The 132.5-Gigabyte Blind Spot: How a Memory Accounting Calculation Uncovered a Hidden Performance Sink

Introduction

In the high-stakes world of Filecoin proof generation, every second counts. The SUPRASEAL_C2 pipeline, responsible for producing Groth16 zero-knowledge proofs for Filecoin's Proof-of-Replication (PoRep), operates at an enormous scale: synthesizing circuits with over 130 million constraints, running multi-scalar multiplications (MSMs) on the GPU, and managing peak memory footprints approaching 200 GiB. When the assistant in this coding session achieved an 8.3% synthesis speedup through the Boolean::add_to_lc optimization, the subsequent end-to-end (E2E) benchmark revealed a puzzling regression: the GPU wrapper time had increased from 34.0 seconds to 36.0 seconds, despite the CUDA internal timing remaining stable at ~26 seconds. This 10-second gap between what the GPU reported and what the Rust pipeline measured became the focus of a deep investigation—and it is in message [msg 1265] that the assistant performs the crucial calculation that identifies the true culprit.

The Scene: A Regression That Wasn't

The assistant had just completed instrumenting the C++ CUDA code with fine-grained timing markers (gettimeofday calls at function entry, after split-vector allocation, before the epilogue, and just before return). The timing breakdown from message [msg 1249] told a clear story:

• CUDA internal GPU time: 26.1 seconds (stable across runs)
• Bellperson "GPU prove time": 26.1 seconds (matching CUDA)
• Pipeline gpu_ms: 36.2 seconds The 10-second discrepancy was not in GPU computation, not in proof assembly (epilogue took only 10ms), and not in the C++ pre-destructor phase. It was entirely in the time between the C++ function returning and the Rust caller regaining control—the destructor chain. Message [msg 1250] confirmed this explicitly: "10.0 seconds is in destructors — the time between return ret and when the Rust caller gets control back." But why would destructors take 10 seconds? The answer required understanding exactly how much memory was being freed, and that is the subject of message [msg 1265].

The Message: A Back-of-the-Envelope Calculation That Changed Everything

In message [msg 1265], the assistant performs a detailed memory accounting exercise. The message reads:

OK so each ProvingAssignment has: - a: Vec<Scalar> — 130M × 32 bytes = 4.17 GB - b: Vec<Scalar> — 130M × 32 bytes = 4.17 GB - c: Vec<Scalar> — 130M × 32 bytes = 4.17 GB - input_assignment: Vec<Scalar> — small - aux_assignment: Vec<Scalar> — 23M × 32 bytes = 0.74 GB

>

For 10 circuits: 10 × (4.17 + 4.17 + 4.17 + 0.74) = 132.5 GB of Rust Vec allocations that get dropped.

>

Plus input_assignments and aux_assignments are Arc<Vec<Fr>> — but input_assignment and aux_assignment are IN the ProvingAssignment, and the separate input_assignments/aux_assignments Arcs may share the same data. Let me check...

>

Actually looking at the function signature: input_assignments: Vec<Arc<Vec<E::Fr>>> and aux_assignments: Vec<Arc<Vec<E::Fr>>> are separate from provers[i].input_assignment and provers[i].aux_assignment. The provers own their own copies while the Arc versions are separate. But prove_from_assignments in pipeline.rs creates these from the SynthesizedProof struct.

>

Let me check how they're created: [read] /home/theuser/curio/extern/cuzk/cuzk-core/src/pipeline.rs

This is a deceptively simple calculation that represents a critical turning point. Let us examine what is happening here.

The Reasoning Process: From Timing Gap to Root Cause

The assistant's thinking proceeds through several layers of analysis:

Layer 1: Quantifying the problem. The assistant knows from the timing instrumentation that ~10 seconds are spent in destructors. But what destructors? The C++ side has split_vectors (bit vectors and tail MSM scalars) and tail_msm_bases—these account for roughly 37 GB. The Rust side has the ProvingAssignment structures passed into prove_from_assignments. By calculating the size of each field (a, b, c, aux_assignment) and multiplying by 10 circuits, the assistant arrives at 132.5 GB. This is the first time the total memory being freed is quantified.

Layer 2: Distinguishing ownership. The assistant then examines whether the input_assignments and aux_assignments passed as Arc<Vec<Fr>> are the same allocations as those inside ProvingAssignment. This matters because if they share data via Arc, the destructor cost is amortized (only one deallocation per allocation). The assistant correctly identifies that they are separate—the provers own their own copies while the Arc versions are separate references. This means the total deallocation burden is even larger than the 132.5 GB estimate.

Layer 3: Tracing the data flow. The assistant reads the pipeline.rs source to understand how SynthesizedProof is constructed and how its fields flow into prove_from_assignments. This is not idle curiosity—it is essential for designing the fix. The fix must intercept the destruction of both the C++ vectors (already handled in message [msg 1256] with a detached cleanup thread) and the Rust ProvingAssignment vectors.

