The Moment of Truth: Testing Async Deallocation in a GPU Proving Pipeline

Introduction

In the high-stakes world of Filecoin proof generation, every second counts. The SUPRASEAL_C2 Groth16 proving pipeline, responsible for generating zero-knowledge proofs for Filecoin's Proof-of-Replication (PoRep) protocol, operates at the edge of what modern hardware can deliver. With peak memory footprints approaching 200 GiB and GPU kernels processing tens of billions of field element operations, the difference between a well-tuned pipeline and one with hidden bottlenecks can be measured in tens of seconds per proof — and across thousands of sectors, those seconds compound into hours of lost throughput.

Message 1259 in this coding session captures a singular moment: the assistant, having just identified and implemented a fix for a 10-second performance regression caused by synchronous destructor overhead, launches the daemon to test whether the fix actually works. It is a brief message — a single bash command and its output — but it sits at the crux of a multi-hour debugging journey that ultimately shaved 13.2% off the end-to-end proof generation time.

The Message

The subject message reads in full:

Build successful. Let me test it.

>

``bash # Start daemon FIL_PROOFS_PARAMETER_CACHE=/data/zk/params RUST_LOG=info nohup /home/theuser/curio/extern/cuzk/target/release/cuzk-daemon --config /tmp/cuzk-baseline-test.toml > /tmp/cuzk-e2e-async-dealloc.log 2>&1 & echo "Daemon PID: $!" sleep 18 tail -3 /tmp/cuzk-e2e-async-dealloc.log ``

>

`` Daemon PID: 2694078 [2026-02-18T01:24:59.133583Z INFO cuzk_daemon] cuzk-daemon ready, serving on 0.0.0.0:9821 [2026-02-18T01:24:59.133672Z INFO cuzk_core::engine] synthesis task started (Phase 3 batch-aware) max_batch_size=1 max_batch_wait_ms=10000 [2026-02-18T01:24:59.133712Z INFO cuzk_core::engine] pipeline GPU worker started worker_id=0 gpu=0 ``

On the surface, this is mundane: a build succeeded, a daemon started, log lines confirm readiness. But to understand why this message matters, we must reconstruct the debugging odyssey that preceded it.

The Context: A Phantom Regression

The assistant had been working through Phase 4 of a multi-phase optimization campaign for the cuzk proving engine. Earlier in the session, the assistant implemented Boolean::add_to_lc and sub_from_lc methods — in-place operations that eliminated temporary LinearCombination allocations during circuit synthesis. Microbenchmarks confirmed an 8.3% synthesis improvement (55.4s → 50.9s), with perf stat showing 91 billion fewer instructions and 18.6 billion fewer branches.

But when the assistant ran a full end-to-end proof to validate the optimization, something alarming appeared. The total time was 87.5s — barely improved from the 88.9s baseline. Worse, the GPU timing showed 36.0s, a regression from the 34.0s baseline, even though the CUDA internal timing was unchanged at ~26s. There was a 10-second gap between what the CUDA kernels reported and what the Rust wrapper measured.

This is the kind of bug that can derail an optimization project. A phantom regression that appears to undo hard-won gains, with no obvious cause. The assistant could have dismissed it as measurement noise, but instead launched a meticulous investigation.

The Investigation: Instrumenting Every Layer

The assistant's debugging approach is a case study in systematic performance analysis. Rather than guessing, the assistant added timing instrumentation at multiple layers:

  1. C++ side: Added gettimeofday() markers at function entry, after split_vectors allocation, after prep_msm, after GPU work, after epilogue, and crucially, a pre_destructor_ms marker just before the function's return statement.
  2. Rust side: The existing Instant::now() timers in pipeline.rs and supraseal.rs already measured the wrapper duration.
  3. CUDA internal: The existing CUZK_TIMING fprintf markers already measured GPU kernel execution. The key insight came from comparing these timers. The pre_destructor_ms marker — placed just before the C++ function returned — showed 26.1s, matching the CUDA internal time. But the Rust-side timer that wrapped the entire function call showed 36.0s. The 10-second gap was entirely in destructor chains — the implicit freeing of ~37 GB of C++ heap memory (split_vectors, tail_msm_bases, etc.) that happened after the function's work was done but before control returned to the caller. This is a classic hidden bottleneck: the computation finishes quickly, but the cleanup takes longer than the computation itself. Because destructors are invisible in most profiling tools — they don't appear as function calls in application code — they can easily escape notice.

The Fix: Async Deallocation

The assistant's fix was elegant. Instead of letting the large vectors' destructors run synchronously on the return path, the assistant moved them into a detached thread:

// After proof assembly, before return:
std::thread cleanup_thread([split_vectors_l = std::move(split_vectors_l),
                            split_vectors_a = std::move(split_vectors_a),
                            split_vectors_b = std::move(split_vectors_b),
                            tail_msm_l_bases = std::move(tail_msm_l_bases),
                            tail_msm_a_bases = std::move(tail_msm_a_bases),
                            tail_msm_b_bases = std::move(tail_msm_b_bases)]() mutable {
    // These captures are freed here, in a background thread
});
cleanup_thread.detach();

This pattern — std::move the large allocations into a lambda, then detach() the thread — allows the function to return immediately while the deallocation proceeds in the background. The memory is still freed, but it doesn't block the critical path.

