The 328 GiB Mistake: Diagnosing a Memory Allocation Regression in CUDA-Accelerated SNARK Proving

Introduction

In the high-stakes world of Filecoin proof generation, where a single Groth16 proof for a 32 GiB sector consumes nearly 200 GiB of peak memory and takes over a minute to compute, every optimization counts — and every misstep is magnified. This article examines a pivotal moment in the development of cuzk, a pipelined SNARK proving engine for Filecoin's Proof-of-Replication (PoRep) protocol. In message <msg id=865>, the assistant confronts an unexpected regression: after implementing five compute-level optimizations drawn from a detailed proposal document, the first end-to-end benchmark shows performance worse than the unoptimized baseline. What follows is a masterclass in diagnostic reasoning, memory allocation physics, and the humility required when theoretical optimizations meet real hardware.

The Setting: Phase 4 Compute Quick Wins

The message occurs at a critical juncture in a multi-phase optimization campaign. Phases 1 through 3 had already transformed cuzk from a monolithic prover into a sophisticated pipelined engine with cross-sector batching, achieving a 1.46× throughput improvement over the original Curio implementation. Phase 4, titled "Compute Quick Wins," promised to squeeze additional performance from the GPU and CPU hotpaths through a set of targeted micro-optimizations documented in c2-optimization-proposal-4.md.

The assistant had just completed implementing five optimizations:

1. A1 — SmallVec for LC Indexer: Replacing Vec<(usize, Scalar)> with SmallVec<[(usize, Scalar); 4]> in the bellpepper-core fork to eliminate approximately 780 million heap allocations per partition during constraint synthesis.
2. A2 — Pre-sizing large vectors: Adding a new_with_capacity constructor to ProvingAssignment in the bellperson fork to pre-allocate the a, b, and c vectors to their full capacity upfront, avoiding the reallocation copies that occur as the vectors grow incrementally.
3. A4 — Parallelize B_G2 CPU MSMs: Changing a sequential loop over circuits to use groth16_pool.par_map, parallelizing the B_G2 multi-scalar multiplication across CPU threads.
4. B1 — Pin a,b,c vectors with cudaHostRegister: Adding CUDA memory pinning around the Rust-provided a, b, and c arrays to enable faster host-to-device transfers via DMA.
5. D4 — Per-MSM window tuning: Splitting a single msm_t object into three instances tuned for the different popcount distributions of the L, A, and B_G1 multi-scalar multiplications. All five changes compiled cleanly and passed unit tests. The stage was set for an end-to-end GPU benchmark on the RTX 5070 Ti with real 32 GiB PoRep data.

The Regression Revealed

The benchmark results, shown in the preceding message <msg id=862>, were sobering:

• Total time: 106,017 ms (106 seconds), up from the Phase 3 baseline of ~89 seconds
• Synthesis time: 61,550 ms (61.6 seconds), up from 54.7 seconds — a 12.6% slowdown
• GPU time: 44,219 ms (44.2 seconds), up from 34.0 seconds — a 30% slowdown Every single optimization had apparently backfired. The synthesis phase, which should have been accelerated by pre-sizing and SmallVec, was slower. The GPU phase, which should have benefited from pinned memory and tuned MSM windows, was dramatically slower. This is the kind of moment that tests an engineer's diagnostic skills: do you blame the measurement, the hardware, or your own code?

The Diagnostic Reasoning

Message <msg id=865> captures the assistant's first diagnostic pass. The reasoning unfolds in several precise steps.

Step 1: Identifying the code path. The assistant notes that the single-proof benchmark went through synthesize_porep_c2_batch — specifically the one-sector path at line 750 of pipeline.rs, which had been modified to use the new synthesize_circuits_batch_with_hint API. This is critical: the pre-sizing hint (A2) was active.

Step 2: Quantifying the memory impact. The assistant performs a back-of-the-envelope calculation that reveals the hidden cost of pre-sizing:

131M pre-alloc × 32B = ~4.1 GiB per vector × 8 vectors = ~32.8 GiB per circuit

The number 131 million comes from the number of constraints in a 32 GiB PoRep circuit — each constraint produces one entry in each of the a, b, and c vectors. Each entry is a 32-byte field element (plus a usize index in some vectors). With 8 vectors per circuit (a, b, c, plus their internal representations), each circuit demands approximately 32.8 GiB of contiguous allocation.

Step 3: Accounting for parallelism. The critical insight comes next:

With 10 circuits running in parallel via rayon, that's 10 × 32.8 GiB = 328 GiB of upfront allocation all at once.

Rayon's parallel execution model means that all 10 circuits in the batch begin synthesis simultaneously. With the old incremental allocation strategy, each circuit's vectors grew gradually via Vec::push_back, which doubles capacity on each resize. This spreads the page fault cost across the entire synthesis duration — at any given moment, only a fraction of the total memory is being faulted in. With pre-sizing, the operating system must fault in 328 GiB of pages all at once, before any actual computation begins.

