The Ten-Second Silence: How Instrumenting C++ Destructors Uncovered a Hidden Performance Bottleneck

In the high-stakes world of Filecoin proof generation, every second counts. When the assistant observed a puzzling 10-second gap between what the GPU reported and what the Rust wrapper measured, it set off a chain of instrumentation that would uncover one of the most subtle performance killers in the SUPRASEAL_C2 Groth16 proving pipeline: synchronous destructor overhead. The pivotal moment of this investigation is captured in a single, deceptively simple command — a grep for timing markers that laid bare the hidden cost of heap deallocation.

The Mystery of the Missing Ten Seconds

The story begins with a regression. After implementing the Boolean::add_to_lc synthesis optimization — which shaved 3.5 seconds off synthesis time — the assistant ran a full end-to-end proof and discovered something troubling. The total GPU time reported by the pipeline wrapper had jumped from 34.0 seconds (the Phase 2/3 baseline) to 36.0 seconds, even though the CUDA internal timing remained rock-steady at approximately 25.7 seconds ([msg 1224]). Something between the Rust caller and the C++ CUDA kernel was consuming an extra 10 seconds that simply should not have been there.

The assistant methodically ruled out the obvious suspects. The prove_from_assignments function in bellperson's supraseal wrapper reported its own internal "GPU prove time" of 25.8 seconds, matching the CUDA instrumentation. The pipeline's gpu_prove function wrapped this call with an Instant::now() timer and got 36.0 seconds. The gap of 10.2 seconds existed entirely between the moment the CUDA kernel returned and the moment the Rust caller regained control ([msg 1226]). Proof serialization was ruled out (it took milliseconds). SRS loading was ruled out (it was cached). The only remaining hypothesis was heap deallocation: the implicit destructors running when large C++ vectors went out of scope at function exit.

Instrumenting the Void

To prove this hypothesis, the assistant needed to measure exactly how long the C++ function spent before returning control to Rust. The existing CUDA code already had timing instrumentation for the GPU phases (prep_msm, ntt_msm_h, batch_add, tail_msm, b_g2_msm), but it had no markers for the function's entry point or its epilogue. The assistant added gettimeofday calls at three strategic locations in groth16_cuda.cu ([msg 1237]): at the very start of the function (after acquiring the mutex), just before the split_vectors allocation, and — most critically — immediately before the return ret statement. This last marker, labeled pre_destructor_ms, would capture the cumulative time from function entry to the moment just before all local C++ objects began their destruction sequence.

After rebuilding the CUDA kernel and running a fresh E2E proof ([msg 1248]), the assistant extracted the timing data with a simple grep command — and that brings us to the subject message.

The Smoking Gun

The message at index 1249 is a single bash command and its output:

grep "CUZK_TIMING" /tmp/cuzk-e2e-timing-debug.log
CUZK_TIMING: split_vectors_ms=0 setup_to_split_ms=0
CUZK_TIMING: prep_msm_ms=1759
CUZK_TIMING: gpu_tid=0 ntt_msm_h_ms=23413
CUZK_TIMING: gpu_tid=0 batch_add_ms=1458
CUZK_TIMING: b_g2_msm_ms=23425 num_circuits=10
CUZK_TIMING: gpu_tid=0 tail_msm_ms=1254 gpu_total_ms=26126
CUZK_TIMING: epilogue_ms=10 pre_destructor_ms=26138

This output is the culmination of the entire investigation. Every line tells a story:

