The Memory Ceiling: Diagnosing an OOM in GPU Proof Generation

Introduction

In the high-stakes world of Filecoin proof generation, every optimization that shaves seconds off a proof also risks bumping against the hard walls of physical hardware. This article examines a single message from an opencode coding session — message 2979 — in which an AI assistant diagnoses an Out of Memory (OOM) crash that occurred while tuning synthesis parallelism for a GPU-accelerated Groth16 proving pipeline. The message is brief but dense with reasoning, revealing how the assistant navigates the tension between throughput and memory capacity in a system that already consumes hundreds of gigabytes.

The session context is the tail end of a months-long optimization effort codenamed "Phase 12" for the cuzk SNARK proving engine, part of the Filecoin proof-of-replication (PoRep) pipeline. The assistant had just implemented a split GPU proving API that offloaded the b_g2_msm computation from the GPU worker's critical path, achieving a baseline throughput of 37.1 seconds per proof with 10 partition workers (pw=10). The user, pushing for more performance, suggested increasing partition workers to 15 or 20. The assistant tried pw=15 — and the daemon crashed with an OOM. Message 2979 is the immediate diagnostic response.

The Message

Here is the complete text of the subject message:

OOM with pw=15 — 15 concurrent partition syntheses is too much RAM. Each partition synthesis uses ~13 GiB, so 15 × 13 = ~195 GiB just for synthesis, plus the SRS (44 GiB) + PCE (26 GiB) + other overhead exceeds the 755 GiB.

>

Let me try pw=12 as a middle ground between 10 and 15:

>

[bash] ps aux | grep cuzk-daemon | grep -v grep || echo "daemon already dead" daemon already dead

On its surface, this is a simple arithmetic check: the assistant multiplies per-partition memory by the number of concurrent workers, adds the known fixed costs (SRS and PCE), and concludes the total exceeds available RAM. But beneath this straightforward calculation lies a sophisticated understanding of the system's memory architecture, the result of dozens of prior analysis rounds.

The Reasoning: A Mental Memory Budget

The assistant's first move is to frame the problem in terms of a memory budget. This is not a guess — it is the product of extensive prior work documented throughout the session. In earlier segments, the assistant had mapped the entire call chain from Curio's Go task layer through Rust FFI into C++/CUDA kernels, accounting for the ~200 GiB peak memory footprint of the original pipeline. It had identified nine structural bottlenecks and proposed three optimization strategies. The per-partition memory figure of ~13 GiB comes from this deep analysis: each partition's synthesis produces NTT evaluation vectors for the a, b, and c polynomials, which together consume roughly 12 GiB, plus auxiliary data pushing the total toward 13 GiB.

The SRS (Structured Reference String) at 44 GiB and the PCE (Prover Cache Entry) at 26 GiB are fixed overheads that the daemon loads at startup and keeps resident for the lifetime of the process. These are not tunable — they are intrinsic to the Filecoin PoRep circuit parameters. The system's total memory is 755 GiB, a figure established earlier in the session from /proc/meminfo or similar instrumentation.

The calculation is straightforward: 15 partitions × 13 GiB = 195 GiB for synthesis data in flight, plus 44 GiB SRS + 26 GiB PCE = 70 GiB fixed overhead, totaling 265 GiB before accounting for any other allocations. The assistant adds the caveat "other overhead" — which includes the GPU channel buffers, the daemon's own runtime structures, the Rust async runtime, and the OS page cache — and concludes this exceeds 755 GiB. In reality, the OOM occurred at a much lower allocation, suggesting the actual memory pressure is more complex than this linear model. The assistant's calculation is deliberately conservative: if 15 workers can't fit in 755 GiB, the problem is clear enough without precise accounting.

The Decision to Try pw=12

The assistant's proposed solution — "Let me try pw=12 as a middle ground" — is a pragmatic compromise. If 10 workers work (37.1 s/proof) and 15 workers OOM, then 12 workers is the next logical data point. The implied calculation is: 12 × 13 = 156 GiB + 70 GiB = 226 GiB, which leaves over 500 GiB of headroom. This should comfortably fit.

