The Instrumentation That Saved the Pipeline: Tracing Memory in a GPU Proving System

The Message

[assistant] [edit] /home/theuser/curio/extern/cuzk/cuzk-core/src/engine.rs Edit applied successfully.

At first glance, this message is unremarkable — a routine edit confirmation from an AI assistant working on a high-performance GPU proving pipeline for Filecoin's proof-of-replication (PoRep) protocol. The assistant has just applied a change to engine.rs, one of the core files in the cuzk proving engine. But this single edit, applied in message [msg 3087], represents a pivotal moment in a multi-day debugging odyssey. It is the moment the team stopped guessing about memory and started seeing it.

The Crisis: Persistent OOM at Scale

To understand why this edit matters, we must understand the crisis that preceded it. The team had been implementing "Phase 12" of a series of optimizations to the SUPRASEAL_C2 Groth16 proof generation pipeline — a system that produces cryptographic proofs for Filecoin storage verification. The pipeline is split into two phases: CPU-bound circuit synthesis (building constraint systems from "vanilla" proofs) and GPU-bound proving (running NTT, MSM, and other cryptographic operations on CUDA hardware). The goal of Phase 12 was to hide the latency of the b_g2_msm operation by offloading it into a background thread, decoupling the GPU worker's critical path from CPU post-processing.

The Phase 12 implementation had succeeded in producing a working split API, and benchmarking with the optimal configuration (gw=2, pw=10, gt=32, j=15) yielded a respectable 37.1 seconds per proof — a ~2.4% improvement over the Phase 11 baseline of 38.0 seconds. But the team wanted more. The natural next step was to increase synthesis parallelism: if pw=10 (partition workers = 10) worked, why not try pw=12 or pw=15? More parallel synthesis should mean more throughput.

It did not work. At pw=12, the daemon crashed with out-of-memory (OOM) errors, with RSS peaking at a staggering 668 GiB on a 755 GiB system. The early deallocation of a, b, c NTT evaluation vectors (~12 GiB per partition, freed immediately after prove_start) had only shaved ~18 GiB off the peak. Something much larger was consuming memory, and nobody knew what.

The User's Insight

In message [msg 3076], the user asked a deceptively simple question:

"Can we count and report number of each large buffer in flight and maybe the stage?"

This question reframed the entire debugging effort. Instead of guessing which data structure was responsible for the memory bloat, the user proposed direct instrumentation: count every large buffer class at every stage of the pipeline. This is the difference between hunting in the dark and turning on the lights.

The assistant immediately recognized the value of this approach. In [msg 3077], it enumerated the key buffer classes to track:

1. ProvingAssignment a/b/c — ~12 GiB per partition (should be freed early)
2. aux_assignments — ~4 GiB per partition (held until finalize)
3. input_assignments — tiny
4. C++ split_vectors/tails — in the pending proof handle
5. C1 parsing buffers — raw JSON + deserialized vanilla proofs

Building the Buffer Tracker

The assistant's plan, laid out in [msg 3077] and [msg 3078], was to add global atomic counters for each buffer class and print a summary line at every key lifecycle event: synthesis start, synthesis complete, prove_start return, prove_finish return, and deallocation complete. These counters would live in the pipeline.rs module, which has visibility into all pipeline stages.

The first step, executed in [msg 3079], was adding the counter infrastructure to pipeline.rs — a set of atomic integers (buf_synth_start, buf_abc_freed, buf_dealloc_done, etc.) and a log_buffers() helper function that prints the current state of every counter. This was the scaffolding.

The second step — message [msg 3087] — was wiring those counters into the engine. The assistant edited engine.rs to call buf_synth_start() when synthesis begins, buf_synth_done() when it completes, and so on at each transition point. The edit was applied to the file at the heart of the proving engine's worker loop, where SynthesizedProof objects are dispatched through channels between CPU synthesis threads and GPU worker threads.

Why This Edit Was Critical

The edit in [msg 3087] was not a performance optimization. It did not make the code faster, reduce memory usage, or fix a bug. It was pure instrumentation — observability for its own sake. And yet it was arguably the most important single change in the entire Phase 12 cycle, because it enabled the team to see what was actually happening.

The buffer counters revealed the true bottleneck almost immediately. The provers counter — tracking how many synthesized partitions were queued and holding their full ~16 GiB datasets — peaked at 28. Twenty-eight synthesized partitions were alive simultaneously, each holding gigabytes of evaluation data, waiting for their turn on the GPU. This was the memory explosion.

The root cause was a subtle scheduling pathology. The partition semaphore (configured to pw=12) released its permit immediately after synthesis completed, allowing the next synthesis task to start. But the synthesized job then had to wait for a single-slot GPU channel to become available. The GPU channel had a capacity of exactly 1 — only one job could be delivered to the GPU at a time. So synthesis tasks would pile up behind the GPU, each holding its full memory footprint, while the semaphore cheerfully admitted more and more tasks into the queue.

The buffer counters made this visible in real time. Without them, the team would have continued guessing — blaming glibc fragmentation, SRS loading, or C1 parsing overhead. With them, the diagnosis was unambiguous: 28 queued partitions × ~16 GiB each = ~448 GiB of unnecessary memory pressure.

Assumptions and Mistakes

The instrumentation effort rested on several assumptions, some of which proved incorrect:

Assumption 1: The early a/b/c deallocation would be sufficient. The assistant had assumed that freeing the ~12 GiB NTT evaluation vectors immediately after prove_start would solve the memory problem. This was based on a correct analysis — the GPU was indeed done with those vectors by that point — but it addressed the wrong bottleneck. The real problem was not how long individual buffers lived, but how many were alive simultaneously.

Assumption 2: Memory pressure came from individual large allocations. The team had been thinking in terms of per-partition memory footprint: if each partition uses ~16 GiB, and we have 12 workers, that's ~192 GiB, which should fit in 755 GiB. But this ignored queuing effects. The actual number of in-flight partitions could far exceed the worker count when the GPU channel is a bottleneck.

Assumption 3: The semaphore would naturally limit memory. The partition semaphore was designed to limit concurrency, but its early-release semantics defeated this purpose. The semaphore counted "synthesis slots," not "total in-flight memory." This is a classic concurrency pitfall: bounding one resource (CPU threads) while ignoring another (GPU channel capacity) creates unbounded accumulation at the boundary.

Input Knowledge Required

To understand this message and its significance, one needs:

• Knowledge of the cuzk proving pipeline architecture: The two-phase split (CPU synthesis → GPU proving), the channel-based communication between phases, and the role of the SynthesizedProof struct.
• Understanding of Groth16 proof generation: The distinction between NTT evaluation vectors (a/b/c), witness assignments (input/aux), and density trackers, and why different components have vastly different sizes (~12 GiB vs ~4 GiB vs ~16 MB).
• Familiarity with Rust concurrency primitives: tokio::sync::mpsc channels, semaphores, atomic counters, and the async worker model used by the engine.
• Awareness of the OOM debugging context: The failed pw=12 experiments, the RSS monitoring, and the earlier use-after-free fix in the C++ CUDA code.

Output Knowledge Created

This message produced:

• Instrumented code: The engine.rs file now calls buffer counter hooks at every key lifecycle event, enabling real-time visibility into memory pressure.
• A diagnostic framework: The combination of buf_synth_start, buf_synth_done, buf_abc_freed, buf_finalize_done, and buf_dealloc_done counters, plus the log_buffers() helper, creates a complete picture of buffer flow through the pipeline.
• The foundation for the fix: The buffer counters would soon reveal the 28-partition queue, leading to two attempted fixes (holding the semaphore until GPU delivery, then increasing channel capacity) and ultimately a stable pw=12 configuration.

The Thinking Process

The assistant's reasoning in the messages leading up to [msg 3087] reveals a methodical debugging approach. In [msg 3077], it enumerates the buffer classes and their sizes, showing a clear mental model of the memory layout. In [msg 3078], it decides where to place the counters ("in the pipeline module where we have visibility into all stages"), demonstrating architectural awareness. In [msg 3080], it plans the six hook points with precise lifecycle semantics.

The decision to instrument at the engine level rather than at every synthesis path (considered in [msg 3083]–[msg 3086]) shows practical engineering judgment. The synthesis paths are numerous and varied — partition synthesis, monolithic synthesis, porep-specific paths — but they all converge at the engine's channel dispatch. By instrumenting the convergence point, the assistant gets complete coverage with minimal code changes.

The Broader Significance

Message [msg 3087] exemplifies a principle that separates effective debugging from guesswork: you cannot optimize what you cannot measure. The team had been operating on a mental model of memory usage that was fundamentally wrong — they thought in terms of per-worker footprint when the real issue was queue depth. No amount of reasoning about glibc fragmentation or SRS loading would have revealed the 28-partition queue. Only direct instrumentation could do that.

The edit itself is small — a few lines added to engine.rs to call counter functions at key points. But it represents a shift from intuition-based debugging to evidence-based debugging. It is the moment the team stopped asking "how much memory does each partition use?" and started asking "how many partitions are alive right now?" — and that reframing was the key to solving the problem.

In the subsequent messages, the buffer counters would reveal the truth, the assistant would iterate through two mitigation strategies, and the pipeline would eventually run stably at pw=12 with peak RSS reduced from 668 GiB to 294.7 GiB. But none of that would have been possible without the instrumentation applied in this single, unassuming edit.