The 28 Provers Problem: How Buffer Instrumentation Revealed the True Memory Bottleneck in GPU-Accelerated Proof Generation

Introduction

In the high-stakes world of Filecoin proof generation, memory is the invisible enemy. A single Groth16 proof for the 32 GiB PoRep (Proof of Replication) circuit consumes tens of gigabytes of RAM during synthesis, and when multiple proofs are generated concurrently, the system's 755 GiB memory ceiling can be breached in seconds, triggering out-of-memory (OOM) failures that abort hours of computation. This article examines a single diagnostic message from an opencode coding session — message index 3106 — in which an AI assistant analyzing the SUPRASEAL_C2 proof generation pipeline makes a critical breakthrough: by instrumenting the system with atomic buffer counters, it identifies the root cause of persistent OOM failures that had resisted earlier optimization attempts.

The message, reproduced below, is a moment of discovery. The assistant has just received the output of its newly-added buffer tracking instrumentation, and the numbers tell a stark story:

There it is! At peak: - provers=28: 28 ProvingAssignment sets alive (a/b/c ~12 GiB each) = 336 GiB - aux=97-99: 97-99 aux buffers alive (~4 GiB each) = ~392 GiB - shells=69-72: density-only shells, tiny

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The aux counter is never being decremented! It reaches 99 — that means buf_dealloc_done() is never being called. The dealloc happens in bellperson's finish_pending_proof which prints BUFFERS[rust_dealloc_finish] but doesn't call buf_dealloc_done() since bellperson doesn't have access to the pipeline module's counter.

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But more importantly, aux=97 means there are 97 aux_assignment buffers being held. At ~4 GiB each that's ~388 GiB just for aux. The buf_dealloc_done() is never called from bellperson, so the counter is accumulating. But the actual memory IS being freed (the dealloc thread runs). The counter just doesn't reflect it.

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Wait, let me look at the provers count too. provers=28 is way more than the 12 partition workers. This means the a/b/c vectors are NOT being freed in some paths. Let me check — the buf_abc_freed() decrements provers and increments shells. provers=28 means 28 unfreed prover sets. The GPU is only processing them slowly.

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Actually, provers tracks the count that have finished synthesis but haven't been through prove_start yet. With j=10 concurrent proofs × 10 partitions each = 100 partitions total, and the GPU can only process them one at a time (~3.5s each for GPU part), there can be a large backlog of synthesized-but-not-yet-GPU-proven partitions queued in the channel.

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This is the real issue: the synthesis queue piles up with synthesized proofs (each holding ~16 GiB of data: 12 GiB provers + 4 GiB aux). With pw=12 synthesizing faster than the GPU can consume, the queue grows.

This article unpacks every dimension of this message: the reasoning that led to it, the decisions embedded in the instrumentation strategy, the assumptions that were validated and invalidated, the input knowledge required to interpret the counters, and the output knowledge that would drive the next phase of optimization. By examining a single message in depth, we gain insight into the iterative, data-driven process of high-performance GPU pipeline optimization.

Context: The Optimization Journey

To understand message 3106, we must first understand where it sits in the broader optimization effort. The session is part of a multi-phase project to optimize the SUPRASEAL_C2 Groth16 proof generation pipeline used by Curio, a Filecoin storage provider. The pipeline is a complex beast: Go code in Curio orchestrates proof generation tasks, which flow through Rust FFI into C++ CUDA kernels that run on NVIDIA GPUs. The peak memory footprint was approximately 200 GiB, but real-world workloads were hitting 668 GiB and OOMing on a 755 GiB system.

The optimization had progressed through multiple phases:

The Instrumentation Design: Decisions and Assumptions

The assistant responded to the user's suggestion by designing a global buffer tracker with atomic counters. This was a non-trivial architectural decision. The counters needed to track:

  1. synth — number of active synthesis tasks
  2. provers — number of ProvingAssignment sets (a/b/c vectors) alive
  3. aux — number of aux_assignment buffers alive
  4. shells — number of density-only shells (post-abc-free)
  5. pending — number of pending proof handles The assistant placed these counters in the pipeline.rs module as global atomic integers, with a log_buffers() helper that printed a summary line at key events. The hooks were inserted at: - buf_synth_start() — when synthesis begins - buf_synth_done() — when synthesis completes - buf_abc_freed() — after prove_start returns (a/b/c freed) - buf_finalize_done() — after finish_pending_proof returns - buf_dealloc_done() — when the Rust dealloc thread completes A critical design decision was where to place the buf_dealloc_done() hook. The deallocation happens inside bellperson, a separate crate that doesn't know about the pipeline module's counters. The assistant initially tried to add the hook directly in bellperson using eprintln! (since the crate already used eprintln! for CUZK_TIMING), but this meant the counter decrement couldn't easily call the pipeline module's function. The assistant noted this limitation but proceeded, planning to use the log output to at least observe whether deallocation was happening. This decision embodied an assumption: that the counter values would be directionally correct even if one counter (aux) couldn't be decremented. The assistant assumed the aux counter would monotonically increase, which would still be useful for detecting whether aux buffers were accumulating. As we'll see, this assumption was partially validated — the aux counter did reveal a problem, but not the one the assistant initially thought.