Step 4: Identifying the failure mechanisms. The assistant lists three likely causes of the regression:

1. Memory page-fault storm: 328 GiB of pages all faulted simultaneously. On a system with, say, 384 GiB of RAM, this means the kernel's page fault handler is overwhelmed, competing for memory bandwidth with the actual computation.
2. TLB pressure: Touching 328 GiB of new pages means the Translation Lookaside Buffer is constantly being evicted and refilled. Each page fault requires a page table walk, and with 328 GiB spread across potentially millions of 4 KiB pages (or even thousands of 2 MiB huge pages), the TLB miss rate skyrockets.
3. Possible NUMA effects: On a multi-socket system, the memory might be spread across NUMA domains, and the allocation pattern could cause remote memory access penalties. Step 5: The triage decision. The assistant decides to revert A2 while keeping A1:

Let me revert A2 (keep A1 SmallVec which is pure stack, no memory issues).

This is a nuanced decision. A1 (SmallVec) replaces heap allocations with stack-allocated small arrays that spill to the heap only when they exceed the inline capacity. This change has no memory footprint impact — it merely reduces allocation count. A2, by contrast, dramatically increases the rate of allocation, converting a gradual memory pressure into an instantaneous spike. The assistant correctly distinguishes between the two optimizations and preserves the one that is clearly safe.

Assumptions and Their Validity

The assistant's reasoning rests on several assumptions, most of which are sound:

Assumption 1: Ten circuits run in parallel via rayon. This is correct — the synthesis pipeline uses rayon's parallel iterator to process all circuits in a batch concurrently. The max_batch_size parameter was set to 1 for this test (single proof), but the PoRep C2 circuit for a 32 GiB sector internally generates 10 circuits (one per partition), and these are synthesized in parallel.

Assumption 2: The previous behavior was incremental doubling. This is the standard Vec growth strategy in Rust — when a Vec needs to grow beyond its current capacity, it doubles the capacity and copies the elements. For a vector that grows from empty to 131 million elements, this involves approximately 27 doublings (2^27 ≈ 134 million), with the total copied data being roughly 2× the final size (the classic "amortized O(1) push" property). The assistant correctly identifies that this spreads memory pressure over time.

Assumption 3: A1 SmallVec has no memory issues. This is reasonable — SmallVec stores elements inline on the stack up to its capacity (4 elements in this case), and only spills to heap allocation when the collection exceeds that size. For the LC Indexer, where most linearization constraints have fewer than 4 variables, this eliminates heap allocations entirely. No additional memory is allocated upfront.

Assumption 4: The pre-sizing is the main cause of the synthesis regression. This is plausible but not definitively proven. The assistant does not consider other potential contributors: the SmallVec change might introduce branching overhead, the new SynthesisCapacityHint API might add synchronization costs, or the compiler might have generated different code due to the changed dependency versions. However, the 328 GiB upfront allocation is by far the most likely culprit, and reverting A2 is the correct first step in isolating the regression.

The Mistake and Its Lessons

The core mistake here is not the regression itself — regressions are an expected part of optimization work. The mistake is the failure to anticipate the interaction between pre-sizing and parallelism. The optimization proposal (c2-optimization-proposal-4.md) correctly identified that Vec reallocation copies waste time and memory bandwidth. The solution — pre-allocate to final capacity — is textbook. But the proposal (and the assistant) implicitly assumed a single-threaded execution model. In a parallel context, pre-sizing multiplies the memory pressure by the number of concurrent threads, turning a benefit into a liability.

This is a classic systems engineering trap: an optimization that is correct in isolation becomes harmful when composed with other system properties. The lesson is not "pre-sizing is bad" but rather "pre-sizing must account for parallelism." A better approach might be:

• Pre-size each circuit's vectors but stagger the start of each circuit's synthesis to avoid simultaneous page faults
• Use a memory pool that pre-allocates and reuses buffers across circuits
• Pre-size with a smaller initial capacity and let the final doubling fill the remaining gap
• Use huge pages (2 MiB or 1 GiB) to reduce TLB pressure from large allocations The assistant's decision to revert A2 while keeping the API available (I'll keep the synthesize_circuits_batch_with_hint API but not use it for now) is wise — it preserves the infrastructure for future experimentation without incurring the current cost.

The Broader Context

This message is a turning point in the Phase 4 campaign. The assistant has just discovered that the "quick wins" are not so quick. The subsequent messages will show the assistant reverting the A2 usage, adding detailed phase-level CUDA timing instrumentation to isolate the GPU regression, and systematically testing each optimization in isolation. The regression becomes a learning opportunity — a chance to understand the system's memory architecture at a deeper level.

For readers unfamiliar with the cuzk project, this message illuminates several key aspects of the system:

• Scale: A single proof generation allocates hundreds of gigabytes of memory. The a, b, and c vectors alone consume over 30 GiB per circuit.
• Parallelism: Rayon is used aggressively to parallelize circuit synthesis across all available CPU cores.
• Memory sensitivity: Performance is dominated by memory allocation and data movement, not just computation.
• Diagnostic rigor: The assistant reasons quantitatively, calculating exact memory sizes and identifying specific failure mechanisms.

Conclusion

Message <msg id=865> captures a moment of diagnostic clarity in the midst of an optimization campaign gone wrong. The assistant's reasoning — from identifying the code path, to calculating the memory impact, to accounting for parallelism, to identifying failure mechanisms, to making a nuanced triage decision — demonstrates the kind of systems thinking required to optimize at this scale. The 328 GiB upfront allocation was not just a bug; it was a collision between a well-intentioned optimization and the physics of virtual memory. The lesson endures: in high-performance computing, the fastest code is not always the one that allocates the most memory at once.