Freeing the Giants: How a Memory Investigation Uncovered 12 GiB of Waste per Partition in a GPU Proving Pipeline

Introduction

In the high-stakes world of GPU-accelerated zero-knowledge proof generation, every gigabyte of memory counts. When a proving pipeline is operating within striking distance of a system's 755 GiB RAM ceiling — peaking at 668 GiB during benchmark runs — even modest memory savings can determine whether a configuration is viable or crashes with an out-of-memory (OOM) error. This is the context for message 3063 in the opencode session, where the assistant investigates a seemingly simple question from the user: "the pending proof handle has nothing that can be freed early?"

What follows is a masterclass in systematic memory investigation. The assistant traces through hundreds of lines of Rust and C++ CUDA code, maps the data dependencies of a background thread, identifies a ~12 GiB per-partition allocation that is held unnecessarily, and designs a surgical fix. This article examines that single message in depth: the reasoning that motivated it, the investigative methodology, the assumptions made, and the knowledge it produced.

The Message in Full

The subject message begins with the assistant's reasoning:

The cleanest way: add a method on PendingProofHandle that drops a, b, c from the provers. Or better yet, do it right in prove_start before returning — since we know the GPU is done with them by that point (cudaHostUnregister already ran).

>

Actually, the GPU uploads and uses a/b/c inside generate_groth16_proofs_start_c (the GPU kernel region). By the time the function returns, cudaHostUnregister has been called and the GPU kernels have finished reading them. So we can drop a/b/c immediately.

>

Let me add this to prove_start — after the C++ call returns, clear the a/b/c vectors before storing in the handle:

The assistant then reads the Rust source file at extern/bellperson/src/groth16/prover/supraseal.rs to examine the PendingProofHandle struct and the prove_start function.

The Trigger: A User's Insightful Question

The message is a direct response to the user's question in message 3055. The context is crucial. The assistant had just completed Phase 12 of the optimization project — a split GPU proving API that decoupled the b_g2_msm computation from the GPU worker's critical path, achieving a 2.4% throughput improvement (37.1s/proof vs 38.0s). However, benchmarking revealed a memory ceiling: attempting to increase synthesis parallelism from pw=10 to pw=12 caused OOM errors with RSS peaking at 668 GiB.

The assistant initially concluded this was a "systemic memory capacity issue, not a leak" — the system simply didn't have enough RAM for more concurrent partitions. The user, however, asked a sharper question: could the PendingProofHandle — the Rust-side struct that holds data until proof finalization — be holding memory unnecessarily? The PendingProofHandle was a new addition in Phase 12, designed to keep data alive while the background b_g2_msm thread ran. If it held data that the background thread didn't actually need, that memory could be freed early.

This question reveals an important dynamic in the conversation: the user is acting as a critical reviewer, challenging the assistant's assumption that the OOM was purely a capacity problem. The user suspects there might be structural waste in the new design, and their question prompts a targeted investigation.

The Investigative Methodology

The assistant's response demonstrates a rigorous, evidence-based approach to answering the question. Rather than speculating, the assistant systematically traces the data dependencies of the background prep_msm_thread through the C++ CUDA code.

Step 1: Reading the Rust struct definition. The assistant reads the PendingProofHandle struct to understand what data it holds: the opaque C++ handle, the number of circuits, and the Rust-side data including provers (containing the massive a, b, c NTT evaluation vectors), input_assignments, aux_assignments, and random scalars.

Step 2: Tracing the C++ background thread. Using awk and grep commands, the assistant extracts the body of the prep_msm_thread lambda function from groth16_cuda.cu. It searches for references to prover.a, prover.b, prover.c, inp_assignment_data, aux_assignment_data, and the density trackers.

Step 3: Identifying what is NOT accessed. The critical discovery: grep -n "prover\.a[^_u]" returns no results — the background thread never reads prover.a, prover.b, or prover.c. These are the NTT evaluation vectors, each approximately 4 GiB (130 million Scalar elements × 32 bytes). Combined, they represent ~12 GiB per partition.

Step 4: Confirming GPU lifecycle. The assistant traces where a, b, c ARE used: only in the GPU kernel region (lines 847-1195 of the CUDA file), where they are registered with cudaHostRegister, transferred to the GPU via cudaMemcpyAsync, and unregistered with cudaHostUnregister. All of this happens inside generate_groth16_proofs_start_c — the C++ function that returns before the PendingProofHandle is even created. By the time prove_start returns to Rust, the GPU has finished reading these vectors.

Step 5: Quantifying the waste. The assistant calculates the sizes: each ProvingAssignment contains a, b, c as Vec<Scalar> with ~130 million elements each at 32 bytes per element, totaling ~12 GiB per partition. The background thread needs only the aux_assignments (~4 GiB), input_assignments (~10 KB), and density trackers (~48 MB) — but NOT the NTT evaluation vectors.

The Decision: Where to Free

The assistant considers two approaches: adding a method on PendingProofHandle that drops the vectors, or freeing them directly in prove_start before returning. The latter is chosen as "cleaner" because it leverages the known lifecycle: the GPU is done with a/b/c by the time prove_start returns, so freeing them immediately is safe and maximally efficient.

This decision reflects a key design principle: free memory as soon as its last consumer is done, not when the containing struct is destroyed. The PendingProofHandle was designed to keep data alive until finalization, but the assistant realizes that different fields have different lifetimes. The a/b/c vectors have the shortest lifetime (GPU kernel only), while aux_assignments and density trackers have longer lifetimes (needed by the background thread).

Assumptions and Potential Pitfalls

The assistant makes several assumptions that deserve scrutiny:

  1. That cudaHostUnregister implies GPU completion. This is a reasonable assumption in CUDA: cudaHostUnregister on registered host memory is safe only after all pending GPU operations on that memory have completed. However, the assistant doesn't verify that explicit synchronization (e.g., cudaStreamSynchronize) has occurred before the unregister calls. If there were a bug in the CUDA code where unregister happened before kernel completion, freeing the Rust vectors could cause use-after-free on the GPU.
  2. That the background thread doesn't access a/b/c through indirect paths. The grep search is thorough but not exhaustive — it checks for direct field access patterns. If the background thread accessed these fields through a pointer or iterator that wasn't captured by the grep pattern, the conclusion could be wrong.
  3. That the memory savings will materially impact OOM behavior. The assistant estimates ~12 GiB per partition freed. With pw=12 and up to 12 concurrent partitions, that's up to 144 GiB of potential savings — which could bring the peak from 668 GiB down to ~524 GiB, well within the 755 GiB limit. However, the actual impact depends on the overlap of partition lifetimes and the scheduler's behavior.

Input Knowledge Required

To fully understand this message, the reader needs:

  1. Understanding of the Phase 12 split API. The PendingProofHandle is a new construct that holds data across the start/finalize boundary. Without knowing this, the question of "what can be freed early" doesn't arise.
  2. Knowledge of CUDA memory management. The concepts of cudaHostRegister, cudaHostUnregister, pinned memory, and the lifecycle of GPU operations are essential to understanding why a/b/c are safe to free after prove_start returns.
  3. Familiarity with the Groth16 proving pipeline. The distinction between NTT evaluation vectors (a, b, c — the polynomial evaluations used in the GPU MSM) and assignment data (input_assignment, aux_assignment — used in the CPU-side b_g2_msm) is crucial.
  4. Understanding of the memory pressure context. The system has 755 GiB RAM, and the pipeline peaks at 668 GiB with pw=12. The assistant is operating in a regime where every gigabyte matters.

Output Knowledge Created

This message produces several valuable pieces of knowledge:

  1. A precise memory accounting for the PendingProofHandle. The assistant maps exactly which fields are needed by which consumer, creating a data lifecycle diagram for the handle.
  2. A quantified savings opportunity. ~12 GiB per partition can be freed early, with potential to reduce peak memory by over 100 GiB at scale.
  3. A validation of the user's intuition. The user suspected structural waste, and the investigation confirmed it — the PendingProofHandle was indeed holding memory unnecessarily.
  4. A reusable investigative pattern. The method of tracing data dependencies through grep/awk across Rust and C++ boundaries is a general technique for memory optimization in hybrid-language systems.

The Thinking Process

The assistant's reasoning in this message reveals several hallmarks of expert debugging:

Hypothesis-driven investigation. The assistant doesn't randomly search for memory savings. It starts with a specific hypothesis: "the background thread doesn't need a/b/c." It then gathers evidence to confirm or refute this.

Cross-language tracing. The data flows from Rust (where PendingProofHandle is defined) through FFI into C++ (where generate_groth16_proofs_start_c runs) and into CUDA (where GPU kernels execute). The assistant traces this entire path, using grep to follow field accesses across language boundaries.

Quantitative thinking. The assistant doesn't just identify what can be freed — it calculates the sizes: "~4 GiB each, ~12 GiB total" for the NTT vectors, "~130M elements × 32 bytes" for the math. This quantification is essential for prioritizing the optimization.

Lifecycle analysis. The key insight is that different data fields have different lifetimes, even within the same struct. The PendingProofHandle was designed with a single lifetime (until finalization), but the assistant recognizes that a/b/c can be freed earlier because their consumer (the GPU kernel region) completes before the handle is even created.

Broader Implications

This message represents a turning point in the optimization project. The Phase 12 split API was designed to improve throughput by hiding latency, but it inadvertently increased memory pressure by extending data lifetimes. The user's question — and the assistant's investigation — reveals that this memory increase was not inherent to the split design, but rather an artifact of an overly conservative data retention policy.

The fix is elegant: free a/b/c immediately in prove_start, before the handle is returned. This preserves the throughput benefit of the split API (the GPU worker can pick up new work immediately) while eliminating the memory cost. It's a classic example of how performance optimization and memory optimization can be complementary rather than conflicting — when you understand the data lifecycle precisely.

Conclusion

Message 3063 is a compact but powerful example of systematic memory investigation in a complex, multi-language GPU proving system. The assistant takes a user's insightful question, traces data dependencies across Rust and C++ boundaries, identifies a ~12 GiB per-partition waste, and designs a targeted fix. The investigation reveals that the PendingProofHandle was holding the massive NTT evaluation vectors (a, b, c) unnecessarily — the background thread doesn't need them, and the GPU is done with them before the handle is even created.

The message demonstrates the value of questioning assumptions about data lifetimes, the power of cross-language tracing with simple tools like grep and awk, and the importance of quantifying memory savings to prioritize optimization work. It's a reminder that in high-performance systems, the difference between "working" and "OOM" often comes down to understanding exactly when each byte can be freed.