Diagnosing the Real OOM: How a Single Message Uncovered the Concurrency Scaling Problem in CuZK Proving

The Message

In the course of debugging persistent Out-of-Memory (OOM) failures during CuZK PoRep proving benchmarks on Vast.ai GPU instances, the assistant produced the following analysis (message index 1123):

I see the issue. The BENCH_CONCURRENCY=5 at line 19 is hardcoded. With 2 GPUs and concurrency 5, we're running 5 proofs simultaneously. Each proof with 10 partition workers generates ~30-40GB of memory demand. 5 * 40GB = 200GB just for synthesis, plus SRS (44GB), PCE (26GB), OS overhead = way more than available.

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The fix should scale concurrency to GPU count. For the benchmark, a reasonable approach: - Concurrency = num_gpus * 2 — this creates a small queue but doesn't overwhelm memory - Or even simpler: concurrency = num_gpus for the benchmark

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Also, partition_workers=10 with 5 concurrent proofs means 50 worker threads synthesizing simultaneously — that's the real problem.

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Let me fix the entrypoint to set a smarter concurrency: [read] /tmp/czk/docker/cuzk/entrypoint.sh

This message represents a pivotal diagnostic moment in a much larger debugging session. It is not a triumphant "fix found" declaration, but rather the crystallization of a correct understanding of a complex systems problem. The assistant had already solved one OOM problem (the warmup phase with PCE extraction), but a second, more subtle OOM problem had emerged during steady-state benchmark operation. This message is where the assistant correctly identifies that the concurrency setting — not just the partition workers — is the root cause.

Context: The OOM Saga

To understand why this message was written, we must trace the debugging arc that preceded it. The session (Segment 8 of a larger conversation) was focused on resolving OOM failures in low-RAM GPU instances running the CuZK proving engine for Filecoin PoRep (Proof of Replication) benchmarks. The proving pipeline involves several memory-intensive components: a 44GB SRS (Structured Reference String) file that must be loaded into memory, a 26GB PCE (Pre-Compiled Constraint Evaluator) cache generated during the first proof, and the partition synthesis itself, which spawns multiple worker threads to compute GPU proofs in parallel.

The first OOM problem had been solved in earlier messages. The assistant discovered that when no PCE cache existed, the daemon's initial proof would trigger both PCE extraction and partition synthesis simultaneously, causing a memory spike that killed low-RAM instances. The fix was elegant: detect the absence of a PCE file, start the daemon with partition_workers=2 for the warmup proof, let the PCE cache be generated, then restart the daemon with the full partition count for the actual benchmark. This fix was deployed and worked correctly — the US instance (2x RTX 3090, 376GB RAM) successfully completed its warmup with partition_workers=2, generating the 26GB PCE cache without crashing.

But then a second crash occurred. During the actual benchmark, with partition_workers=10 and concurrency=5, the process was killed. The assistant's earlier messages ([msg 1115] through [msg 1121]) document the frantic investigation: checking dmesg, finding the container unreachable, discovering via the vast-manager dashboard that the instance had been automatically destroyed with bench_rate: 0.0. The lifecycle management code the assistant had written earlier — which automatically destroys underperforming instances — had worked correctly, but it had destroyed an instance that should have been capable of passing the benchmark. Something was fundamentally wrong with the configuration.

The Diagnostic Leap

Message 1123 is where the assistant makes the critical diagnostic leap. The key insight is expressed in a single sentence: "5 concurrent proofs x 10 partition workers = too much memory, even with 376GB RAM." This is the moment the assistant realizes that the problem is not just about peak memory during warmup, but about steady-state memory pressure during concurrent proving.

The assistant performs a rough back-of-the-envelope calculation: each proof with 10 partition workers generates ~30-40GB of memory demand. With 5 concurrent proofs, that's 150-200GB just for synthesis. Add the 44GB SRS file, the 26GB PCE cache, and OS overhead, and the total exceeds the 376GB available. The calculation is approximate but directionally correct — and it reveals the fundamental issue.