Assumptions and Potential Pitfalls

The assistant makes several assumptions in this calculation:

1. The size of Scalar is 32 bytes. This is correct for BLS12-381 field elements (Fr), which are 32 bytes (256 bits). The assistant has been working with BLS12-381 throughout this session, so this is a safe assumption.
2. The count of 130 million elements per vector. This comes from the circuit structure for 32 GiB sectors in Filecoin PoRep. The assistant has previously established this constraint count through earlier analysis. It is a correct assumption for the specific benchmark being used.
3. All 10 circuits have the same structure. The benchmark uses 10 partitions of the same PoRep circuit, so each ProvingAssignment has identical dimensions. This is accurate for the test scenario.
4. The Arc references are the last reference. The assistant assumes that when prove_from_assignments returns, the Arc references in input_assignments and aux_assignments are the last remaining references, triggering actual deallocation. This is a reasonable assumption given the ownership flow, but the assistant hedges by saying "they may be the last reference." The one subtle mistake is that the assistant initially wonders if the Arc versions share data with the ProvingAssignment fields. Upon closer inspection of the function signature and the pipeline code, the assistant correctly realizes they are separate. This self-correction is a good example of the assistant's thoroughness.

Input Knowledge Required

To fully understand this message, one needs:

• Understanding of Rust's ownership model: Specifically, how Vec deallocation works (each Vec's destructor frees its heap allocation), how Arc affects deallocation (only the last Arc drops the data), and how function arguments are dropped when a function returns.
• Knowledge of the Filecoin PoRep circuit structure: The 130M constraint count and 23M auxiliary variable count for 32 GiB sectors, which the assistant has established through prior analysis in this session.
• Familiarity with the SUPRASEAL_C2 pipeline architecture: The flow from synthesis (producing ProvingAssignment structures) through GPU proving (C++ CUDA code) back to Rust, and how timing is measured at each layer.
• Understanding of memory sizes: That a 32-byte field element × 130 million = 4.17 GB, and that freeing 132.5 GB of memory through sequential destructors takes measurable time (roughly 10 seconds at ~13 GB/s deallocation throughput, which is realistic for munmap/madvise operations).

Output Knowledge Created

This message creates several critical pieces of knowledge:

1. A quantified memory budget for the Rust-side deallocation: 132.5 GB across 10 circuits, broken down by field (a, b, c, aux). This is the first precise accounting of what must be freed after GPU proving.
2. A confirmation that the C++ async deallocation fix alone is insufficient: The assistant had already implemented async deallocation for the C++ side (message [msg 1256]), but this calculation reveals that the Rust side has more than three times the memory to free (132.5 GB vs ~37 GB). The fix must be applied on both sides.
3. A clear optimization target: The 10-second destructor overhead is not irreducible—it is a consequence of synchronous sequential deallocation of enormous buffers. Moving this to background threads can recover the full 10 seconds.
4. A diagnostic methodology: The message demonstrates how to trace a timing discrepancy back to its root cause through layered instrumentation and quantitative reasoning. The approach—instrument at every layer, compare timestamps, calculate expected deallocation time from memory sizes—is a template for similar investigations.

The Broader Significance

Message [msg 1265] is significant because it represents the moment the assistant fully understands the problem. The earlier messages established that there was a 10-second gap. This message establishes why: 132.5 GB of Rust Vec allocations and ~37 GB of C++ vectors are being freed synchronously, blocking the calling thread for 10 seconds.

The insight is almost absurdly simple once stated: freeing 170 GB of memory takes time. But the path to this insight required:

1. Instrumenting the C++ code with timing markers (messages [msg 1237]–[msg 1240])
2. Rebuilding and running the benchmark (messages [msg 1247]–[msg 1248])
3. Reading the timing output to identify the gap (message [msg 1249])
4. Correlating the gap with destructor timing (message [msg 1250])
5. Performing the memory accounting (message [msg 1265]) Each step eliminated a hypothesis. The GPU wasn't slower. The epilogue wasn't slow. The C++ computation wasn't slow. The destructors were slow—and the memory accounting proved it.

What Happened Next

The assistant immediately acted on this insight. In message [msg 1267], it applied the same async deallocation pattern to the Rust side, spawning a detached thread to take ownership of provers, input_assignments, and aux_assignments and free them in the background. The result, shown in message [msg 1270], was dramatic: 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 from 87.5 seconds to 77.2 seconds—a 13.2% improvement over the baseline.

The final Phase 4 commit (2da2a901) included the Boolean::add_to_lc synthesis optimization, the async deallocation on both C++ and Rust sides, the Vec recycling pool, software prefetch intrinsics, and the A4/D4 CUDA changes. The 132.5 GB blind spot had been found and eliminated.

Conclusion

Message [msg 1265] is a masterclass in performance debugging through quantitative reasoning. It demonstrates that sometimes the most valuable tool is not a profiler or a debugger, but a calculator and a clear understanding of memory ownership. The assistant's back-of-the-envelope calculation—4.17 GB per vector, three vectors per circuit, ten circuits—transformed a mysterious 10-second timing gap into a concrete, actionable optimization target. In doing so, it recovered 10 seconds of end-to-end proof generation time and completed Phase 4 of the optimization roadmap with a 13.2% total improvement.