The Hypothesis That Died by Measurement: Why Pre-Allocating 265 GB of Vectors Changed Nothing

In the long arc of performance engineering, there are moments of triumph and moments of humility. The message at <msg id=1342> in this opencode session is firmly in the latter category — and it is arguably more valuable for it. After implementing a theoretically compelling optimization that promised to eliminate approximately 265 GB of redundant memory copies during Groth16 proof synthesis, the assistant runs a benchmark and confronts an uncomfortable truth: zero measurable improvement. The synthesis time remains stubbornly at ~50.7 seconds, with or without the optimization. What follows is a masterclass in data-driven performance analysis: a careful recalculation of the actual overhead, a recognition of why geometric Vec growth amortizes allocation cost, and a measured decision to test a more targeted scenario before declaring the hypothesis dead.

The Context: A Chain of Optimizations

To understand <msg id=1342>, we must first understand what came before it. The session is deep into "Phase 4" of a performance optimization campaign for the cuzk proving engine — a Rust/CUDA system that generates Groth16 proofs for Filecoin's Proof-of-Replication (PoRep) protocol. The assistant had already achieved significant wins: a 13.2% end-to-end improvement by optimizing synthesis hot paths (Boolean::add_to_lc methods that eliminated temporary LinearCombination allocations) and by fixing a GPU wrapper regression caused by synchronous destructor overhead.

That last fix was particularly instructive. The assistant discovered that after GPU proving completed, the C++ and Rust sides were spending ~10 seconds freeing ~37 GB of C++ vectors and ~130 GB of Rust Vecs synchronously — blocking the main thread while munmap and destructors ran. The fix was to move deallocation into detached threads, allowing the function to return immediately. This was a dramatic win: GPU wrapper time dropped from 36.0s to 26.2s, matching the CUDA internal timing exactly.

It was in the wake of this victory that the user (or the assistant itself) posed a natural question: if deallocation was a bottleneck, could allocation be one too? The logic was seductive. During synthesis, the ProvingAssignment struct — which holds the a, b, c, and aux_assignment constraint vectors — grows organically via Vec::push(). Each push may trigger a reallocation, which copies the entire existing buffer to a new memory region. With ~130 million constraints per circuit and 10 circuits running in parallel via rayon, the total volume of copied data was estimated at ~265 GB. Surely eliminating that would yield a measurable speedup.

The Implementation: Wiring Up a Dormant API

The assistant's investigation revealed that bellperson (the underlying constraint-synthesis library) already had an API for this: SynthesisCapacityHint. A ProvingAssignment could be pre-allocated to its final size before synthesis began, avoiding all reallocation. The problem was that this API was never wired up in the pipeline callers — every synthesis call site in pipeline.rs was using the plain synthesize_circuits_batch function, which grew vectors organically.

Over the course of messages <msg id=1303> through <msg id=1339>, the assistant implemented a global hint cache and modified all six synthesis call sites. The cache stored the constraint counts for each CircuitId (PoRep 32G, Winning PoSt, Window PoSt, SnapDeals) after the first synthesis, so subsequent runs could pre-allocate. The changes touched synthesize_porep_c2_multi, synthesize_porep_c2_partition, synthesize_porep_c2_batch, synthesize_winning_post, synthesize_window_post, and synthesize_snap_deals. There was even a minor debugging detour when the assistant accidentally mapped the wrong CircuitId to a call site (line 505 got SnapDeals32G instead of Porep32G), requiring a fixup in <msg id=1335>.

After compilation succeeded (with only some harmless warnings about unused functions), the assistant was ready to benchmark. The plan was straightforward: run 4 iterations of a single-partition synthesis microbenchmark. The first iteration would have no cached hint and grow organically. Iterations 2-4 would use the cached hint and pre-allocate. If the hypothesis was correct, iterations 2-4 would be measurably faster.

The Verdict: No Measurable Improvement

The benchmark results, displayed in <msg id=1342>, are devastatingly clear:

• Iteration 1 (no hint, organic growth): 50.7 seconds
• Iteration 2 (with hint, pre-allocated): 50.9 seconds
• Iteration 3 (with hint, pre-allocated): 51.0 seconds
• Iteration 4 (with hint, pre-allocated): 50.9 seconds Not only is there no improvement — the pre-allocated runs are marginally slower (by 0.2-0.3 seconds), though this is well within noise. The optimization that was going to save 265 GB of copying has produced exactly nothing. This is the moment where a less disciplined engineer might hand-wave: "well, the measurement must be wrong," or "maybe the hint isn't working," or "let me try a different approach." The assistant does none of these things. Instead, the assistant does something far more valuable: they recalculate the actual overhead and explain why the intuition was wrong.

The Recalculation: Why 265 GB Was a Misleading Number

The assistant's analysis proceeds in two steps. First, they recalculate the actual copy volume for a single circuit (not 10):

• a vector: 130 million elements × 32 bytes = 4.17 GB. With ~27 doublings, total data copied ≈ 8.3 GB.
• b and c vectors: same story, ~8.3 GB each.
• aux_assignment: 0.74 GB → ~1.5 GB copied.
• Total wasted copy for 1 circuit: ~26.5 GB. With a modern Zen4 memory bandwidth of ~40 GB/s, copying 26.5 GB takes about 0.66 seconds. Combined with mmap/munmap syscall overhead for ~100 large allocations, perhaps 1-2 seconds total. But this cost is distributed across 50.7 seconds of synthesis — each reallocation costs ~25 milliseconds and is interleaved with actual computation. The key insight is that Vec::push() with geometric doubling amortizes extremely well. The average cost per push is O(1) with a small constant factor. The expensive reallocations happen early, when the Vec is small and copies are cheap. By the time the Vec reaches multi-gigabyte size, it doubles infrequently — the last few doublings go from 2 GB to 4 GB to 8 GB, but those are only 2-3 copies total. The vast majority of the 27 doublings happen at sub-kilobyte or sub-megabyte sizes, where the copy cost is negligible. The assistant calculates the actual overhead at ~0.66 seconds out of 50.7 seconds — about 1.3%. This is well below the noise floor of the benchmark and explains why pre-allocation shows zero impact.

