Instrumenting the Invisible: How Buffer Tracing Uncovered a Memory Bottleneck in GPU Proof Generation

Introduction

In the high-stakes world of Filecoin proof generation, every gigabyte of memory and every millisecond of GPU time matters. The SUPRASEAL_C2 pipeline—a Groth16 proving engine designed to handle 32 GiB sectors—operates at the edge of a 755 GiB system's capacity, synthesizing circuits and dispatching GPU work in a carefully orchestrated dance. When a seemingly modest configuration change from pw=10 to pw=12 (increasing partition workers from 10 to 12) caused the system to OOM with a peak RSS of 668 GiB, the root cause was anything but obvious.

This is the story of a single message in an opencode coding session—message index 3080—where the assistant designed a buffer instrumentation strategy that would ultimately reveal the true bottleneck. It is a masterclass in diagnostic thinking: when your hypothesis fails, instrument reality rather than guess again.

The Message: A Plan for Visibility

The subject message reads in full:

[assistant] Now I need to call these at the right places. Let me add the hooks:

>

1. buf_synth_start() — when synthesis begins (in the synthesis dispatcher) 2. buf_synth_done() — when synthesis completes (in the synthesis function) 3. buf_abc_freed() — after prove_start returns (in gpu_prove_start) 4. buf_finalize_done() — after finish_pending_proof returns (in gpu_prove_finish) 5. buf_dealloc_done() — when the Rust dealloc thread completes (in finish_pending_proof in bellperson) 6. log_buffers() — at each of these events

>

Let me start with the pipeline.rs functions: [read] /home/theuser/curio/extern/cuzk/cuzk-core/src/pipeline.rs

At first glance, this looks like a simple to-do list. But beneath the surface, it encodes a sophisticated theory about where memory lives and dies in a distributed proving pipeline. The assistant is not just adding logging—it is building a mental model of the system's memory lifecycle and then designing probes to validate that model.

The Context That Made This Message Necessary

To understand why this message was written, we must trace the chain of events that led to it. The Phase 12 optimization had just been completed: a split GPU proving API that decoupled the b_g2_msm computation from the GPU worker's critical path, running it in a background thread while the GPU picked up new work. The benchmark results showed a modest 2.4% improvement (37.1s/proof vs 38.0s), but the real story was in the failures: pw=12 OOM'd at 668 GiB peak RSS, and pw=15 failed immediately.

The user asked a sharp question at msg 3055: "the pending proof handle has nothing that can be freed early?" This prompted the assistant to trace the C++ prep_msm_thread code path, discovering that the massive a, b, c NTT evaluation vectors (~12 GiB per partition) were only needed by the GPU kernel region, which completed before prove_start returned. The assistant implemented an early deallocation of these vectors, expecting to reclaim significant memory.

But the fix barely moved the needle. With the early free, pw=12 still peaked at ~650 GiB RSS—only ~18 GiB lower than before. This was the puzzle that set the stage for message 3080. The assistant's hypothesis (that the pending proof handle's a/b/c vectors were the dominant memory consumer) had been falsified. Something else was consuming the other ~280 GiB of memory.

Why This Message Was Written: The Reasoning and Motivation

The user's suggestion at msg 3076—"Can we count and report number of each large buffer in flight and maybe the stage?"—was the catalyst. But the assistant's response in msg 3080 goes far beyond simply adding counters. It reflects a fundamental shift in diagnostic strategy.

The assistant had been operating under a mental model where memory pressure came from individual large allocations that could be identified and eliminated one by one. The early a/b/c free was the archetype of this approach: find a big allocation, prove it's unnecessary, delete it. But the failure of that fix revealed that the problem was systemic, not atomic. The assistant needed to understand the dynamics of memory—how buffers accumulated, how long they lived, and at what stages they multiplied.

The six hooks the assistant proposes form a lifecycle tracing system. Each hook corresponds to a transition in a buffer's state:

The Thinking Process Visible in the Message

The message reveals a structured, almost architectural approach to debugging. The assistant does not simply add a single counter at the OOM point. Instead, it designs a complete tracing framework that covers the entire lifecycle of a partition from synthesis through GPU processing to deallocation.

The choice of hook locations is particularly telling. The assistant places hooks at boundaries—points where ownership of memory transitions between stages. This is the hallmark of a systems thinker: instead of measuring raw allocation sizes (which would be noisy and hard to interpret), measure the number of partitions in each state. The product of count × per-partition size then gives the memory footprint for each stage.

The assistant also demonstrates a clear understanding of the pipeline architecture. It knows that synthesis runs in parallel (controlled by partition_workers), that GPU dispatch is serialized through a channel (capacity 1 at this point), and that finalization happens in a background thread. The hook placement reflects this mental model: buf_synth_start/buf_synth_done bracket the parallel synthesis phase, while buf_finalize_done/buf_dealloc_done bracket the serial finalization phase.

Assumptions and Potential Blind Spots

The assistant makes several assumptions in this message. First, it assumes that counting partitions in each stage will be sufficient to identify the bottleneck. This is reasonable—if the synthesis stage has 12 partitions in flight while the GPU channel can only accept 1, the math is straightforward. But it assumes that per-partition memory is roughly uniform, which may not hold if some partitions have larger circuits.