The Moment of Discovery: Reading the Counters

When the assistant ran the benchmark with pw=12 and examined the buffer logs, the numbers were startling. The key lines from the log showed:

BUFFERS[synth_done]: synth=3 provers=28 aux=97 shells=69 pending=0 est=727GiB
BUFFERS[synth_done]: synth=2 provers=28 aux=98 shells=70 pending=0 est=732GiB
BUFFERS[synth_done]: synth=1 provers=27 aux=99 shells=72 pending=0 est=724GiB

The assistant's reaction — "There it is!" — captures the excitement of a diagnostic breakthrough. The numbers immediately told a story that earlier black-box observations (RSS peaking at 668 GiB) could not explain.

The assistant's reasoning process in the message is a masterclass in reading instrumentation data:

Step 1: Identify the aux counter anomaly. The aux counter reached 99, but the dealloc thread was running (confirmed by BUFFERS[rust_dealloc_finish] log lines). The assistant correctly deduced that buf_dealloc_done() was never called from bellperson, so the counter was accumulating without decrementing. However, the assistant also correctly noted that the actual memory WAS being freed — the counter was just wrong.

Step 2: Pivot to the provers counter. Even accounting for the aux counter bug, the provers=28 was the real smoking gun. With only 12 partition workers (pw=12), having 28 ProvingAssignment sets alive meant that sets were accumulating faster than they were being consumed. Each ProvingAssignment set holds the a/b/c NTT evaluation vectors, approximately 12 GiB each. 28 × 12 GiB = 336 GiB just for a/b/c vectors.

Step 3: Trace the accumulation path. The assistant reasoned that provers tracks the count of synthesized partitions that haven't yet been through prove_start (where a/b/c are freed). With j=10 concurrent proofs × 10 partitions each = 100 partitions total, and the GPU processing them at ~3.5 seconds per partition, the synthesis side (pw=12) was producing partitions faster than the single-slot GPU channel could consume them. The backlog of 28 synthesized-but-not-yet-GPU-proven partitions was the memory killer.

Step 4: Identify the fundamental mismatch. The root cause was a throughput imbalance: the CPU synthesis pipeline (12 parallel workers) could produce partitions faster than the GPU could prove them, and the channel between them had no capacity limit. Each synthesized partition in the queue held its full ~16 GiB dataset (12 GiB a/b/c + 4 GiB aux), waiting for its turn on the GPU.

What the Message Reveals About the System Architecture

To fully appreciate the assistant's diagnostic reasoning, we need to understand the system architecture that the counters were measuring. The SUPRASEAL_C2 pipeline has three main stages:

  1. Synthesis (CPU-bound): Builds circuits from vanilla proofs, runs bellperson::synthesize_circuits_batch() to produce intermediate state including a/b/c NTT evaluation vectors (~12 GiB per partition), density trackers, and witness assignments (~4 GiB per partition for aux).
  2. GPU Prove (GPU-bound): Takes the synthesized data and runs the Groth16 proving protocol on GPU, including multi-scalar multiplication (MSM), number-theoretic transform (NTT), and the split b_g2_msm operation.
  3. Finalization (CPU post-processing): Completes the proof by combining GPU results with the witness assignments. The Phase 12 split API had decoupled the GPU prove and finalization stages, allowing the GPU worker to start the next proof while finalization runs in a background thread. However, the synthesis-to-GPU channel was a critical bottleneck: it had a capacity of 1 (a single-slot channel), meaning only one synthesized partition could be delivered to the GPU at a time. The buffer counters revealed that the synthesis workers were completing partitions and sending them into the channel, but the channel's single-slot capacity meant that completed jobs were piling up in the channel's buffer. The channel's buffer was unbounded (implemented as tokio::sync::mpsc::channel), so it would accept any number of jobs, each holding ~16 GiB of data. This was the root cause that earlier analysis had missed. The assistant had previously assumed the memory pressure came from glibc arena fragmentation or from the pending proof handles holding memory unnecessarily. The buffer counters provided direct evidence that the real problem was the sheer volume of synthesized data queued between the CPU and GPU stages.

Assumptions Validated and Invalidated

Message 3106 is particularly valuable for the way it reveals the assistant's evolving understanding. Several assumptions were challenged:

Assumption 1: The early a/b/c deallocation would solve the memory problem. The assistant had implemented early deallocation of a/b/c vectors in prove_start, expecting this to free ~12 GiB per partition immediately. The buffer counters showed that while this helped at the individual partition level, it didn't address the systemic issue of 28 partitions accumulating in the queue. The deallocation only happens when prove_start is called, but partitions were waiting in the channel before prove_start was invoked.

