Instrumentation-Driven Debugging: Isolating a 10-Second Performance Gap in GPU Proving

Introduction

In the high-stakes world of Filecoin proof generation, every second counts. The SUPRASEAL_C2 Groth16 proving pipeline, responsible for generating proofs for Filecoin's Proof-of-Replication (PoRep), had already been optimized through multiple phases of work — from batch-mode pipeline rewrites to cross-sector batching and compute-level optimizations. But in message [msg 1239], we witness a critical moment in the debugging of a stubborn performance regression: a 10.2-second gap between what the Rust-side timer reports and what the CUDA internal instrumentation measures. This message, seemingly small — just a file read and a stated intent — captures the essence of instrumentation-driven performance debugging at its most methodical.

The subject message reads:

Now let me also add a marker right at the start of the prep_msm_thread to see how much time is spent between function entry and prep start.

>

[read] /home/theuser/curio/extern/supraseal-c2/cuda/groth16_cuda.cu

>

<path>/home/theuser/curio/extern/supraseal-c2/cuda/groth16_cuda.cu</path> <type>file</type> <content>158: std::atomic<bool> caught_exception{false}; 159: size_t n_gpus = std::min(ngpus(), num_circuits); 160: 161: std::thread prep_msm_thread([&, num_circuits] 162: { 163: auto t_prep_start = std::chrono::steady_clock::now(); 164: // pre-processing step 165: // mark inp and significant scalars in aux assignments 166: groth16_pool.par_map(num_circuits...

At first glance, this appears to be a routine code-reading operation. But to understand its significance, we must dive into the performance mystery that prompted it.

The Performance Mystery: A 10-Second Ghost

The story begins in the preceding messages. The assistant had just completed benchmarking the Boolean::add_to_lc synthesis optimization, achieving a respectable 13.2% end-to-end improvement — synthesis time dropped from ~55.4s to ~50.9s. However, a troubling regression appeared in the GPU wrapper: the Rust-side timer around prove_from_assignments() reported 36.0 seconds, while the CUDA internal instrumentation (the CUZK_TIMING markers embedded in the C++/CUDA kernel) consistently measured only ~25.7 seconds of actual GPU compute time. This left a 10.2-second gap unaccounted for.

The assistant's investigation in [msg 1234] had already ruled out several hypotheses. The gap wasn't in SRS loading (that was preloaded and took 0ms). It wasn't in data transfer to the GPU (the Rust wrapper's internal "GPU prove time" of 25.8s closely matched the CUDA internal timing). The gap had to be in overhead that occurred outside the CUDA timing markers but inside the Rust Instant::now() wrapper.

The leading hypothesis, developed through careful analysis of the code paths in supraseal.rs and pipeline.rs, was that the 10.2 seconds were consumed by destructor overhead — the synchronous deallocation of massive heap-allocated vectors when they went out of scope. The ProvingAssignment structures for 10 parallel circuits each held ~4.17 GB of field element data, totaling ~41.7 GB. The split_vectors and tail_msm bases in the C++ code added another ~37 GB. When these structures were destroyed at function exit, the C++ runtime and Rust's allocator had to munmap and free hundreds of gigabytes of memory — and if this happened synchronously on the calling thread, it would manifest as a multi-second pause.

The Message: Adding a Timing Marker

Message [msg 1239] represents the next logical step in this investigation. The assistant had already added three timing markers in [msg 1237]: one for pre-prep overhead (before the CUDA work begins), one for the epilogue (proof assembly after CUDA work), and one just before the function return (to measure destructor time by comparing with the Rust-side timer). Now, the assistant realizes that an additional marker is needed — one placed at the very beginning of the prep_msm_thread lambda.

The reasoning is subtle but important. The existing "pre-prep overhead" marker, added in the previous round, was likely placed at function entry, before the thread was spawned. But the assistant needs to distinguish between two different sources of pre-CUDA overhead:

1. Thread creation and scheduling overhead: The time between function entry and when the prep_msm_thread lambda actually begins executing. This includes thread creation, GPU selection logic (std::min(ngpus(), num_circuits) at line 159), and the scheduler's decision to run the thread.
2. Prep work itself: The time spent inside the thread doing preprocessing (marking inputs, significant scalars in aux assignments) before the main GPU compute phases begin. By adding a marker at line 163 (which, notably, already exists from the previous instrumentation round — auto t_prep_start = std::chrono::steady_clock::now();), the assistant can measure the second quantity. Combined with a marker at function entry (from msg 1237), the difference between function-entry time and t_prep_start gives the first quantity — the thread creation and scheduling overhead.

The Debugging Methodology: Instrumentation-Driven Divide and Conquer

This message exemplifies a debugging methodology that is both systematic and powerful. The assistant is applying a classic "divide and conquer" strategy to a performance problem:

1. Measure the whole: The Rust-side timer gives the total time (36.0s).
2. Measure the known parts: CUDA internal timers give the GPU compute time (25.7s).
3. Identify the gap: 36.0s - 25.7s = 10.3s unaccounted.
4. Hypothesize the source: Destructor overhead at function exit.
5. Add instrumentation to test the hypothesis: Markers at function entry, at prep start, at epilogue start, and just before return.
6. Refine instrumentation: Add more markers to further narrow down within identified phases. This is not a blind guess — it's a methodical narrowing of the problem space. Each round of instrumentation eliminates some possible explanations and highlights others. The assistant is building a timing map of the entire function, layer by layer, until the exact location of the 10-second gap is pinpointed. The choice to read the file rather than immediately edit it is also significant. The assistant needs to see the current state of the code — including the markers added in [msg 1237] — before deciding exactly where to place the new marker. This prevents duplicate instrumentation and ensures the new marker complements, rather than overlaps with, existing measurements.