Second, the assistant assumes that the Rust-side buffers are the dominant memory consumers. The counters track Rust allocations (ProvingAssignment, aux_assignments, etc.) but not C++ allocations (the SRS, PCE tables, CUDA device memory). If the C++ side were the problem, these counters would miss it.

Third, the assistant assumes that the bottleneck is in the number of buffers rather than their size. The counters track counts, not sizes. If a single partition somehow grew to 100 GiB, the counters would show "1" and miss the problem.

These assumptions are reasonable given what the assistant knew at the time. The early a/b/c free had already established that per-partition memory was ~16 GiB (12 GiB a/b/c + 4 GiB aux_assignments). With 12 partition workers, the expected peak from synthesis alone was ~192 GiB. The actual peak of 668 GiB suggested something was multiplying the buffers, not enlarging them. So counting was the right approach.

Input Knowledge Required to Understand This Message

To fully grasp this message, one needs significant domain knowledge:

  1. The SUPRASEAL_C2 pipeline architecture: Understanding that synthesis is CPU-bound and runs in parallel, while GPU dispatch is serialized through a channel. The partition_workers (pw) parameter controls how many syntheses run concurrently.
  2. The Groth16 proof structure: Knowing that each partition produces a ProvingAssignment containing a, b, c evaluation vectors (~12 GiB total), aux_assignments (~4 GiB), and density trackers (~48 MB). Understanding that prove_start uploads these to the GPU, runs kernels, and then the GPU is done with a/b/c.
  3. The Phase 12 split API: The refactoring that split generate_groth16_proofs into start and finalize phases, with b_g2_msm running in a background thread. This created the PendingProofHandle that holds data between start and finalize.
  4. The Rust/C++ FFI boundary: Understanding that Rust Vecs are passed to C++ as raw pointers, and that the C++ prep_msm_thread reads from these pointers asynchronously. This is why the assistant had to be careful about when memory can be freed.
  5. The memory pressure context: Knowing that the system has 755 GiB of RAM, that pw=10 peaks at ~367 GiB, and that pw=12 OOMs at ~668 GiB. This establishes the constraint space.

Output Knowledge Created by This Message

This message creates several forms of knowledge:

  1. A tracing architecture: The six-hook design becomes the foundation for understanding memory dynamics. It is implemented in subsequent messages and ultimately reveals that the partition semaphore releases too early, allowing synthesized partitions to pile up while blocking on the single-slot GPU channel.
  2. A diagnostic methodology: The message establishes a pattern of instrumenting lifecycle boundaries rather than raw allocation sizes. This methodology is reusable for any future memory debugging.
  3. A falsification of the "big allocation" hypothesis: By designing counters that would show whether a/b/c accumulation was the problem, the assistant implicitly acknowledges that the early free fix was insufficient. The counters would later show that provers peaked at 28—meaning 28 synthesized partitions were queued holding their full datasets.
  4. A bridge to the next fix: The tracing design directly enables the discovery that the semaphore should hold its permit until the job is delivered to the GPU channel. This fix reduces peak RSS from 668 GiB to 294.7 GiB, enabling pw=12 to run without OOM for the first time.

Mistakes and Incorrect Assumptions

The most significant incorrect assumption embedded in this message is that the hooks would be sufficient to diagnose the problem without also measuring timing. The counters show how many buffers are in each state, but they don't show how long they stay there. In the subsequent analysis, the assistant would discover that the real issue was not just the count of buffers but the fact that they were held for extended periods while waiting for the GPU channel.

However, this is not really a mistake—it's a deliberate scoping decision. The assistant could have added timing instrumentation, but the immediate question was "where is the memory?" not "why is it held so long?" The count-based approach was the right first step, and it successfully identified the bottleneck (28 queued partitions). The timing analysis came later as a natural follow-up.

Another potential blind spot is the assumption that the C++ side is not the dominant memory consumer. The counters focus on Rust-side buffers. If the C++ SRS or PCE tables were bloated, these counters would miss them. But the assistant had already accounted for SRS/PCE memory in earlier analysis (~70 GiB), and the OOM behavior was correlated with partition count, not SRS size. So this assumption was sound.

Conclusion

Message 3080 is a turning point in the debugging of the SUPRASEAL_C2 memory bottleneck. It represents a shift from hunting individual allocations to understanding system dynamics. The assistant designs a tracing framework that treats memory as a flow through lifecycle stages, with counters at each transition point. This approach is elegant because it maps directly onto the pipeline architecture: the hooks correspond to real ownership transitions in the code, making the instrumentation both easy to implement and easy to interpret.

The message also reveals the assistant's thinking process: structured, architectural, and hypothesis-driven. When the early a/b/c free failed to solve the OOM, the assistant did not try another random fix. Instead, it designed an observability layer that would make the invisible visible. The buffer counters would ultimately reveal that 28 synthesized partitions were queued waiting for the GPU channel, leading to the semaphore fix that reduced peak RSS by more than half.

In the broader context of the optimization project, this message is where debugging becomes science. The assistant stops guessing and starts measuring. The result is not just a fix for pw=12 but a reusable diagnostic framework for any future memory issues in the pipeline.