Testing the Async Deallocation Fix: A Diagnostic E2E Proof in the cuzk Proving Engine

Introduction

In the relentless pursuit of optimizing Filecoin's Groth16 proof generation pipeline, every millisecond counts — and sometimes, the most significant gains hide not in computation but in cleanup. Message 1260 of this opencode session captures a pivotal diagnostic moment: the first end-to-end test of an asynchronous deallocation fix designed to eliminate a 10-second destructor overhead that had been silently inflating GPU wrapper times. This message is a bash command execution that submits a PoRep (Proof-of-Replication) proof through the cuzk proving engine while monitoring memory usage, and its output reveals both progress and the stubborn persistence of a remaining bottleneck.

The context is Phase 4 of a multi-phase optimization campaign targeting the cuzk proving engine, which implements Groth16 proofs for Filecoin's storage verification. Earlier phases had already achieved significant throughput improvements: Phase 2 introduced async overlap between synthesis and GPU proving (1.27×), and Phase 3 implemented cross-sector batching (1.46×). Phase 4 turned to compute-level optimizations, including Boolean::add_to_lc methods that reduced synthesis time by 8.3%, and — most critically — an investigation into a mysterious 10-second gap between CUDA's internal timing (~26s) and the Rust-side measurement (~36s) of GPU proving time.

The 10-Second Mystery

The investigation preceding this message (messages 1231–1259) had been a masterclass in layered instrumentation. The assistant had traced the 10-second gap through multiple levels of abstraction: from the Rust Instant::now() timers in pipeline.rs, through the prove_from_assignments function in bellperson's supraseal.rs, into the C++ FFI boundary, and finally into the CUDA kernel code itself. By adding CUZK_TIMING markers at strategic points in groth16_cuda.cu, the assistant pinpointed the culprit: synchronous destructor overhead.

The numbers were staggering. The C++ side held approximately 37 GB of heap-allocated vectors — split_vectors for L, A, B, and their associated tail_msm_bases — each containing bit-vectors and scalar data for the multi-scalar multiplication (MSM) computations. When the generate_groth16_proofs_c function returned, the implicit destructor chain for these vectors ran synchronously, blocking the calling thread for roughly 10 seconds. The CUDA kernels themselves had completed in 26.1 seconds, but the Rust caller didn't regain control until 36.2 seconds had elapsed.

The fix, implemented in the immediately preceding messages, was elegant: instead of allowing the large vectors to be destroyed in the function's epilogue, the assistant moved them into a detached thread that would free the memory asynchronously. The function would return immediately, and the deallocation would happen in the background, invisible to the timing measurement.

The Message: An E2E Proof with Memory Monitoring

Message 1260 is the first test of this fix. The assistant executes:

bash /tmp/cuzk-memmon.sh > /tmp/cuzk-async-memmon.log 2>&1 &
MEMMON_PID=$!

time /home/theuser/curio/extern/cuzk/target/release/cuzk-bench --addr http://127.0.0.1:9821 single --type porep --c1 /data/32gbench/c1.json 2>&1

kill $MEMMON_PID 2>/dev/null
echo "=== Memory peak ==="
cat /tmp/cuzk-memmon.csv 2>/dev/null | sort -t, -k2 -n | tail -1

The command starts a memory monitoring script in the background, submits a single PoRep proof via the cuzk-bench tool, then kills the monitor and prints the peak memory usage. The memory monitoring is a new addition — the assistant wants to understand not just timing but also the memory footprint of the pipeline, particularly whether the async deallocation creates any memory pressure by delaying the freeing of 37 GB of C++ vectors.

The output reveals the proof result:

=== Proof Result ===
status:    COMPLETED
job_id:    ead3b44d-045f-4000-9c21-18dd8722112d
timings:   total=86943 ms (queue=244 ms, srs=0 ms, synth=50839 ms, gpu=35858 ms)
wall time: 87055 ms
proof:     1920 bytes (hex: b5ccb9ebd8...

The proof completed successfully — correctness is preserved. The synthesis time of 50.8 seconds reflects the Boolean::add_to_lc optimization working as expected (down from ~55.4s baseline). But the GPU time of 35.9 seconds tells a more complicated story.

Reading the Results: Progress and a Remaining Bottleneck

The GPU time of 35,858 ms is only a marginal improvement from the baseline of ~36.0s. The async deallocation fix on the C++ side should have eliminated the ~10s destructor overhead, bringing the GPU wrapper time down to match the CUDA internal time of ~26s. Yet the measurement stubbornly remains near 36s.

This is the critical diagnostic insight of this message. The C++ async deallocation fix alone was insufficient because it only addressed one layer of the destructor chain. The Rust side holds its own massive allocations: the ProvingAssignment vectors (a, b, c, aux_assignment) that together total approximately 130 GB across 10 parallel circuits. These Rust Vecs are owned by the prove_from_assignments function and are freed when that function returns — synchronously, on the same thread, blocking the Rust caller.

The assistant had focused on the C++ destructors because the instrumentation had shown the gap between the CUDA pre_destructor marker (26.1s) and the Rust measurement (36.2s). But the pre_destructor marker was placed just before the C++ return ret statement. The Rust-side destructors fire after the C++ function returns, as the Rust caller drops its own vectors. These 130 GB of Rust allocations contribute their own ~10s of deallocation time.

The memory monitoring in this message serves another purpose: it helps the assistant understand whether the async deallocation creates memory pressure. If the C++ vectors are freed in a background thread, their memory remains resident (as RSS) until the thread actually runs and completes the deallocation. This could cause peak memory to increase if the next proof generation starts before the cleanup thread finishes. The memory peak data (not fully visible in the truncated output) would inform whether this is a concern.