split_vectors_ms=0: The allocation of the split_vectors data structures — which hold approximately 37 GB of intermediate MSM results across 10 circuits — is instantaneous from a wall-clock perspective. Memory allocation itself is not the bottleneck.
prep_msm_ms=1759: The preprocessing phase (marking input scalars, preparing auxiliary assignments) takes 1.8 seconds. This is a known, expected cost.
ntt_msm_h_ms=23413 and tail_msm_ms=1254: The core GPU computation — Number Theoretic Transforms and Multi-Scalar Multiplications — takes 23.4 seconds and 1.3 seconds respectively. Combined with batch_add_ms=1458 (1.5 seconds), the total GPU internal time is 26.1 seconds (gpu_total_ms=26126). This matches the CUDA internal timing from previous runs perfectly.
b_g2_msm_ms=23425: The G2-side MSM runs in parallel with the GPU computation, taking 23.4 seconds. This is expected behavior in the two-threaded architecture.
epilogue_ms=10: The proof assembly phase — copying results into the output proof structure — takes a mere 10 milliseconds. This definitively rules out any post-processing overhead.
pre_destructor_ms=26138: This is the critical number. From the moment the function entered to the moment just before return ret, the elapsed time is 26,138 milliseconds — essentially identical to the GPU internal total of 26,126 milliseconds. The difference of 12 milliseconds accounts for the function entry overhead and the gettimeofday calls themselves. Now compare this to the Rust-side measurement. The gpu_prove function in pipeline.rs wrapped the entire prove_from_assignments call with an Instant::now() timer and reported 36,164 milliseconds ([msg 1248]). The difference between the Rust timer (36.2 seconds) and the C++ pre-destructor marker (26.1 seconds) is exactly 10.0 seconds. This 10-second gap is the time spent in C++ destructors, running after return ret but before the Rust caller regains control.

The Scale of the Deallocation

To understand why destructors take 10 seconds, one must appreciate the sheer volume of memory involved. The SUPRASEAL_C2 pipeline, when proving 10 circuits in parallel (the standard batch size for Filecoin PoRep), allocates approximately 37 GB of C++ heap memory across split_vectors (intermediate MSM results split across GPU threads) and tail_msm_bases (the base points for the final tail MSM computation). On top of this, the Rust side holds approximately 130 GB of ProvingAssignment vectors (a, b, c, aux_assignment) that are also freed after the GPU prove call completes.

When the C++ function returns, the destructors for all these vectors fire synchronously. Each vector's destructor calls free() or munmap() on its backing memory, which involves TLB shootdowns, page table updates, and — for large allocations — actual TLB cache misses across multiple CPU cores. On a system with limited memory bandwidth and deep NUMA hierarchies (as is typical for GPU-provisioned cloud instances), this process can take many seconds. The 10-second measurement was, in fact, a conservative estimate; the actual deallocation time could vary depending on memory pressure and kernel page reclamation policies.

Why This Matters

The discovery in message 1249 is a textbook example of why layered timing instrumentation is essential for performance engineering. The CUDA internal timers showed that the GPU was performing admirably — 26.1 seconds of compute, exactly as expected. The Rust wrapper timer showed a 36-second wall-clock time. Without the pre_destructor_ms marker, the 10-second gap would have remained a mysterious "wrapper overhead" that could have been misattributed to data transfer, context switching, or any number of other plausible but incorrect causes.

The fix that followed this discovery was elegant: move the deallocation of these large vectors into detached threads on both the C++ and Rust sides ([msg 1250]). By spawning a thread to free the memory asynchronously, the function returns immediately, and the Rust caller regains control after only the true GPU compute time. After implementing this fix, the GPU wrapper time dropped from 36.0 seconds to 26.2 seconds — matching the CUDA internal time exactly — and the total end-to-end proof time improved to 77.2 seconds, a 13.2% reduction from the 88.9-second baseline.

The Deeper Lesson

The subject message is remarkable not for its complexity — it is, after all, a simple grep command — but for what it represents. It is the moment when a hypothesis, carefully constructed through deductive reasoning and incremental instrumentation, is confirmed by hard data. The assistant did not guess at the cause of the regression; it designed an experiment, added the necessary instrumentation, and let the numbers speak.

This approach — instrument first, hypothesize second, optimize third — is the hallmark of serious systems performance work. The pre_destructor_ms marker was the key insight: by measuring time up to the exact point before destructors fire, the assistant isolated the deallocation cost from all other potential causes. The 10-millisecond epilogue time proved that proof assembly was not the culprit. The 26.1-second pre-destructor time proved that the C++ function itself was performing correctly. The only remaining explanation was the silent, invisible work of freeing memory — work that the CPU must do, but that no timer was capturing.

In the end, the ten-second silence was not a mystery at all. It was simply the sound of 37 gigabytes of heap memory being returned to the operating system, one page at a time, while the Rust caller waited patiently for a function that had already finished its real work.