Input Knowledge Required

To understand this message, one needs knowledge of several layers of the system:

• The CUDA kernel structure: Understanding that generate_groth16_proofs_c is the C entry point called from Rust, and that it spawns threads for prep work and GPU compute.
• The timing architecture: The CUZK_TIMING fprintf markers embedded in the CUDA code, which measure individual phases like prep_msm, ntt_msm_h, batch_add, b_g2_msm, and tail_msm.
• The Rust-C++ boundary: How prove_from_assignments() in bellperson calls into the C++ generate_groth16_proofs_c via FFI, and how the Rust Instant::now() timer wraps this call.
• The memory profile: The ~200 GiB peak memory footprint of the proving pipeline, with ~41.7 GB in Rust-side ProvingAssignment vectors and ~37 GB in C++ split_vectors and tail_msm bases.
• The previous instrumentation: The markers added in [msg 1237] for pre-prep overhead, epilogue, and destructor timing. Without this context, the message appears trivial — just reading a file. With it, the message becomes a critical step in a sophisticated debugging session.

Output Knowledge Created

The immediate output of this message is minimal — the assistant reads the file and sees the current code. However, the intended output — which will be realized in the next round — is a new timing marker that will help quantify thread creation and scheduling overhead. This marker, combined with the others, will allow the assistant to construct a complete timing breakdown of the function:

function_entry → t_prep_start : thread creation + scheduling overhead
t_prep_start → t_epilogue_start : prep work + GPU compute
t_epilogue_start → t_return_marker : proof assembly
t_return_marker → Rust timer end : destructor overhead

With this breakdown, the assistant can definitively identify which phase contains the 10-second gap. If the gap is in the destructor phase, the hypothesis is confirmed and the fix is clear: move deallocation into detached threads. If the gap is elsewhere, the hypothesis must be revised.

Assumptions and Thinking Process

The assistant's thinking, visible in the reasoning behind this message, reveals several assumptions:

1. The gap is in overhead, not in compute: The assistant assumes that the CUDA internal timing of ~25.7s is accurate and that the Rust timer's 36.0s includes overhead outside the actual GPU compute. This is a reasonable assumption given that the CUDA timing markers are placed at the beginning and end of the actual GPU work.
2. The overhead is in destructor/deallocation: The leading hypothesis is that synchronous destruction of large vectors at function exit causes the gap. This is informed by the memory profile — hundreds of gigabytes of data being freed.
3. Thread creation overhead is measurable: By adding a marker at the start of the prep_msm_thread, the assistant assumes that thread creation and scheduling overhead is significant enough to measure (microseconds to milliseconds) and that it contributes to the gap.
4. The instrumentation itself doesn't affect timing: The assistant assumes that adding std::chrono::steady_clock::now() calls doesn't significantly alter the performance characteristics being measured. These assumptions are reasonable, but they could be wrong. For instance, if the CUDA runtime performs lazy initialization that isn't captured by the internal timers, the gap could be in CUDA context setup rather than in destructor overhead. The instrumentation will reveal the truth.

Connection to the Broader Optimization Effort

This message is part of Phase 4 of a multi-phase optimization effort for the cuzk proving engine. The earlier phases had already achieved significant throughput improvements: Phase 2's async overlap between synthesis and GPU proving achieved 1.27x throughput improvement, and Phase 3's cross-sector batching achieved 1.46x. Phase 4 targeted compute-level optimizations, including SmallVec optimizations, pre-sizing for ProvingAssignment, parallelizing B_G2 CPU MSMs, pinning vectors with cudaHostRegister, and per-MSM window tuning.

However, several of these optimizations had been reverted due to regressions. The cudaHostRegister optimization (B1) was reverted because it caused a slowdown. The SmallVec optimization (A1) was found to cause a 5-6 second synthesis slowdown. The debugging of the GPU wrapper regression — which is what this message addresses — was the final piece of the Phase 4 puzzle.

The resolution of this debugging effort, which will come in subsequent messages, is the implementation of async deallocation: moving the destruction of large vectors into detached threads on both the C++ and Rust sides. This fix drops the GPU wrapper time from 36.0s to 26.2s — matching the CUDA internal time exactly — and brings the total E2E time to 77.2s, a 13.2% improvement over the 88.9s baseline.

Conclusion

Message [msg 1239] may appear unremarkable at first glance — a simple file read with a stated intent to add a timing marker. But in the context of the broader debugging effort, it represents a critical step in a methodical, instrumentation-driven investigation of a 10-second performance mystery. The assistant's approach — measure, hypothesize, instrument, refine — is a masterclass in performance debugging. Each marker added peels away another layer of uncertainty, bringing the team closer to the root cause.

The lesson is clear: when faced with a performance regression that defies easy explanation, the answer is not to guess — it's to measure. And when the first round of measurements doesn't tell the whole story, the answer is to measure more finely. This message captures that philosophy in action.