The Ten-Second Silence: How Asynchronous Deallocation Uncovered a Hidden Bottleneck in Groth16 Proving

Introduction

In the relentless pursuit of performance optimization, the most insidious bottlenecks are often not the ones that announce themselves with fanfare. They are the silent ones—the microseconds that accumulate into seconds, the hidden overheads that live in the shadows between timestamps. Message [msg 1256] captures one such moment of discovery: a single, unadorned sentence that marks the transition from diagnosis to treatment of a ten-second performance gap that had been lurking, invisible, throughout months of optimization work on the SUPRASEAL_C2 Groth16 proof generation pipeline.

The message itself is deceptively simple:

Now I understand the structure. The plan: after the proof assembly loop, move all the large vectors into a detached cleanup thread so the function can return immediately. This avoids blocking on destructors.

But behind this brief statement lies an extraordinary detective story—one that spans C++ and Rust, crosses language boundaries, involves gigabytes of heap memory, and ultimately reveals that the GPU proving pipeline had been silently wasting ten seconds per proof on nothing more than the act of freeing memory.

The Context: A Regression That Wasn't

To understand the significance of this message, we must step back into the investigation that preceded it. The assistant had been working on Phase 4 of the cuzk proving engine optimization, having just completed and benchmarked the Boolean::add_to_lc synthesis optimization. The microbenchmarks were promising: synthesis time dropped 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 assistant ran a full end-to-end proof to validate the gains, a troubling anomaly appeared.

The total time was 87.5 seconds—better than the 88.9-second baseline, but the GPU timing told a confusing story. The CUDA internal timers consistently reported ~26 seconds for the actual GPU computation (NTT, MSM, batch addition). Yet the Rust-side wrapper, which measured the wall-clock time around the prove_from_assignments() function call, reported 36.0 seconds. A ten-second gap had opened up between what the GPU reported and what the CPU measured.

This looked like a regression. Earlier Phase 3 benchmarks had shown GPU wrapper times around 34 seconds, and now it was 36 seconds. The assistant initially suspected that the Boolean::add_to_lc changes had somehow degraded GPU performance. But the CUDA internal timings were rock-solid at ~26 seconds, unchanged from the baseline. The gap was entirely in the wrapper overhead.

The Investigation: Instrumenting the Silence

The assistant embarked on a systematic investigation, deploying an "explore agent" subagent to drill into the timing discrepancy ([msg 1234]). The subagent traced the complete execution flow from the Rust Instant::now() call through the C++ generate_groth16_proofs_c function and back, mapping every segment of execution time. The analysis revealed that the function's internal timer (pre_destructor_ms) measured 26.1 seconds from entry to just before the return ret statement. But the Rust caller measured 36.2 seconds. The ten-second gap was entirely post-return—the time between the C++ return statement executing and the Rust caller regaining control.

This could only be one thing: destructor chains. When generate_groth16_proofs_c returns, all local variables go out of scope, triggering their destructors. The function held several massive data structures: split_vectors instances for L, A, and B-G1 points, each containing std::vector<std::vector<mask_t>> and std::vector<std::vector<fr_t>> members, plus tail_msm_bases vectors. The total heap memory managed by these structures was approximately 37 GB. Freeing 37 GB of heap memory through the C++ allocator's free() and munmap() calls was taking ten seconds.

The assistant confirmed this by checking the Phase 2/3 baseline logs ([msg 1251]), which showed the same pattern: bellperson internal GPU time of ~26 seconds but pipeline-reported gpu_ms of 37.9 seconds. The "regression" was not a regression at all—it was simply run-to-run variance in destructor timing that had always been present but had never been isolated and measured.

The Insight: Understanding the Structure

Message [msg 1256] is the crystallization of this understanding. The assistant had just read the split_vectors class definition in groth16_split_msm.cu ([msg 1255]), confirming its structure:

class split_vectors {
public:
    std::vector<std::vector<mask_t>> bit_vector;
    std::vector<std::vector<fr_t>>   tail_msm_scalars;
    size_t batch_size, bit_vector_size;
    // ...
};

The key realization was that these std::vector members are heap-allocated and move-constructible. C++ vectors transfer ownership of their heap buffers through move semantics without copying data—they simply swap internal pointers. This meant the assistant could move the large vectors into a detached thread's lambda capture, and when that thread's lambda went out of scope (potentially long after the main function had returned), the destructors would run in the background.

The plan was elegant: "after the proof assembly loop, move all the large vectors into a detached cleanup thread so the function can return immediately. This avoids blocking on destructors."

The Implementation and Its Flaws

The initial implementation, applied in the edit that followed message [msg 1256], created a detached thread lambda that captured the large vectors by move. But the assistant immediately recognized a flaw in message [msg 1257]: the gettimeofday timing instrumentation placed around the thread creation wouldn't capture the actual deallocation time, because the destruction happens when the lambda's captures go out of scope (when the lambda returns), not when the thread is spawned. The timing was measuring the thread creation overhead, not the async deallocation benefit.

This self-correction is characteristic of the assistant's methodology throughout the session: each instrumentation reveals new questions, each answer exposes new layers. The assistant fixed the timing to measure the actual deallocation within the lambda, then rebuilt and tested.

The Result: A Ten-Second Victory

The first test with only the C++ async deallocation (<msg id=1260-1261>) showed... no improvement. The total time was still ~87 seconds with gpu_ms=35858. The C++ async dealloc thread reported only 5.5 seconds of background work, but the pipeline wrapper still measured 35.9 seconds. The assistant realized the remaining gap was on the Rust side: prove_from_assignments() takes ownership of provers: Vec&lt;ProvingAssignment&gt;, input_assignments: Vec&lt;Arc&lt;Vec&lt;Fr&gt;&gt;&gt;, and aux_assignments: Vec&lt;Arc&lt;Vec&lt;Fr&gt;&gt;&gt;. Each ProvingAssignment contains a, b, c vectors of 130 million field elements each (4.17 GB per vector), plus an aux_assignment of 23 million elements (0.74 GB). For 10 circuits, that's approximately 132.5 GB of Rust heap allocations that get dropped synchronously when the function returns.

The assistant applied the same async deallocation pattern on the Rust side ([msg 1267]), spawning a thread to drop the massive vectors in the background. The result, shown in message [msg 1270], was dramatic:

The Deeper Lesson: Hidden Costs of Memory Management

This episode illuminates a profound lesson about performance optimization in high-throughput computing systems. The Groth16 proving pipeline processes approximately 200 GiB of peak memory across its lifetime, with massive allocations flowing through both C++ and Rust heaps. The cost of deallocating this memory is not merely the CPU time spent in free()—it includes TLB cache misses, page table walks, and the kernel's munmap() overhead for large allocations that were serviced by mmap().

The asymmetry between allocation and deallocation is critical: allocation via push() in Rust's Vec is amortized O(1) through geometric growth, and it happens in parallel with computation (the synthesis work). Deallocation, by contrast, is synchronous and serialized—the thread that owns the memory must walk every page, coalesce every free chunk, and return every virtual memory region. When 132 GB of Rust Vec data and 37 GB of C++ vectors are freed on the hot path, the thread is blocked for ten seconds doing nothing but bookkeeping.

The async deallocation pattern transforms this cost from synchronous blocking into background work that overlaps with the next computation. The proving pipeline can begin processing the next job while the previous job's memory is still being reclaimed. This is particularly valuable in a persistent proving daemon (the architecture established in earlier phases), where the GPU worker thread should never be blocked on memory management.

Assumptions and Corrections

Several assumptions were tested and corrected during this investigation:

  1. The assumption of a GPU regression: The initial 36-second GPU wrapper time looked like a regression from the 34-second baseline. The assistant correctly suspected this was not real and instrumented the C++ code to verify, discovering that CUDA internal time was stable at ~26 seconds.
  2. The assumption that destructor overhead was purely C++: After fixing the C++ side with no improvement, the assistant correctly identified the Rust-side drop of ProvingAssignment vectors as the dominant cost (~9.7 seconds vs ~5.5 seconds for C++).
  3. The assumption that the fix was complete after one side: The lesson that both language heaps must be addressed independently is a valuable architectural insight for any cross-language FFI boundary.

Knowledge Created

This message and its surrounding investigation produced several forms of knowledge:

Conclusion

Message [msg 1256] is a turning point—the moment when a ten-second mystery became a ten-second opportunity. The assistant's brief statement, "move all the large vectors into a detached cleanup thread so the function can return immediately," encapsulates a fundamental insight about high-performance systems: sometimes the most important optimization is not making computation faster, but getting out of the way when computation is done. The silence of destructors, once measured and understood, could be deferred to where it no longer mattered.