The 10-Second Ghost: Diagnosing Destructor Overhead in a GPU Proving Pipeline

Introduction

In the high-stakes world of Filecoin proof generation, every second counts. A single PoRep (Proof of Replication) C2 proof consumes approximately 200 GiB of memory and ties up a GPU for nearly 90 seconds. When an optimization effort that shaves 3.5 seconds off synthesis time is partially nullified by a mysterious 2-second GPU regression, the hunt begins. But when that regression balloons into a 10.2-second gap between what the GPU reports and what the Rust runtime measures, the hunt becomes a forensic investigation.

This article examines a single message from an opencode coding session — message index 1247 — in which the assistant, having just completed a build of instrumented CUDA code, launches an end-to-end (E2E) proof generation test to collect timing data. The message appears deceptively simple: a bash command that starts a daemon, waits 18 seconds, and checks the log. But behind this routine action lies the culmination of a deep diagnostic chain that traces a 10-second performance regression to its root cause: the synchronous deallocation of 37 GB of C++ vectors.

The Message

The subject message reads:

[assistant] Good, built successfully. Now run the E2E test to see the timing breakdown.
[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-timing-debug.log 2>&1 &
echo "Daemon PID: $!"
sleep 18
tail -3 /tmp/cuzk-e2e-timing-debug.log
Daemon PID: 2597404
[2026-02-18T01:19:36.424042Z] INFO cuzk_daemon: cuzk-daemon ready, serving on 0.0.0.0:9821
[2026-02-18T01:19:36.424126Z] INFO cuzk_core::engine: pipeline GPU worker started worker_id=0 gpu=0
[2026-02-18T01:19:36.424123Z] INFO cuzk_core::engine: synthesis task started (Phase 3 batch-aware) max_batch_size=1 max_batch_wait_ms=...

At first glance, this is a routine operational command: start the daemon, verify it's running, prepare to submit a proof. But the context transforms this into a pivotal moment in a performance debugging saga. The assistant isn't just running a test — it is testing a specific hypothesis about where 10 seconds of hidden overhead are hiding.

Context: The Performance Optimization Campaign

To understand why this message matters, we must step back to see the broader optimization campaign. The assistant and user were deep into "Phase 4" of a multi-phase effort to optimize the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin's Curio software. The pipeline, which converts circuit witnesses into zero-knowledge proofs, was a known bottleneck: each proof consumed ~200 GiB of peak memory and took ~89 seconds end-to-end.

Phase 4 targeted compute-level optimizations. The team had already implemented several promising changes: a Boolean::add_to_lc method that eliminated temporary LinearCombination allocations during synthesis, a Vec recycling pool, software prefetch intrinsics, and CUDA-level tuning for MSM (Multi-Scalar Multiplication) windows. Microbenchmarks showed synthesis time dropping from ~55.4 seconds to ~50.9 seconds — an 8.3% improvement confirmed by perf stat showing 91 billion fewer instructions executed.

But when the full end-to-end proof was run, the results were puzzling. The total time dropped only to 87.5 seconds — a modest 1.6% improvement, far less than expected. Worse, the GPU phase, which should have been unaffected by synthesis changes, had regressed from 34.0 seconds to 36.0 seconds. The CUDA internal timing, however, was rock-solid at ~25.7 seconds. The 10-second gap between the CUDA-internal timer and the Rust-side Instant::now() wrapper was the smoking gun.

The Diagnostic Chain

The assistant's investigation before this message ([msg 1225] through [msg 1246]) reads like a detective story. The first clue was a timestamp analysis showing that while bellperson's internal "GPU prove time" logged 25.8 seconds, the pipeline's "GPU prove complete" logged 35,961 milliseconds — 36.0 seconds. The 10.2-second gap occurred after the GPU finished its work but before the pipeline reported completion.

The assistant traced the code path through three layers: the pipeline's gpu_prove() function in Rust, the prove_from_assignments() function in bellperson's supraseal wrapper, and finally the C++/CUDA generate_groth16_proofs_c() function in supraseal-c2. Each layer added its own timing instrumentation, and the discrepancy narrowed to a specific region: the epilogue of the C++ function, where large vectors (split_vectors, tail_msm bases, results) holding approximately 37 GB of data were being destructed synchronously.

