The Ten-Second Shadow: Instrumenting a GPU Timing Gap in the Supraseal-C2 Prover

Introduction

In the high-stakes world of Filecoin proof generation, every second counts. When the assistant benchmarked a promising new optimization—replacing temporary Vec allocations with in-place Boolean::add_to_lc and sub_from_lc methods—the synthesis phase dropped from 55.4s to 50.9s, an 8.3% improvement confirmed by perf stat showing 91 billion fewer instructions and 18.6 billion fewer branches. But the full end-to-end proof told a more troubling story. While synthesis had improved, the GPU phase had mysteriously regressed from 34.0s to 36.0s, despite the CUDA kernel internal timing remaining unchanged at ~25.7s. A phantom 10.2 seconds had materialized between bellperson's internal "GPU prove time" log and the pipeline's "GPU prove complete" marker. Message 1236 captures the moment the assistant began reading the C++/CUDA source code to hunt down this invisible overhead.

The Investigation Context

The story begins with the E2E benchmark in message 1222, where the assistant submitted a PoRep C2 proof through the cuzk daemon. The results were encouraging on the surface: total time 87.5s, synthesis 51.2s, GPU 36.0s. But comparing against the Phase 2/3 baseline of 88.9s total (54.7s synthesis, 34.0s GPU), a worrying pattern emerged. The synthesis improvement was real—3.5s faster—but the GPU phase had gotten slower by 2.0s. The CUDA internal breakdown was identical: prep_msm at 1.8s, ntt_msm_h at 23.0s, batch_add at 1.4s, b_g2_msm at 23.5s, tail_msm at 1.3s, gpu_total_ms at 25.7s. The regression was entirely in the wrapper layer.

In message 1225, the assistant extracted the detailed log timestamps and constructed a precise timeline. The gap was stark: bellperson's "GPU prove time" logged at 01:09:13.35 (25.8s of actual GPU work), but the pipeline's "GPU prove complete" didn't fire until 01:09:23.53—a full 10.2 seconds later. The assistant traced through the code in messages 1227-1232, examining pipeline.rs, engine.rs, and supraseal.rs, ruling out proof serialization and SRS lookup as the cause. The only remaining explanation was heap deallocation.

By message 1235, the assistant had formed a clear hypothesis: "The 10.2s gap is almost certainly heap deallocation of ~37 GB in implicit destructors at function exit." The generate_groth16_proofs_c function in the C++/CUDA layer allocates massive vectors—split_vectors, tail_msm bases—that collectively hold tens of gigabytes of field element data. When the function returns, C++ destructors fire synchronously, freeing this memory. The Rust side then drops its own ProvingAssignment vectors (a, b, c, aux_assignment), adding another ~130 GB of deallocation. All of this happens after the CUDA kernels finish but before the pipeline's outer timer stops.

The Message: Reading the CUDA Source

Message 1236 is a single read tool call:

[assistant] [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>100: #if defined(_MSC_VER) && !defined(__clang__) && !defined(__builtin_popcountll)
101: #define __builtin_popcountll(x) __popcnt64(x)
102: #endif
103: 
104: extern "C"
105: RustError::by_value generate_groth16_proofs_c(const Assignment<fr_t> provers[],
106:                                               size_t num_circuits,
107:                                               const fr_t r_s[], cons...

On its surface, this is unremarkable: the assistant is reading a file. But the which file and the where in the file reveal the investigative strategy. The assistant is not reading the file from the beginning; the content starts at line 100, right at the extern &#34;C&#34; function declaration of generate_groth16_proofs_c. This is the FFI boundary—the exact point where Rust calls into C++/CUDA code. The assistant had already read the end of this function (lines 680-692 in message 1235) showing the proof assembly and thread joining. Now they are reading the beginning to see the function signature and understand the full data flow: what parameters come in, what data structures are created, and where the large allocations happen.

Why This Read Matters

This read is a critical step in a hypothesis-driven debugging process. The assistant has already:

  1. Measured the gap (10.2s)
  2. Located where the gap occurs (between bellperson's internal timer and the pipeline's outer timer)
  3. Ruled out obvious causes (proof serialization, SRS lookup)
  4. Formed a hypothesis (destructor overhead) Now they need to verify the hypothesis by understanding the C++ function's structure well enough to add precise timing instrumentation. Reading the function signature reveals the FFI interface: the function takes arrays of Assignment&lt;fr_t&gt; provers (the synthesized witness data), fr_t scalar values for r and s, and SRS parameters. These are the inputs that must be converted from Rust's representation to C++'s representation. The function returns a RustError::by_value and presumably fills in proof output buffers. The assistant needs to know exactly where to place fprintf(stderr, &#34;CUZK_TIMING: ...&#34;) markers to isolate the overhead. The strategy will be to add markers: - Just before the CUDA kernel launch (to measure pre-kernel setup) - Just after the last CUDA kernel finishes (to measure kernel-only time) - Just before the function returns (to measure post-kernel work including destructors) By comparing these markers with the Rust-side Instant::now() timers, the assistant can pinpoint exactly which phase contributes the 10.2s.

The Thinking Process

What makes this message interesting is what it reveals about the assistant's debugging methodology. The assistant is not randomly reading files or guessing at causes. Each read is targeted and purposeful. The investigation follows a clear chain:

  1. Observe the anomaly: GPU time increased despite identical CUDA internals.
  2. Narrow the window: Use log timestamps to isolate the gap to a specific 10.2s interval.
  3. Trace the code: Read the Rust pipeline code to understand what happens in that interval.
  4. Form a hypothesis: Based on the data volumes (~37 GB C++ vectors, ~130 GB Rust Vecs), infer that destructor overhead is the cause.
  5. Plan verification: Read the C++ source to understand where to add instrumentation. This is textbook performance debugging: measure, narrow, hypothesize, instrument, verify. The assistant resists the temptation to guess or apply fixes prematurely. Instead, they invest the time to instrument the code and collect definitive evidence. The read in message 1236 is particularly strategic because it targets the FFI boundary. In a mixed-language system (Rust calling into C++/CUDA), the boundary between languages is where unexpected overheads often hide. Memory ownership semantics differ between Rust and C++, and the cost of converting between representations or cleaning up after a foreign function call can be substantial. By reading the C function signature, the assistant is preparing to instrument exactly this boundary.

Resolution and Broader Lessons

The investigation succeeded. After adding timing instrumentation (message 1237 onward), the assistant confirmed that the 10.2s was indeed synchronous destructor overhead. The fix was elegant: move the large vector deallocations 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 to 26.2s—matching the CUDA internal time exactly—and the total E2E time improved to 77.2s, a 13.2% reduction from the 88.9s baseline.

The broader lesson is the importance of instrumenting timing at every layer of a complex pipeline. Without the CUZK_TIMING markers inside the CUDA code, the assistant would have seen only "GPU: 36.0s" and might have incorrectly attributed the regression to the GPU kernels themselves. The multi-layer instrumentation—CUDA internal, bellperson wrapper, pipeline wrapper, engine—allowed the assistant to see that the kernels were fine and the overhead was in the glue code. This layered approach to measurement is essential in any system where work crosses language boundaries and abstraction layers.

Message 1236, a simple file read, captures the moment between hypothesis and verification—the point where the assistant moves from "what might be happening" to "let me confirm exactly what is happening." It is a small but essential step in a debugging process that ultimately recovered 11.7 seconds of wasted wall time.