Assumption 2: The aux counter bug was the main issue. The assistant initially focused on the aux counter not being decremented, but quickly realized the actual memory was being freed — the counter was just wrong. The real issue was the provers counter.

Assumption 3: pw=12 would only add ~26 GiB more synthesis memory. Earlier, the assistant had calculated that increasing from pw=10 to pw=12 would add only 2 more synthesis workers × ~13 GiB each = ~26 GiB. The actual peak increase was ~300 GiB, which the assistant initially attributed to glibc fragmentation. The buffer counters showed the real cause: the queue backlog, not fragmentation.

Assumption 4: The channel capacity of 1 would prevent backlog. The assistant had designed the channel with capacity 1, assuming this would naturally limit the queue. However, tokio::sync::mpsc::channel with capacity 1 only limits how many items are in the channel itself — it doesn't prevent the sender from queueing items in an internal buffer. The synthesized jobs were being sent and buffered internally, growing unboundedly.

The Thinking Process: A Window into Diagnostic Reasoning

The message's structure reveals the assistant's thinking process in real-time. We can observe several cognitive patterns:

Pattern 1: Triangulation. The assistant doesn't accept any single counter at face value. It cross-references the aux counter (99) with the observation that dealloc IS happening (confirmed by log lines), concluding the counter is wrong. It then pivots to the provers counter as a more reliable indicator.

Pattern 2: Quantitative reasoning. The assistant immediately converts counter values to memory estimates: 28 provers × 12 GiB = 336 GiB, 97 aux × 4 GiB = 388 GiB. This quantitative framing makes the problem concrete and actionable.

Pattern 3: System-level thinking. Rather than focusing on individual buffers, the assistant traces the flow of data through the pipeline: synthesis produces partitions → they enter the channel → GPU consumes them one at a time. The imbalance between production rate (12 workers) and consumption rate (1 GPU slot) is identified as the root cause.

Pattern 4: Self-correction. The assistant starts with one hypothesis (aux counter bug), tests it against other evidence (dealloc IS happening), and quickly pivots to a more productive line of inquiry (provers backlog). This willingness to abandon initial hypotheses is characteristic of effective debugging.

Input Knowledge Required

To understand message 3106, a reader needs substantial domain knowledge:

  1. Groth16 proof structure: Understanding that a/b/c are NTT evaluation vectors (~12 GiB per partition), aux_assignments are witness data (~4 GiB), and these are the primary memory consumers.
  2. Pipeline architecture: Knowledge of the three-stage pipeline (synthesis → GPU prove → finalization) and the channel-based communication between stages.
  3. Atomic counter semantics: Understanding that global atomics can track buffer counts across threads, and that missing decrements cause counter drift.
  4. The Phase 12 split API: Knowing that prove_start now returns a handle and frees a/b/c early, while prove_finish completes the proof later.
  5. Benchmark parameters: pw=12 means 12 partition workers, j=10 means 10 concurrent proofs, each proof has 10 partitions.
  6. Memory sizing: Knowing that each partition's data is ~16 GiB (12 GiB a/b/c + 4 GiB aux), and that the system has 755 GiB total RAM. Without this knowledge, the message's significance would be lost. The assistant's ability to interpret the counters depends on a deep mental model of the system's memory layout and data flow.

Output Knowledge Created

Message 3106 produces several pieces of actionable knowledge:

  1. The queue backlog is the root cause. The synthesis pipeline can produce partitions faster than the GPU can consume them, causing a growing backlog of ~16 GiB partitions in the channel buffer.
  2. The backlog size is quantifiable. At peak, 28 provers were queued, representing ~336 GiB of a/b/c vectors alone. This is the primary driver of the 668 GiB RSS peak.
  3. The aux counter instrumentation is incomplete. The buf_dealloc_done() hook needs to be called from bellperson, requiring either a cross-crate callback or a different instrumentation approach.
  4. The channel capacity control is ineffective. tokio::sync::mpsc::channel with capacity 1 doesn't prevent the sender from buffering internally. The channel needs either a bounded sender or a different synchronization mechanism.
  5. The fix direction is clear. The assistant needs to either slow down synthesis (reducing pw), increase GPU throughput, or limit the channel buffer size to prevent unbounded queue growth. This output knowledge directly drives the next optimization steps. In the subsequent messages (not shown in this article), the assistant would implement a fix: increasing the channel capacity from 1 to partition_workers and holding the semaphore permit until the job is delivered to the channel, dramatically reducing peak RSS from 668 GiB to 294.7 GiB.

Conclusion: The Power of Instrumentation

Message 3106 is a testament to the power of targeted instrumentation in performance debugging. For weeks, the optimization effort had been chasing memory issues through indirect evidence — RSS peaks, OOM logs, and theoretical calculations. The addition of atomic buffer counters transformed the diagnostic process from inference to direct observation.

The assistant's reasoning in this message demonstrates several principles of effective performance analysis: