The 10-Second Silent Killer: How Async Deallocation Unlocked an 11.6% Performance Gain in Groth16 Proof Generation

Introduction

In the relentless pursuit of performance optimization, the most impactful gains often come not from clever algorithms or parallelization, but from eliminating hidden overhead that everyone assumed was unavoidable. Message 1271 in this opencode coding session captures one such moment: a 10.2-second improvement in end-to-end Groth16 proof generation time, achieved not by making anything compute faster, but by fixing how memory gets freed.

The message reads:

MASSIVE improvement!

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- Total: 77.3s (down from 87.5s — 10.2s faster, 11.6% improvement) - GPU: 26.2s (down from 36.0s — matches CUDA internal time perfectly!) - Synthesis: 50.8s (consistent with add_to_lc optimization)

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Let me verify the timing details: [bash] grep "CUZK_TIMING\|GPU prove time\|gpu_ms\|synthesis time" /tmp/cuzk-e2e-async-dealloc2.log ... synthesis time: 50.796762943s CUZK_TIMING: split_vectors_ms=0 setup_to_split_ms=0 CUZK_TIMING: prep_msm_ms=1757 CUZK_TIMING: gpu_tid=0 ntt_msm_h_ms=23474 CUZK_TIMING: b_g2_msm_ms=23139 num_circuits=10 CUZK_TIMING: gpu_tid=0 batch_add_ms=1477 CUZK_TIMING: gpu_tid=0 tail_m...

This message is the triumphant conclusion of a diagnostic journey that began with a puzzling discrepancy: the CUDA kernel timings showed ~26 seconds of GPU compute, but the Rust pipeline wrapper measured ~36 seconds for the same GPU proving phase. That 10-second gap was the silent killer — and this message announces its demise.

The Context: A Multi-Phase Optimization Campaign

To understand why this message matters, we must situate it within the broader optimization campaign. The subject is the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep), a computationally intensive process that synthesizes circuit constraints and then performs multi-scalar multiplication (MSM) and number-theoretic transform (NTT) operations on GPUs. The pipeline processes 10 parallel circuits, each requiring ~4 GB of constraint vectors, totaling ~130 GB of heap allocations across the Rust and C++ boundary.

The optimization effort was organized into phases. Phase 1 had established the baseline. Phase 2 introduced an async overlap pipeline between synthesis and GPU proving. Phase 3 implemented cross-sector batching. Phase 4, the current phase, was focused on compute-level optimizations: SmallVec for the LC Indexer, pre-sizing for ProvingAssignment, parallelizing B_G2 CPU MSMs, pinning vectors with cudaHostRegister, and per-MSM window tuning. Several of these had already been implemented, benchmarked, and in some cases reverted due to regressions.

But a stubborn problem remained. The Boolean::add_to_lc optimization had shaved ~4.5 seconds off synthesis time (from ~55.4s to ~50.9s), a respectable 8.3% improvement. Yet the end-to-end time showed only a modest gain because the GPU wrapper time had apparently regressed from ~34s to ~36s. This was the puzzle that needed solving.

The Discovery: Destructor Overhead as the Hidden Bottleneck

The key insight, developed over messages 1248–1270, was that the apparent GPU regression was an illusion. The CUDA internal timings were stable at ~26 seconds across runs. The extra 10 seconds was not in GPU computation at all — it was in the destructor chain that fired when large C++ vectors went out of scope at the end of groth16_cuda.cu's generate_groth16_proof function.

The vectors in question were enormous. The split_vectors structures (containing bit_vector and tail_msm_scalars) and the tail_msm_bases together held approximately 37 GB of heap memory on the C++ side. When the function returned, the implicit destructors for these std::vector instances had to traverse and free all that memory — a synchronous operation that blocked the calling thread for ~10 seconds.

But the problem was even deeper. On the Rust side, prove_from_assignments in bellperson's supraseal.rs received ownership of Vec<ProvingAssignment> containing a, b, and c vectors — each 4.17 GB for 130 million 32-byte field elements. Across 10 circuits, that's approximately 132 GB of Rust Vec allocations. When prove_from_assignments returned its result, these vectors were dropped, triggering another synchronous deallocation cascade.

The total picture was staggering: approximately 170 GB of heap memory being freed synchronously, blocking the main thread for 10–12 seconds after GPU computation completed. This overhead had been present since Phase 2 and Phase 3 baselines — it was simply never measured because the pipeline timer wrapped the entire prove_from_assignments call, conflating GPU compute with destructor cleanup.

The Fix: Async Deallocation on Both Sides of the FFI Boundary

The solution, implemented across messages 1253–1268, was elegantly simple: move the large vectors into detached threads that free them asynchronously, allowing the function to return immediately while deallocation happens in the background.

On the C++ side (message 1256–1257), the fix involved capturing the large vectors (split_vectors_l, split_vectors_a, split_vectors_b, tail_msm_bases_l, tail_msm_bases_a, tail_msm_bases_b) into a lambda that runs in a detached std::thread. The gettimeofday timing was adjusted to measure the actual deallocation time within the thread, providing visibility into how long the background cleanup takes.

