The 10-Second Dealloc: How Async Destruction Unlocked a 13.2% Performance Win in Groth16 Proving

In the high-stakes world of Filecoin proof generation, every second counts. When the assistant in this opencode session ran an end-to-end benchmark of the cuzk proving pipeline and saw total=77294 ms — a 77.3-second proof — it represented the culmination of a meticulous debugging journey that had uncovered and eliminated a hidden 10-second tax on every proof. The message at [msg 1270] is the payoff: a single bash command that submits a PoRep C2 proof to the cuzk daemon and reports the result. But behind those numbers lies a story of instrumentation, hypothesis-driven optimization, and the surprising cost of simply freeing memory.

The Message

The assistant executes a shell script that launches a memory monitor in the background, submits a proof via the cuzk-bench CLI tool, and then reports the peak memory usage:

[assistant] [bash] bash /tmp/cuzk-memmon.sh > /tmp/cuzk-async2-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
2026-02-18T01:29:41.692370Z  INFO cuzk_bench: loading C1 output path=/data/32gbench/c1.json
2026-02-18T01:29:41.711184Z  INFO cuzk_bench: submitting proof proof_type=porep

=== Proof Result ===
status:    COMPLETED
job_id:    84d0155d-777c-4619-901f-4388605b3aa5
timings:   total=77294 ms (queue=248 ms, srs=0 ms, synth=50797 ms, gpu=26248 ms)
wall time: 77420 ms
proof:     1920 bytes (hex: 97fe01e49c...

The critical numbers are synth=50797 ms (50.8 seconds for circuit synthesis) and gpu=26248 ms (26.2 seconds for GPU proving). The total of 77.3 seconds represents a 13.2% improvement over the 88.9-second baseline established earlier in Phase 4.

Context: The Discovery of Hidden Destructor Overhead

To understand why this message matters, we must trace the investigation that led to it. The assistant had been working through a series of optimizations for the cuzk Groth16 proving pipeline, targeting both CPU synthesis and GPU proving phases. After implementing the Boolean::add_to_lc synthesis optimization ([msg 1246]), an end-to-end test showed total=86932 ms with gpu=36164 ms ([msg 1248]). But the CUDA-internal timing, captured by the CUZK_TIMING instrumentation, showed only gpu_total_ms=26126 ([msg 1249]). There was a 10-second gap between what the GPU reported and what the Rust pipeline measured.

The assistant's reasoning in [msg 1250] is sharp: "So: pre_destructor_ms = 26.1s, but Rust side measures 36.2s. That means 10.0 seconds is in destructors — the time between return ret and when the Rust caller gets control back." This was the key insight. The C++ function generate_groth16_proof was completing its work in 26.1 seconds, but the implicit destructor calls for the ~37 GB of heap-allocated vectors (split_vectors, tail_msm_*_bases) were taking 10 seconds to run after the function returned.

The Fix: Async Deallocation on Both Sides

The assistant's response was to implement asynchronous deallocation. In the C++ code (groth16_cuda.cu, [msg 1256]), the large vectors were moved into a detached std::thread lambda capture, allowing the function to return immediately while the destructors ran in the background. A timing fix was also needed ([msg 1257]) to correctly measure the actual deallocation time.

But the first test of this C++ fix ([msg 1260]) showed the problem was only half-solved. The bellperson internal timer now correctly showed 26.1s (matching CUDA), but the pipeline wrapper still measured 35.9s. The assistant traced this to the Rust side: prove_from_assignments takes ownership of Vec<ProvingAssignment>, where each ProvingAssignment contains a, b, c Vecs of ~130 million field elements each — approximately 4.17 GB per vector, or 132.5 GB total across 10 circuits ([msg 1265]). When the function returned, Rust's ownership model demanded that these allocations be freed synchronously.

The fix in [msg 1267] applied the same pattern on the Rust side: spawn a detached thread that takes ownership of the large allocations and drops them in the background. After rebuilding ([msg 1268]) and restarting the daemon ([msg 1269]), the stage was set for the definitive test.

The Result: Perfect Timer Alignment

The subject message's output tells the story. The gpu=26248 ms now matches the CUDA internal time of ~26.1s exactly. The 10-second destructor tax has been eliminated. The synthesis time of 50.8 seconds reflects the Boolean::add_to_lc optimization, and the total of 77.3 seconds represents a clean 13.2% end-to-end improvement.