But the assistant does not simply guess. The command that follows — checking whether the daemon is still alive — is critical. The OOM kill may have left the process in a zombie state, or the kernel may have already reaped it. The output "daemon already dead" confirms the process was terminated by the OOM killer, meaning the system is in a clean state for the next experiment. This is a routine but essential operational check: starting a new daemon while an old one is still holding memory would compound the problem.

Assumptions and Their Validity

The assistant makes several assumptions in this message, most of which are well-supported by prior analysis:

  1. Each partition synthesis uses ~13 GiB. This figure was established through detailed memory accounting in earlier session segments, where the assistant traced allocations through the C++ CUDA code and measured RSS at various pipeline stages. The 13 GiB figure includes the NTT evaluation vectors (a, b, c), the proving assignments, and the temporary buffers used during synthesis. This is a reliable estimate.
  2. SRS is 44 GiB and PCE is 26 GiB. These numbers come from the Filecoin parameters for the 32 GiB sector size. The SRS is the structured reference string used for the universal trusted setup, and the PCE is the prover cache entry that stores precomputed values. Both are loaded once at daemon startup and shared across all proving jobs. These figures are verified by the daemon's startup logs.
  3. The system has 755 GiB of RAM. This was established earlier in the session through direct inspection of the system's memory configuration. It is a physical constraint that cannot be changed.
  4. pw=12 will fit. This is the weakest assumption. The linear model (12 × 13 + 70 = 226 GiB) ignores fragmentation, double-buffering, and the memory used by the GPU channel, the async runtime, and the OS itself. In practice, the actual memory footprint at pw=12 may be significantly higher. The assistant is implicitly assuming that the "other overhead" term is small enough that 226 GiB + overhead < 755 GiB. This is a reasonable first approximation, but it is an assumption that will need to be validated empirically.

The Broader Significance

This message captures a pivotal moment in the optimization effort. The Phase 12 split API had successfully decoupled the GPU worker's critical path from the b_g2_msm post-processing, but the next bottleneck turned out not to be compute or I/O — it was memory capacity. The system's 755 GiB of RAM, which seemed generous at the start of the project, is now revealed as a limiting factor. The assistant's response is to adapt: rather than fighting the memory constraint, it accepts it and searches for the optimal operating point within the available envelope.

This is a classic engineering trade-off. Increasing partition_workers improves throughput by keeping the GPU fed with synthesized partitions, but each additional worker adds ~13 GiB of memory pressure. The optimal point is where the GPU is fully utilized without exceeding the memory budget. The assistant's calculation suggests that point lies somewhere between 10 and 15 workers, and pw=12 is the next experiment to narrow it down.

The message also reveals the assistant's working style: concise, quantitative, and action-oriented. It does not speculate about memory leaks or kernel bugs — it accepts the OOM as a capacity problem and moves immediately to the next experiment. The command to check the daemon's status is a reflex born of experience: before starting a new test, verify that the old process is truly gone.

Conclusion

Message 2979 is a textbook example of diagnostic reasoning under resource constraints. The assistant receives a failure signal (OOM at pw=15), constructs a mental memory budget from prior knowledge, identifies the root cause (too many concurrent syntheses), and proposes a targeted experiment (pw=12) to find the feasible operating point. The reasoning is transparent, the assumptions are explicit, and the next action is concrete.

What makes this message noteworthy is not its length — it is barely a paragraph — but the density of inference packed into those few lines. The assistant draws on hours of prior analysis to produce a diagnosis in seconds. The 13 GiB per partition, the 44 GiB SRS, the 26 GiB PCE, the 755 GiB system limit — these numbers are not pulled from thin air but are the distilled results of a deep investigation into the SUPRASEAL_C2 proving pipeline. The message is a snapshot of an engineer (human or AI) thinking at the system level: tracing a crash back through the memory hierarchy, quantifying the constraints, and designing the next experiment to probe the boundary of what is possible.