The hypothesis was elegant: when generate_groth16_proofs_c() returns, the C++ runtime must destruct all local variables. For vectors holding billions of field elements, this means calling destructors for each element and freeing pages. On a system with ~200 GiB of RAM, the munmap syscall for these allocations can block the calling thread for seconds. The Rust side, which called the C function, was measuring this destructor time as part of the "GPU prove" duration because the timer stopped only after the C function returned.

To test this hypothesis, the assistant added gettimeofday() instrumentation at strategic points in the CUDA code: at function entry (before the mutex lock), at the start of split_vectors allocation, at the epilogue start (after GPU work completes), and at the epilogue end (just before the function returns). The build succeeded (as confirmed by the subject message's "Good, built successfully"), and the E2E test was about to collect the evidence.

What This Message Reveals About the Debugging Process

The subject message is a study in disciplined performance engineering. Several aspects are noteworthy:

Hypothesis-driven instrumentation. The assistant didn't blindly add timers everywhere. It had a specific hypothesis — synchronous destructor overhead — and placed gettimeofday() calls at precisely the boundaries needed to confirm or refute it. The instrumentation was minimal and surgical.

The build-verify loop. The message shows a tight iteration: edit the CUDA source, rebuild, run the test. The build had failed twice before (messages 1242–1245) due to missing sys/time.h and shell globbing issues. Each failure was diagnosed and fixed. The subject message marks the successful build, and the assistant immediately proceeds to the test.

Understanding tool behavior. The assistant used nohup and backgrounding to keep the daemon running, sleep 18 to wait for initialization (the daemon takes ~18 seconds to load SRS parameters from disk), and tail -3 to verify readiness without flooding the output. These are practical operational decisions that reflect deep familiarity with the system.

The unspoken assumption. The assistant assumes that the instrumentation will not perturb the timing — that adding gettimeofday() calls does not measurably change the behavior being measured. This is a reasonable assumption for microsecond-precision timers measuring multi-second operations, but it is still an assumption worth noting.

The Knowledge Required

To fully understand this message, one needs knowledge spanning several domains:

• Groth16 proofs and Filecoin PoRep: The proof system being optimized, including the distinction between C1 (synthesis) and C2 (proving) phases.
• The SUPRASEAL_C2 pipeline: The C++/CUDA library that implements NTT (Number Theoretic Transform) and MSM on GPUs, and its Rust bindings through bellperson.
• CUDA programming and GPU timing: The use of cudaEvent for GPU-side timing and gettimeofday for host-side timing.
• Rust memory management: How Rust's ownership model interacts with C++ allocations, and how destructors (both Rust Drop and C++ destructors) can introduce latency.
• Performance analysis methodology: The importance of layered timing, the distinction between wall-clock and CPU time, and the technique of subtracting known sub-measurements to isolate unknown overheads.
• The Curio/cuzk architecture: How the daemon, engine, pipeline, and GPU workers interact, and where timing measurements are taken at each layer.

The Outcome

The E2E test launched in this message would confirm the hypothesis. The new instrumentation would show that the C++ epilogue — the destruction of split_vectors, tail_msm bases, and other large allocations — took approximately 10 seconds. The fix, implemented in subsequent messages, was to move these large allocations into detached threads on both the C++ and Rust sides, allowing the function to return immediately while deallocation happens in the background. After this fix, the GPU wrapper time dropped from 36.0 seconds to 26.2 seconds (matching the CUDA internal time exactly), and the total E2E time improved to 77.2 seconds — a 13.2% reduction from the 88.9-second baseline.

Conclusion

This message captures the moment when a performance investigation transitions from hypothesis to experiment. The assistant had identified a probable cause for a 10-second regression, instrumented the code to collect evidence, and was about to run the definitive test. The bash command is mundane, but the reasoning behind it is sophisticated: a deep understanding of how C++ destructors, Rust ownership, and GPU timing interact to create a hidden bottleneck that cost 10 seconds per proof.

The broader lesson is that in complex systems, performance problems often hide at the boundaries between layers. The GPU was fast; the C++ computation was fast; but the transition from C++ back to Rust — specifically, the synchronous destruction of 37 GB of vectors — was a silent performance killer. Only by instrumenting every layer and comparing timestamps could this ghost be made visible. The subject message is the moment when the ghost was about to be caught.