to be allocated exactly once at the correct size. But this API was never wired up in any of the pipeline callers.

The assistant implemented a global hint cache (HINT_CACHE), a HashMap<CircuitId, SynthesisCapacityHint> protected by a OnceLock<RwLock>. On the first synthesis for a given circuit type, the vectors grow organically and the final capacities are recorded. On subsequent syntheses, the cached hint is passed to synthesize_circuits_batch_with_hint, theoretically eliminating all reallocation copies.

All six synthesis call sites in pipeline.rs were modified:

• synthesize_porep_c2_multi (multi-sector batch)
• synthesize_porep_c2_partition (single partition)
• synthesize_porep_c2_batch (cross-sector batch)
• synthesize_winning_post
• synthesize_window_post
• synthesize_snap_deals Each call was switched from synthesize_circuits_batch to synthesize_with_hint, with the appropriate CircuitId for hint lookup. The infrastructure was clean, defensive, and committed as part of the Phase 4 optimization suite.

The Test: Two Proofs, One Question

Message <msg id=1344> executes the critical benchmark. The assistant starts by running a single-partition synth-only microbenchmark with four iterations:

=== Synthesis Microbenchmark ===
iter 1 (no hint): 50.7s
iter 2 (with hint): 50.9s
iter 3 (with hint): 51.0s
iter 4 (with hint): 50.9s

The result is immediate and unambiguous: zero measurable improvement. The assistant's reasoning, captured in the message's thinking process, identifies why:

"With 1 circuit: a: 130M × 32B = 4.17 GB, ~27 doublings → total copied ~8.3 GB. b, c: same → ~8.3 GB each. aux_assignment: 0.74 GB → ~1.5 GB total copied. Total wasted copy: ~26.5 GB for 1 circuit. With modern Zen4 memory bandwidth (~40 GB/s), copying 26.5 GB takes ~0.66s. Combined with mmap/munmap syscall overhead for ~100 large allocations, maybe 1-2s total. But this is distributed across 50.7s of synthesis — each reallocation costs ~25ms and is interleaved with the actual computation."

The assistant correctly identifies the key insight: push() with geometric doubling amortizes well. The average cost per push is O(1) with a small constant. The reallocations happen early when Vecs are small and copies are cheap; by the time Vecs are large, they double infrequently. The total copy overhead is ~0.66s out of 50.7s — about 1.3%, well within noise.

But the assistant doesn't stop there. The real test must be with 10 parallel circuits, where memory pressure and TLB thrashing from 10× the mmap/munmap activity could amplify the cost. Message <msg id=1344> runs this full end-to-end test using the daemon:

=== PROOF 1 (no hint) ===
total=76937 ms (synth=50652 ms, gpu=26038 ms)
wall time: 77067 ms

The second proof (with hint) is initiated in the same command, and its result is captured in the subsequent message <msg id=1345>:

=== PROOF 2 (with hint) ===
synthesis time: 50.65s (identical)

The Verdict: Allocation Is Not Deallocation

The result is definitive: pre-allocation has zero measurable impact on synthesis time, even with 10 parallel circuits. The synthesis bottleneck is purely computational, not allocator-bound.

This finding carries a profound lesson about the asymmetry of memory operations in high-performance computing. The previous deallocation win — moving synchronous destructors to detached threads — eliminated a 10-second GPU wrapper regression because munmap of ~167 GB of memory is a synchronous, serialized kernel operation that blocks the calling thread until the page tables are updated. Allocation, by contrast, is amortized: mmap for new pages is lazy (fault-on-demand on Linux), and the copies during geometric doubling are small when they happen and overlap with computation.

The assistant's final reflection, visible in the chunk summary, captures the meta-lesson:

"The themes of this chunk are hypothesis-driven optimization, the critical importance of measurement over intuition, and the fundamental asymmetry of alloc vs dealloc: Rust's geometric push() amortizes cost and overlaps with parallel computation, while the previous dealloc win came from synchronous munmap of large GPU-phase buffers."

What This Message Teaches Us

Message <msg id=1344> is a masterclass in disciplined performance engineering. It demonstrates:

1. Hypothesis formulation: The user's hypothesis was grounded in a real, measurable win (async deallocation) and extended by analogy to a related domain (allocation). This is how good optimization work proceeds — by identifying patterns and testing them.
2. Measurement over intuition: Despite a theoretical model predicting ~265 GB of wasted copies and a plausible mechanism for slowdown, the actual measurement showed zero impact. The assistant did not rationalize away the result or search for confounding factors; it accepted the data and updated its mental model.
3. Layered benchmarking: The investigation proceeded from microbenchmark (single circuit, synth-only) to full end-to-end test (10 circuits, GPU proving). Each layer confirmed the same result, eliminating the possibility that the effect was masked by noise or hidden in a different subsystem.
4. Deep system understanding: The assistant's reasoning about why allocation doesn't matter — amortized geometric doubling, early small copies, overlap with computation, lazy mmap — reveals a sophisticated mental model of how Rust's Vec and the Linux memory subsystem interact.

The Path Forward

With allocation ruled out as a bottleneck, the synthesis phase is confirmed to be purely compute-bound. The ~50 seconds are spent in actual constraint evaluation, field arithmetic, and linear combination construction — not in memory management. This conclusion reinforces the necessity of Phase 5 (PCE — Polynomial Commitment Engine) for significant gains, where the computational work itself must be reduced or accelerated.

The SynthesisCapacityHint infrastructure, while not performance-critical, remains a defensive optimization. It eliminates a class of potential regressions (e.g., if a future change alters allocation patterns) and provides a clean API for any downstream consumer that wants deterministic allocation. It was committed as part of the Phase 4 changeset (2da2a901), a permanent guardrail against a problem that, in the current system, does not exist.

In the end, the most valuable output of message <msg id=1344> is not a performance win — it is knowledge. The team now knows, with empirical certainty, that the synthesis bottleneck is computational, not allocator-related. That knowledge saves future investigators from chasing the same dead end and focuses effort where it can actually make a difference: on the arithmetic itself.