The Thinking Process Visible in the Message

The assistant's reasoning is evident in the structure of this test. First, the decision to add memory monitoring alongside the timing measurement shows a holistic understanding of the optimization problem: performance improvements must not come at the cost of uncontrolled memory growth, especially in a production proving engine that may handle multiple concurrent proofs.

Second, the choice to run a single proof rather than a batch is deliberate. The assistant needs a clean measurement of the fix's effect without the confounding variables of batch overlap. The max_batch_size=1 configuration (visible in the daemon startup from the preceding message) ensures that synthesis and GPU proving run sequentially, giving the clearest possible timing breakdown.

Third, the assistant does not immediately conclude that the fix failed. The 35.9s GPU time is a data point, not a verdict. The message is diagnostic — it reveals that the problem has two layers (C++ and Rust destructors), and fixing only one layer is insufficient. The assistant will go on to implement a Rust-side async deallocation (using detached threads or by moving the vectors into a channel to be freed later), eventually achieving the 77.2s total time with GPU wrapper time matching the CUDA internal ~26s.

Input Knowledge Required

To fully understand this message, one needs knowledge of several layers of the proving pipeline:

  1. The Groth16 proof flow: The pipeline involves synthesis (building the circuit and computing witness assignments) followed by GPU proving (NTT, MSM, and proof assembly). These phases are overlapped in the production pipeline but run sequentially in this test.
  2. The memory architecture: Each of the 10 parallel circuits in a PoRep proof generates approximately 4.17 GB of ProvingAssignment data, totaling ~130 GB across all circuits. The C++ side adds another ~37 GB of split vectors and MSM bases. These numbers are essential for understanding why destructors take 10 seconds.
  3. The instrumentation framework: The CUZK_TIMING markers added in previous messages provide the layer-by-layer timing breakdown that informed this fix. Without those markers, the 10-second gap would remain mysterious.
  4. The ownership model: The Rust prove_from_assignments function receives ownership of the ProvingAssignment vectors and drops them when it returns. The C++ generate_groth16_proofs_c function similarly owns the split vectors. Understanding which layer owns which allocation is critical for targeting the fix.
  5. The async deallocation pattern: Moving large vectors into a std::thread with std::move captures allows the function to return immediately while deallocation happens in the background. This is a well-known pattern for avoiding destructor-induced latency spikes.

Output Knowledge Created

This message produces several pieces of actionable knowledge:

  1. Confirmation of correctness: The proof completes successfully with the async deallocation fix. The 1920-byte proof is valid, confirming that moving vectors into a detached thread does not corrupt data (since the vectors are no longer needed after the proof is assembled).
  2. Evidence of an incomplete fix: The GPU time of 35.9s demonstrates that the C++ async deallocation alone does not solve the problem. The remaining ~10s of overhead must come from another layer — the Rust-side ProvingAssignment destructors.
  3. Synthesis improvement validated: The 50.8s synthesis time confirms that the Boolean::add_to_lc optimization is effective, showing an ~8% improvement from the ~55.4s baseline.
  4. Memory monitoring infrastructure: The addition of /tmp/cuzk-memmon.sh creates a reusable tool for tracking peak memory during proof generation, which will be valuable for evaluating the memory impact of future optimizations.

Assumptions and Their Validity

The assistant made a reasonable but ultimately incomplete assumption: that the 10-second destructor overhead was entirely in the C++ layer. The instrumentation had shown a gap between the CUDA pre_destructor marker (26.1s) and the Rust measurement (36.2s), and the most natural explanation was that the C++ destructors for the 37 GB of split vectors were responsible.

However, this assumption overlooked the Rust-side destructors. The ProvingAssignment vectors are freed when prove_from_assignments returns, which happens after the C++ function returns but before the Rust Instant::now() measurement stops. The Rust destructors for ~130 GB of data contribute their own ~10s of overhead, and the C++ fix alone cannot address them.

This is not a mistake in the traditional sense — it's an iterative discovery process. The assistant correctly identified and fixed one layer of the problem, and this diagnostic test revealed the remaining layer. The full fix will require applying the same async deallocation pattern on the Rust side, either by moving the ProvingAssignment vectors into a background thread or by using a channel-based approach to defer their deallocation.

Conclusion

Message 1260 is a testament to the importance of measurement-driven optimization. The assistant did not assume the fix worked — they tested it, measured it, and let the data speak. The 35.9s GPU time was not a failure; it was a revelation that the problem had a second layer. This diagnostic approach — instrument, hypothesize, fix, test, and re-analyze — is the hallmark of serious performance engineering.

The message also demonstrates the value of memory monitoring alongside timing measurements. In a production proving engine that may handle multiple concurrent proofs, understanding peak memory is as important as understanding throughput. The async deallocation pattern, while effective at reducing latency, could potentially increase peak memory if cleanup threads fall behind. The memory monitoring infrastructure established here will be essential for evaluating this tradeoff.

Ultimately, this message is a pivot point. It confirms that the synthesis optimizations are working (50.8s, down from 55.4s), but it reveals that the GPU wrapper optimization is only half-complete. The assistant will go on to implement Rust-side async deallocation, achieving a final GPU wrapper time of 26.2s (matching CUDA internal time exactly) and a total E2E time of 77.2s — a 13.2% improvement from the 88.9s baseline. But that success is built on the diagnostic foundation laid in this message: the willingness to test, measure, and discover that the problem was deeper than it first appeared.