On the Rust side (message 1267), the same pattern was applied in supraseal.rs: after generate_groth16_proof returns, the function arguments (provers, input_assignments, aux_assignments) are moved into a std::thread::spawn closure that drops them in the background. The function returns immediately with the proofs, while the ~132 GB of Rust heap is freed concurrently.

The Results: Validating the Hypothesis

Message 1271 reports the outcome of this fix. The numbers speak for themselves:

The Thinking Process: From Symptom to Root Cause

This message is remarkable not just for the performance gain, but for the diagnostic rigor that preceded it. The assistant's thinking, visible across the preceding messages, demonstrates a systematic approach to performance analysis:

  1. Measure at every layer: The assistant had instrumented both the C++ code (with CUZK_TIMING macros) and the Rust code (with bellperson's internal timer and the pipeline's gpu_duration). This multi-layer instrumentation was essential — without it, the 10-second gap between CUDA internal time and Rust wrapper time would have remained invisible.
  2. Formulate and test hypotheses: When the apparent GPU regression appeared (36s vs 34s baseline), the assistant did not jump to conclusions. Instead, it examined the CUDA internal timings and found they were stable at ~26s. This led to the hypothesis that destructor overhead was the culprit.
  3. Quantify the problem: The assistant estimated the memory being freed: ~37 GB of C++ vectors and ~132 GB of Rust Vecs. These concrete numbers confirmed the hypothesis was plausible.
  4. Iterate the fix: The first attempt (message 1256–1257) only addressed the C++ side. When testing showed the Rust-side gap remained (bellperson timer at 26.1s but pipeline timer at 35.9s), the assistant traced the remaining 9.7s to Rust Vec drops and applied the same async pattern there (message 1267).
  5. Validate with timing: After each fix, the assistant verified not just the end-to-end time but the detailed timing breakdown, ensuring the fix was working as expected and not masking other issues.

Assumptions and Decisions

Several assumptions underpin this work. The assistant assumed that split_vectors (a custom class in groth16_split_msm.cu) was movable — it contains std::vector members which are move-constructible by default, but the class doesn't define custom move constructors. This assumption proved correct, as the build succeeded and the fix worked.

The assistant also assumed that the Arc<Vec<Fr>> references in input_assignments and aux_assignments would be the last reference when dropped inside the async thread. This is a safe assumption given the ownership flow in pipeline.rs, but it's worth noting that if any other code held a reference to these Arcs, the deallocation would not actually happen in the background — the async drop would just decrement the reference count.

A key decision was to use detached threads for deallocation rather than alternatives like madvise(MADV_DONTNEED) or explicit clear() + shrink_to_fit(). The thread approach is simpler and works on both C++ and Rust sides, but it does mean that memory pressure persists slightly longer (until the background thread finishes freeing). For a system that immediately starts the next proof, this could theoretically increase peak memory. However, the assistant's memory monitoring showed no regression.

Input Knowledge Required

To fully understand this message, one needs knowledge of:

Output Knowledge Created

This message creates several pieces of knowledge:

  1. A validated optimization technique: Async deallocation of large vectors at FFI boundaries is proven effective, recovering 10 seconds that would otherwise be wasted.
  2. A benchmark result: The Phase 4 end-to-end time of 77.3s for a PoRep C2 proof (10 circuits, 32 GiB sector) serves as a new baseline for further optimization.
  3. A diagnostic methodology: The importance of instrumenting timing at every layer (CUDA internal, C++ wrapper, Rust wrapper, pipeline) is demonstrated. Without this layered instrumentation, the destructor overhead would have remained invisible.
  4. A composable optimization: This fix combines with the earlier Boolean::add_to_lc optimization (synthesis improvement) and the Phase 2/3 pipeline improvements (async overlap, cross-sector batching) to produce a cumulative 13.2% improvement from the Phase 4 baseline.

Conclusion

Message 1271 is a testament to the power of profiling-driven optimization. The 10.2-second improvement came not from making anything compute faster, but from eliminating a hidden cost that everyone had been paying without noticing. The destructor overhead had been present since the beginning of the optimization campaign — it was simply masked by the way timers were placed.

The deeper lesson is about measurement. The assistant's insistence on instrumenting timing at every layer — CUDA kernels, C++ functions, Rust FFI wrappers, and pipeline orchestration — created the visibility needed to identify the bottleneck. Without the CUZK_TIMING macros showing that GPU compute was only 26 seconds, the 36-second pipeline timer would have been accepted as "the GPU time" and optimization efforts would have focused on the wrong target.

This message also illustrates the importance of questioning assumptions. The apparent GPU regression (36s vs 34s) could have been dismissed as run-to-run variance or blamed on the Boolean::add_to_lc changes. Instead, the assistant dug deeper, found the real cause, and eliminated it. The result is a cleaner, faster pipeline where every second of measured time corresponds to actual useful work.