The Fundamental Asymmetry: Alloc vs. Dealloc

The most important intellectual contribution of <msg id=1342> is the recognition of a fundamental asymmetry between allocation and deallocation in Rust:

Allocation (via push() with geometric doubling): Amortized O(1) per element. Reallocations happen early when vectors are small. Copies are cheap and overlap with computation. The allocator can satisfy growth from the free list without touching the OS.

Deallocation (via munmap of large allocations): Synchronous, expensive, and cannot be overlapped with computation. When a 4 GB Vec is dropped, the allocator must munmap the backing memory, which is a syscall that touches page tables and TLB state. With 10 circuits dropping ~130 GB of Vecs simultaneously, this creates a synchronous bottleneck that blocks the main thread.

This explains why the async deallocation fix (moving destructors to detached threads) produced a 10-second speedup, while pre-allocation produces zero improvement. The two problems look symmetric but are fundamentally different in their performance characteristics.

The Next Experiment: Full E2E with 10 Circuits

The assistant does not fully abandon the hypothesis. They note that the single-partition benchmark uses only 1 circuit, not the full 10 circuits of an E2E batch. With 10 circuits reallocating independently and concurrently via rayon, there could be additional memory pressure and TLB thrashing from 10× the mmap/munmap activity. The assistant decides to test this scenario by building the daemon and running a full E2E benchmark.

This is a measured response: the hypothesis is not dead, but it is wounded. The assistant is willing to be proven wrong again — and as the chunk summary reveals, the full E2E test also shows zero impact. The synthesis bottleneck is purely computational, not allocator-related.

The Broader Lesson: Measurement Over Intuition

What makes <msg id=1342> a remarkable piece of engineering writing is not the code — the code is straightforward, just wiring up an existing API. What makes it remarkable is the thinking. The assistant could have simply reported "no improvement" and moved on. Instead, they:

1. Calculated the actual expected overhead (0.66 seconds, 1.3%) rather than relying on the intuitive but misleading 265 GB figure.
2. Explained why geometric doubling works — the reallocations happen early and cheaply.
3. Identified the alloc/dealloc asymmetry — why the previous dealloc fix worked but this alloc fix doesn't.
4. Designed a follow-up experiment (10-circuit E2E) to test a remaining edge case. This is the essence of performance engineering: forming hypotheses, testing them rigorously, and when they fail, understanding why they failed rather than discarding the data. The 265 GB figure was not wrong — that much data is copied over the lifetime of the process. But the cost of that copying is hidden inside the computation, overlapped with useful work, and amortized to insignificance. Only measurement could reveal this.

Input Knowledge Required

To fully understand <msg id=1342>, the reader needs:

• Understanding of Rust's Vec growth strategy: Geometric doubling (typically doubling capacity when full) and its amortized O(1) cost per push.
• Knowledge of memory bandwidth: Zen4's ~40 GB/s DDR5 bandwidth and what it means for bulk copy operations.
• Familiarity with the ProvingAssignment struct: The a, b, c, and aux_assignment vectors that store constraint evaluations during Groth16 synthesis.
• Context from the previous deallocation fix: The async destructor optimization that saved 10 seconds by moving munmap to background threads.
• Understanding of the cuzk pipeline architecture: How 10 circuits are synthesized in parallel via rayon, each producing multi-gigabyte vectors.

Output Knowledge Created

This message produces several valuable pieces of knowledge:

1. Empirical result: Pre-allocation via SynthesisCapacityHint has zero measurable impact on synthesis time for single-partition PoRep proofs.
2. Explanatory model: Geometric doubling amortizes reallocation cost to ~1.3% of total synthesis time, well below the noise floor.
3. Asymmetry principle: Allocation and deallocation have fundamentally different performance characteristics in Rust — the former is amortized and overlapped, the latter can be synchronous and blocking.
4. Methodological lesson: The importance of recalculating expected effect sizes when an optimization fails, rather than accepting or rejecting the hypothesis based on intuition.
5. Direction for future work: The synthesis bottleneck is confirmed to be purely computational, reinforcing the necessity of Phase 5 (PCE — Probabilistic Checkable Encoding or similar compute-level optimization) for significant gains.

Conclusion

Message <msg id=1342> is a small masterpiece of performance analysis. It documents the moment when a promising hypothesis collides with reality, and the engineer responds not with denial but with understanding. The 265 GB of wasted copies turned out to be a red herring — the copies were cheap, amortized, and hidden inside the computation. The real bottleneck, as the assistant correctly identifies, is computational, not allocator-related. The path forward is not more clever memory management but deeper algorithmic changes.

In a field where intuition often leads us astray, this message stands as a reminder: measure first, understand second, optimize third. And when the measurement says no, listen to what it's telling you.