The assistant then identifies the root cause: the BENCH_CONCURRENCY=5 variable at line 19 of entrypoint.sh is hardcoded. It does not scale with the available hardware. On a single-GPU instance with 500GB RAM (like the Norway instance that achieved 41.32 proofs/hour), concurrency 5 might be manageable. But on a dual-GPU instance with 376GB RAM, concurrency 5 creates a memory demand that exceeds the system's capacity.

The Proposed Solution

The assistant proposes two alternative formulas for scaling concurrency:

  1. Concurrency = num_gpus * 2 — which creates a small queue but doesn't overwhelm memory
  2. Concurrency = num_gpus — a more conservative approach Both proposals are grounded in a simple observation: GPU count is a proxy for both compute capacity and memory pressure. More GPUs mean more proofs can be processed in parallel, but each proof still consumes the same amount of RAM. By tying concurrency to GPU count rather than using a hardcoded value, the system automatically adapts to different hardware configurations. The assistant also notes a compounding factor: "partition_workers=10 with 5 concurrent proofs means 50 worker threads synthesizing simultaneously — that's the real problem." This observation is crucial because it reveals that the partition workers and concurrency settings interact multiplicatively. Ten partition workers per proof times five concurrent proofs equals fifty threads competing for memory. Reducing concurrency to 2 (for 2 GPUs) would bring this down to twenty threads, dramatically reducing memory pressure.

Assumptions and Their Validity

The assistant makes several assumptions in this message, most of which are reasonable but worth examining:

Assumption 1: Each proof with 10 partition workers generates ~30-40GB of memory demand. This is an estimate, not a precise measurement. The assistant does not have access to detailed per-proof memory profiling. However, the estimate is consistent with observable behavior: the daemon was using ~92GB RSS during the warmup with 2 partition workers, suggesting each worker consumes significant memory. Scaling up to 10 workers would increase memory proportionally, and multiplying by 5 concurrent proofs gives the ~200GB figure. The assumption is directionally correct even if the exact numbers are approximate.

Assumption 2: GPU count is a good proxy for optimal concurrency. This is reasonable for a GPU-bound workload, but it ignores other factors like memory bandwidth, PCIe topology, and the specific proof type. The assistant later refines this approach in subsequent messages, adding RAM-based scaling and eventually moving to a data-driven experimental system. The assumption is a useful heuristic, not a final answer.

Assumption 3: The hardcoded BENCH_CONCURRENCY=5 is the root cause. This is correct in the sense that it's the trigger, but the deeper root cause is the lack of hardware-aware configuration in general. The assistant's focus on this specific variable is appropriate for a tactical fix, but the strategic solution — a data-driven hardware discovery system — emerges later in the session.

Mistakes and Incorrect Assumptions

The most notable mistake in this message is not one of commission but of omission: the assistant does not consider that the partition workers count itself might need to scale with available RAM, not just the concurrency. The partition_workers=10 setting was derived from the RAM detection logic (line 156-157 of entrypoint.sh, which sets PARTITION_WORKERS=10 for systems with less than 400GB RAM). But on a 376GB system with 2 GPUs, 10 partition workers per proof might still be too aggressive even with reduced concurrency. The assistant later refines this in subsequent messages, adding a more nuanced RAM-based calculation.

Another subtle issue: the assistant proposes "concurrency = num_gpus * 2" as a reasonable approach, but this still allows 4 concurrent proofs on a 2-GPU system — only a reduction from 5 to 4. Given the memory pressure calculation, even 4 concurrent proofs might be too much. The more conservative "concurrency = num_gpus" (giving 2 concurrent proofs) is likely the safer choice, and the assistant presents both options without committing to one.