The next message ([msg 1271]) confirms the triumph with the assistant's exclamation: "MASSIVE improvement!" and shows the timing breakdown where all three timers — CUDA pre_destructor_ms, bellperson "GPU prove time", and pipeline gpu_ms — align at 26,249 ms. A second run ([msg 1273]) confirms consistency at 77.0 seconds.

The Thinking Process: From Symptom to Root Cause

What makes this message remarkable is the reasoning chain that preceded it. The assistant did not jump to conclusions. When the first E2E test showed a GPU regression (36.0s vs a 34.0s baseline), the assistant did not assume the optimization was faulty. Instead, it instrumented the C++ code with CUZK_TIMING markers to measure exactly where time was spent ([msg 1249]). This revealed that the CUDA compute time was stable at ~26s — the regression was an illusion caused by destructor variance.

The assistant then examined historical data ([msg 1251]-[msg 1252]), finding that the destructor gap had existed all along (11.8s in the Phase 4 baseline). The apparent regression was just normal run-to-run variance in page deallocation. This is a textbook example of why instrumentation at every layer is essential: without the CUDA timing markers, the assistant might have wasted time debugging a non-existent regression.

The decision to use detached threads for deallocation rather than alternatives like madvise(MADV_DONTNEED) or explicit clear() calls was pragmatic. Detached threads are simple, require no kernel interaction, and allow the memory to be freed asynchronously without any risk of use-after-free (since ownership is transferred into the lambda). The assistant verified that split_vectors was movable by checking that it contained only std::vector members with default move constructors ([msg 1258]).

Assumptions and Correctness

The assistant made several assumptions that proved correct:

The destructor overhead was purely computational, not I/O-bound. This was confirmed by the 10-second gap being consistent across runs and proportional to the volume of memory (~37 GB C++ + ~132 GB Rust).
Moving vectors into a detached thread is safe. This holds because the vectors are no longer needed after the proof is assembled — the epilogue (proof assembly) takes only 10ms ([msg 1249]), and after that, the data has been consumed.
The Rust Arc<Vec<Fr>> references would be the last reference when dropped. This was correct because the SynthesizedProof struct passes ownership of these Arcs into prove_from_assignments, and no other reference is retained. One subtle mistake was in the initial C++ timing implementation ([msg 1257]): the assistant realized that the gettimeofday calls inside the lambda would not capture the actual deallocation because the destruction happens when the lambda's captures go out of scope (when the lambda returns), not when the thread is spawned. This was fixed by restructuring the timing.

Input and Output Knowledge

To fully understand this message, one needs:

Groth16 proof generation pipeline: Knowledge that Filecoin PoRep proofs involve synthesizing circuits (converting R1CS constraints into polynomial evaluations) and then running GPU-based multi-scalar multiplications (MSMs) and number-theoretic transforms (NTTs).
The cuzk architecture: Understanding that the pipeline separates synthesis (CPU-bound, ~50s) from GPU proving (~26s), with an async channel between them.
Rust ownership model: The insight that dropping large Vecs is synchronous and can dominate function return time.
C++ RAII destructor semantics: That std::vector destructors are synchronous and run when the object goes out of scope. The output knowledge created by this message is:
Quantified proof that async deallocation eliminates 10s of overhead from the GPU wrapper function.
Validation that the Boolean::add_to_lc synthesis optimization is real and consistent (~50.8s synthesis time).
A new baseline of 77.3s for a single PoRep C2 proof, representing a 13.2% end-to-end improvement.
Evidence that the GPU compute time is stable at ~26s and was never actually regressed.

Broader Significance

This message exemplifies a class of optimization that is easy to overlook: the cost of doing nothing. The destructor overhead was invisible to most profiling tools because it happened after the function returned, outside any instrumented region. It was only visible because the assistant had placed timing markers at multiple layers (CUDA kernel, C++ wrapper, Rust bellperson, Rust pipeline) and could compare the deltas.

The lesson is profound: in systems that allocate and free hundreds of gigabytes per operation, the deallocation phase can dominate the tail latency even if the compute phase is well-optimized. The fix — moving deallocation into a background thread — is a pattern that generalizes to any system with large, short-lived allocations. It is the programming equivalent of cleaning up the banquet hall after the guests have left: the cleanup matters, but it should not delay the next event.

The 77.3-second proof in this message is not just a number. It is the result of a disciplined optimization process that measured first, hypothesized second, and verified third — and that refused to accept a 10-second mystery without digging until it was found.