The Hypothesis That Didn't Pay Off: Investigating Allocation Overhead in Groth16 Synthesis

In the high-stakes world of Filecoin proof generation, every second counts. The SUPRASEAL_C2 pipeline at Curio had already been through multiple optimization phases—async overlap between synthesis and GPU proving, cross-sector batching, Boolean gadget micro-optimizations, and async deallocation of GPU-phase buffers. Each had delivered measurable throughput improvements. But one hypothesis remained tantalizing: if deallocating ~130 GB of Rust vectors after GPU proving had cost 10 seconds, surely allocating them during synthesis must have a similar cost.

This article examines message [msg 1296] in the opencode session, a pivotal moment where the assistant transitions from investigation to implementation, wiring up a pre-allocation API that—despite compelling theoretical motivation—would ultimately deliver zero measurable improvement.

The Context: A Session Driven by Profiling

The conversation leading to [msg 1296] reveals a development cycle driven by empirical measurement. In [msg 1290], the assistant had just completed a comprehensive projection-vs-actuals analysis of Phase 4 optimizations. The numbers were sobering: the original plan projected 2-3x throughput improvement, but actual results stood at a modest 1.15x. Two textbook optimizations (SmallVec for LinearCombination and cudaHostRegister for pinned memory) had been invalidated by real hardware testing on Zen4. The synthesis bottleneck remained stubbornly at ~50 seconds.

The user's question in [msg 1291] was sharp: "If there was a win in dealloc, is it possible that alloc can have a similar one?" This wasn't idle speculation. The previous chunk had just discovered that synchronous destructor overhead from freeing ~37 GB of C++ vectors and ~130 GB of Rust Vecs after GPU proving was costing 10 seconds. The fix—moving deallocation into detached threads—had been a clean win. The symmetry argument was compelling: if freeing memory is expensive, allocating it should be too.

The Investigation That Confirmed the Opportunity

The assistant's response in [msg 1292] validated the intuition, laying out the mechanics: the ProvingAssignment Vecs (a, b, c, aux_assignment) grow incrementally during synthesis via push() with geometric doubling. For 130 million constraints, each Vec undergoes approximately 27 reallocation cycles. Each reallocation involves an mmap for the larger buffer, a memcpy of old data, and an munmap of the old buffer. The assistant dispatched a thorough investigation task to trace the exact allocation behavior.

The task returned in [msg 1293] with a striking finding: the infrastructure to fix this already existed but was never wired up. Bellperson's ProvingAssignment had a new_with_capacity() constructor, and there was a synthesize_circuits_batch_with_hint() function that accepted a SynthesisCapacityHint struct. The pipeline was passing None as the hint at every call site. The theoretical waste was staggering: approximately 25 GB of redundant memcpy per circuit from the geometric doubling series, multiplied by 10 parallel circuits to ~250 GB of wasted copies and ~810 mmap/munmap syscalls.

Message 1296: The Turn from Investigation to Implementation

[msg 1296] is the message where the assistant stops investigating and starts implementing. The full text reads:

Now let me check all the synthesis call sites to wire up hints. The best approach: use a static once_cell / AtomicUsize that records the capacity from the first proof, then reuse it for all subsequent proofs. But even simpler — for the initial implementation, I can hardcode known values per circuit type, or better yet, change the pipeline to pass hints.

>

Let me look at the import and see what's exported: [read] /home/theuser/curio/extern/cuzk/cuzk-core/src/pipeline.rs

This message is deceptively simple. It contains only a single read tool call, but it represents a critical decision point. The assistant is weighing three approaches:

1. A static cache using once_cell or AtomicUsize that records capacity from the first proof and reuses it for subsequent proofs. This is elegant and adaptive—it works for any circuit type without hardcoded knowledge.
2. Hardcoded known values per circuit type. The assistant already knows from profiling that PoRep C2 has ~130,278,869 constraints and ~23,048,689 auxiliary variables. This would be fast to implement but fragile—it breaks if circuit parameters change.
3. Passing hints through the pipeline from the caller. This is architecturally cleanest but requires changing the API surface of multiple functions. The assistant's reasoning shows a preference for approach 1 (the static cache), calling it "the best approach," but also acknowledges that hardcoding would be "simpler." The phrase "or better yet, change the pipeline to pass hints" reveals a tension between quick implementation and architectural purity.

The Thinking Process: What the Reasoning Reveals

The assistant's reasoning in this message reveals several layers of decision-making. First, there's the recognition that this is a mechanical change—the hard part (understanding the allocation pattern and identifying the existing API) is already done. What remains is plumbing: finding all six synthesis call sites and replacing synthesize_circuits_batch with synthesize_circuits_batch_with_hint.