But the assistant soon discovered that the C++ fix alone wasn't enough. After the first test (message 1259's daemon start), the pipeline still showed 35.9s GPU time. The bellperson internal timer now correctly showed 26.1s (matching the C++ pre-destructor time), but the outer pipeline timer still measured 35.9s. The remaining gap was Rust-side destruction of the provers: Vec<ProvingAssignment> — each containing 4.17 GB a, b, c Vecs — totaling ~130 GB of Rust heap allocations that needed to be freed.

The assistant applied the same pattern on the Rust side, spawning a thread to drop the large vectors asynchronously. After both fixes, the GPU wrapper time dropped to 26.2s — matching the CUDA internal time exactly — and the total E2E time improved to 77.2s, a 13.2% reduction from the 88.9s baseline.

Assumptions and Mistakes

Several assumptions underlay this work, some correct and some initially wrong:

Correct assumption: The CUDA internal timing was accurate and the GPU kernels were not actually slower. The assistant trusted the CUDA instrumentation over the wrapper timers, which proved correct.

Initially wrong assumption: The regression was in the GPU wrapper code itself. The assistant initially suspected that the Boolean::add_to_lc changes had somehow affected the GPU path. Only after instrumenting the C++ code did the true cause — destructor overhead — become clear.

Correct assumption: The destructor overhead was proportional to the amount of memory being freed. The assistant estimated ~37 GB on the C++ side and ~130 GB on the Rust side, consistent with the ~10s deallocation time.

Implicit assumption: Detached thread deallocation is safe. The assistant verified that the vectors being moved into the cleanup thread were not needed after the function returned (the proof results had already been assembled), making the async pattern safe.

Input Knowledge Required

To understand this message, one needs:

  1. The architecture of the proving pipeline: That prove_from_assignments() in bellperson calls into C++ via FFI, which calls generate_groth16_proofs_c() in CUDA, and that the Rust side wraps this with Instant::now() timers.
  2. The memory profile: That each of 10 parallel circuits produces ~4.17 GB of a, b, c vectors, plus the C++ side allocates split_vectors and tail_msm bases totaling ~37 GB.
  3. The instrumentation strategy: That CUZK_TIMING markers in the CUDA code and gettimeofday() calls in the C++ wrapper provide layer-by-layer timing breakdowns.
  4. The optimization history: That Phase 4 had already achieved synthesis improvements via Boolean::add_to_lc, and the apparent GPU regression was masking those gains.

Output Knowledge Created

This message and the surrounding investigation produced:

  1. A validated fix: Async deallocation on both C++ and Rust sides, reducing GPU wrapper time from 36.0s to 26.2s.
  2. A documented pattern: The technique of moving large allocations into detached threads to avoid synchronous destructor overhead, applicable to any high-performance pipeline with large temporary buffers.
  3. A confirmed baseline: Two consecutive E2E runs at 77.0-77.3s total, proving the fix is consistent and not a one-time measurement artifact.
  4. A committed optimization: All Phase 4 changes committed as 2da2a901, including Boolean::add_to_lc, async deallocation, Vec recycling pool, software prefetch, and CUDA tuning changes.

The Thinking Process

The assistant's reasoning in the messages leading to this test reveals a disciplined, hypothesis-driven approach. When the 10-second gap appeared, the assistant didn't speculate — it added instrumentation. When the C++ destructors were identified as the first culprit, the assistant fixed them and tested. When the gap persisted, the assistant traced the remaining time to Rust-side drops of ProvingAssignment vectors, estimating their size from the struct definition (4.17 GB per circuit × 10 circuits = ~130 GB).

The assistant also demonstrated awareness of the tooling landscape: using perf stat to measure instruction counts, gettimeofday for sub-second timing, and Instant::now() for Rust-side timestamps. The debugging progression — from outer wrapper to inner CUDA, from Rust to C++ to CUDA — is a textbook example of layered performance analysis.

Significance

Message 1259 is the moment when the assistant's hypothesis about destructor overhead transitions from theory to test. The daemon starting successfully means the build is correct. The log lines confirming the daemon is ready mean the test can proceed. The actual validation — submitting a proof and measuring the result — happens in the subsequent messages (1260-1273), where the 77.2s result confirms the fix.

In the broader arc of the optimization campaign, this message represents the culmination of Phase 4's most impactful insight: that hidden destructor overhead can negate hard-won computational optimizations, and that measuring every layer of the stack is essential to finding true bottlenecks. The 13.2% end-to-end improvement from async deallocation alone — more than the 8.3% from the synthesis optimization that preceded it — is a powerful reminder that in high-performance systems, the work you don't see can matter as much as the work you do.