Input Knowledge Required

To fully understand this message, one needs:

  1. The architecture of CuZK proving: Knowledge that the proving pipeline involves SRS loading (~44GB), PCE cache generation (~26GB), and partition synthesis with multiple worker threads. Understanding that partition workers are spawned per-proof and consume significant RAM.
  2. The benchmark configuration: Understanding that BENCH_CONCURRENCY controls how many proofs run simultaneously, while PARTITION_WORKERS controls how many synthesis threads each proof uses. The interaction between these two parameters is critical.
  3. The hardware context: The US instance has 2x RTX 3090 GPUs and 376GB RAM. The Norway instance had 1x RTX 4090 and 500GB RAM. These differences explain why the same configuration worked on one instance but not the other.
  4. The prior debugging history: The assistant had already solved the warmup OOM problem with the partition_workers=2 fix. This message builds on that understanding to address a different phase of the benchmark.
  5. The lifecycle management system: The assistant had recently implemented automatic destruction of underperforming instances. This is why the crashed instance was immediately destroyed, creating urgency to fix the configuration before deploying new instances.

Output Knowledge Created

This message produces several valuable outputs:

  1. A correct diagnosis: The hardcoded concurrency is identified as the root cause of the steady-state OOM. This is a non-obvious finding because the symptom (process killed) looks identical to the earlier warmup OOM, but the cause is different.
  2. A scaling heuristic: The principle that concurrency should scale with GPU count is established. This becomes the foundation for the dynamic hardware-aware configuration system developed later in the session.
  3. A memory pressure model: The rough calculation (5 proofs × 10 workers × ~4GB per worker + SRS + PCE + overhead > total RAM) provides a mental model for reasoning about memory constraints. Even if the exact numbers are approximate, the model is useful for identifying bottlenecks.
  4. A direction for the fix: The assistant immediately proceeds to read the entrypoint.sh file and plan the code change. The message ends with "Let me fix the entrypoint to set a smarter concurrency," signaling the transition from diagnosis to intervention.

The Thinking Process

The assistant's reasoning in this message follows a clear logical progression:

  1. Observation: The benchmark crashed during steady-state operation, not during warmup. The warmup fix worked correctly.
  2. Quantification: The assistant calculates the memory demand: 5 concurrent proofs × 10 partition workers × estimated per-worker memory + fixed overheads (SRS, PCE, OS).
  3. Comparison: The calculated demand (~200GB+ for synthesis alone, plus 44GB SRS, 26GB PCE, etc.) exceeds the 376GB available.
  4. Attribution: The hardcoded BENCH_CONCURRENCY=5 is identified as the variable that, when combined with the hardware's memory limits, causes the OOM.
  5. Generalization: The assistant realizes that concurrency must be a function of hardware capabilities, not a fixed constant. GPU count is proposed as the scaling factor.
  6. Action: The assistant reads the entrypoint.sh file to understand the current RAM detection logic and plan the modification. This progression is notable for its clarity and economy. The assistant does not chase irrelevant details or explore dead ends. It goes directly from symptom to root cause to proposed fix in a single message, demonstrating a strong understanding of the system's memory architecture.

Broader Significance

This message is a microcosm of the entire debugging session. It illustrates a pattern that repeats throughout Segment 8: the assistant identifies a hardcoded parameter, traces its failure on specific hardware, and replaces it with a dynamic, hardware-aware calculation. The warmup fix replaced hardcoded partition_workers with a PCE-cache-dependent strategy. This message replaces hardcoded BENCH_CONCURRENCY with a GPU-count-dependent formula. Later messages will further refine the RAM-based calculations and eventually abandon hardcoded thresholds entirely in favor of a data-driven experimental system.

The message also reveals the assistant's working style: it uses rough quantitative estimates to guide reasoning, it generalizes from specific failures to systemic principles, and it moves quickly from diagnosis to action. The tone is confident but not triumphal — the assistant has found the right problem, but the fix is not yet implemented or tested.

Conclusion

Message 1123 is a turning point in the OOM debugging saga. It correctly identifies that the concurrency parameter, not just the partition workers, must be scaled to match available hardware. The assistant's calculation — 5 proofs × 10 workers × ~4GB each — provides a simple but effective model for understanding memory pressure. The proposed fix of scaling concurrency to GPU count is a reasonable heuristic that addresses the immediate problem while laying the groundwork for a more sophisticated, data-driven configuration system. This message exemplifies the kind of diagnostic reasoning that separates a superficial fix from a true understanding of system behavior.