Second, the assistant is thinking about generality. The PoRep C2 circuit is the primary target, but the pipeline also handles Winning PoSt, Window PoSt, and SnapDeals circuits. A hardcoded approach would need values for each. A caching approach learns from the first synthesis of each circuit type and applies to subsequent ones.

Third, there's an implicit assumption that this optimization will have measurable impact. The assistant has already estimated ~250 GB of wasted copies and ~810 syscalls. The dealloc win was 10 seconds. The alloc win, by symmetry, should be comparable. This assumption is never stated explicitly in [msg 1296], but it permeates the implementation decisions—the assistant is investing in a global caching infrastructure, not a quick hack.

Input Knowledge Required

To understand this message, one needs knowledge of:

• Rust's Vec growth strategy: geometric doubling (typically doubling capacity when full), which amortizes push() to O(1) but causes logarithmic reallocations
• The mmap/munmap memory model: how the kernel handles large allocations, including page table management and TLB effects
• Bellperson's ProvingAssignment structure: the five main Vecs (a, b, c, input_assignment, aux_assignment) that store R1CS constraint evaluations
• The pipeline architecture: pipeline.rs contains six synthesis call sites across functions like synthesize_porep_c2_multi, synthesize_porep_c2_partition, synthesize_porep_c2_batch, synthesize_winning_post, synthesize_window_post, and synthesize_snap_deals
• The SynthesisCapacityHint API: a pre-existing struct with num_constraints, num_aux, and num_inputs fields, and the synthesize_circuits_batch_with_hint function that accepts Option<SynthesisCapacityHint>

Output Knowledge Created

This message creates the implementation plan for wiring up capacity hints. It establishes:

1. The approach: a global hint cache that records capacity from the first proof
2. The scope: all six synthesis call sites in pipeline.rs
3. The mechanism: replacing synthesize_circuits_batch with synthesize_circuits_batch_with_hint at each site
4. The next step: reading the imports to understand what's already exported The actual implementation would follow in subsequent messages ([msg 1298] through [msg 1339]), involving edits to pipeline.rs, handling compilation errors from unhandled CircuitId variants, and eventually benchmarking.

What Happened Next: The Zero Result

The implementation proceeded smoothly. The assistant added a cache_hint function and a synthesize_with_hint helper, then replaced all six call sites. The code compiled after fixing some CircuitId match arms. The first benchmark used a single-partition synth-only test: four iterations, where the first grew organically and iterations 2-4 used the cached hint.

The result was unambiguous: zero measurable impact. Synthesis time was 50.7s without hints and 50.9s with hints—within noise. A full E2E test with the daemon confirmed: 50.65s synthesis time with and without hints.

The assistant's post-mortem analysis in [msg 1347] explained the asymmetry. The dealloc win came from synchronous munmap of large contiguous GPU-phase buffers—a kernel operation that tears down page tables all at once. The alloc during synthesis, by contrast, happens incrementally via push() with geometric doubling. The reallocations occur early when Vecs are small (first 20 doublings copy less than 256 MB total), and the late-stage reallocations (from ~2 GB to ~4 GB) happen only 3-4 times per Vec, each copying ~2-4 GB at memory bandwidth speed (~100 ms), concurrently across 10 circuits on 96 cores. The amortization is remarkably efficient.

The Deeper Lesson: Measurement Over Intuition

[msg 1296] represents a moment where compelling theoretical reasoning met the reality of empirical measurement—and lost. The symmetry argument between alloc and dealloc was intellectually satisfying. The estimated 250 GB of wasted copies was alarming. The existing but unused API was a smoking gun. Every indicator pointed to a significant win.

Yet the measurement showed zero impact. The lesson is not that the implementation was wasted—the hint infrastructure remains as defensive code that prevents pathological fragmentation under memory pressure. The lesson is about the fundamental asymmetry of memory operations in high-performance computing. Dealloc hurts when it's synchronous and involves large contiguous regions. Alloc doesn't hurt when it's incremental, amortized, and overlapped with computation.

The assistant's response to this zero result was exemplary: it committed the code anyway as a defensive optimization, documented the benchmark results, and updated the project's understanding of where the real bottleneck lies. The synthesis time of ~50 seconds is now confirmed to be dominated by pure computation (field arithmetic and LC evaluation), not memory management. This conclusion directly motivates Phase 5 (PCE), which aims to eliminate synthesis entirely by replacing circuit synthesis with sparse matrix-vector multiply.

In the end, [msg 1296] is a testament to the value of hypothesis-driven optimization—even when the hypothesis is wrong. The investigation was thorough, the implementation was clean, and the measurement was definitive. The project moved forward with a clearer understanding of its performance landscape, armed with data rather than intuition.