Diagnosing the OOM: How One Message Uncovered the Root Cause of Benchmark Crashes in CuZK Proving

In the middle of a complex deployment pipeline for CuZK—a high-performance zero-knowledge proving engine for Filecoin—a single message from the AI assistant crystallized a critical debugging insight. Message [msg 1042] is a turning point in the session: it is the moment where scattered observations about out-of-memory (OOM) crashes coalesced into a precise root-cause diagnosis and a concrete remediation plan. This message, though brief and interleaved with operational checks, represents the kind of reasoning that separates a surface-level understanding from a deep, architectural comprehension of a system's failure modes.

The Context: A Deployment Under Strain

To appreciate the significance of this message, one must understand the broader context. The team had been deploying GPU instances on Vast.ai to benchmark CuZK's PoRep (Proof of Replication) proving performance. Two instances were active: a Norway-based machine with a single RTX 4090 and 500GB of RAM, and a BC Canada-based machine with dual RTX 3090s but only 125GB of RAM. The BC Canada instance had been killed—its benchmark produced a rate of zero—while the Norway instance was mid-benchmark, having just finished downloading Filecoin proof parameters.

In the messages immediately preceding [msg 1042], the assistant had been piecing together the state of the system. It discovered that the vast-manager API endpoint had changed (the route was /api/dashboard, not /api/instances), it confirmed that the BC Canada instance was in a killed state with bench_rate: 0, and it read the contents of benchmark.sh to understand the warmup workflow. These reconnaissance steps were essential groundwork, but they had not yet answered the central question: why did the BC Canada instance crash?

The Diagnostic Leap

Message [msg 1042] begins with a statement that reveals the assistant's reasoning process:

Good. I can see the issue clearly. The OOM problem on low-RAM machines (like the BC Canada 125GB instance) happens because the warmup starts the daemon with the full PARTITION_WORKERS (10 in that case). When PCE doesn't exist yet, the first proof triggers PCE extraction which runs all partition workers simultaneously, each allocating substantial memory.

This is the core insight. The assistant had connected three pieces of information:

The BC Canada instance had 125GB of RAM—far less than the Norway instance's 500GB, and less than the typical dual-3090 setup would expect.
The benchmark script started the daemon with a fixed PARTITION_WORKERS setting—in this case, 10 workers.
The first proof triggers Pre-Compiled Constraint Evaluator (PCE) extraction—a memory-intensive operation that synthesizes constraint systems for each partition. The critical realization is that PCE extraction does not happen lazily or incrementally. When the daemon starts without a pre-existing PCE cache, the very first proof request forces the system to generate PCE data for all partitions simultaneously. With 10 partition workers, each allocating significant memory for constraint synthesis, the total memory demand on a 125GB machine would spike dramatically. The Norway instance, with 500GB of RAM, could absorb this spike. The BC Canada instance could not.

The Proposed Fix: A Two-Phase Warmup

Having identified the root cause, the assistant immediately formulated a fix:

The fix: start the daemon with partition_workers = 2 for the warmup proof (just enough to generate PCE), then restart it with the full partition count for the actual benchmark.

This is a elegant solution that addresses the problem at its source. The reasoning is straightforward: PCE extraction needs to happen only once. Once the PCE cache files exist on disk, subsequent daemon starts can use the cached data without re-extracting. By limiting the daemon to just 2 partition workers during the warmup proof, the memory footprint of PCE extraction is dramatically reduced—from 10 simultaneous allocations to just 2. After the PCE cache is populated, the daemon can be restarted with the full complement of partition workers for the actual benchmark, where the memory pressure is lower because the constraint systems are already compiled.

This two-phase approach makes an implicit assumption: that 2 partition workers are sufficient to generate a complete PCE cache for all partitions. This is a reasonable assumption because PCE extraction is partition-independent—each worker handles its own partition's constraints, and the cache is built per-partition. Running 2 workers sequentially (or in parallel on 2 partitions at a time) would eventually cover all partitions, though it would take longer than running all 10 simultaneously. The trade-off is time versus memory, and on a memory-constrained machine, the trade-off is clearly worth making.

Operational Verification: Checking the Norway Benchmark

After articulating the diagnosis and fix, the assistant does not immediately jump to implementation. Instead, it pauses to check the Norway benchmark's progress:

Let me also check the Norway benchmark progress before making the fix.

This is a disciplined approach. Before modifying the benchmark script, the assistant wants to confirm that the existing code path works correctly on a machine with sufficient memory. The Norway instance serves as a control: if it completes its benchmark successfully, the fix can be applied to the BC Canada instance (or a replacement) without worrying about regressions.

The assistant runs two SSH commands. The first checks the benchmark results log:

=== Batch Benchmark ===
proof type:  porep
count:       12
concurrency: 5

The benchmark has just started—it has loaded the C1 output and printed the header, but no proofs have completed yet. The second command checks GPU utilization:

00000000:01:00.0, 100 %, 15852 MiB, 24564 MiB, 45

The GPU is at 100% utilization, using 15.8GB of its 24.5GB VRAM, running at 45°C. This confirms that the benchmark is actively computing proofs, not stuck or idle. The Norway instance is healthy and making progress.

Input Knowledge Required

To fully understand this message, a reader needs familiarity with several concepts:

CuZK's proving architecture: CuZK is a GPU-accelerated zero-knowledge proving engine. It uses a daemon process that handles proof requests and a set of "partition workers" that parallelize the synthesis and proving work across partitions of the circuit.
PCE (Pre-Compiled Constraint Evaluator): This is a compilation step that transforms high-level constraint representations into an optimized, GPU-ready format. PCE extraction is a one-time cost per circuit type—once the cache files exist, subsequent proofs reuse them.
The benchmark workflow: benchmark.sh starts the CuZK daemon, performs a warmup proof to initialize caches and warm GPU kernels, then runs a timed batch of proofs to measure throughput.
The deployment infrastructure: Vast.ai provides cloud GPU rentals. The vast-manager service tracks instances, their states, and benchmark results. Instances are identified by Vast.ai IDs (e.g., 32711932) and have associated metadata like GPU count, RAM, and geolocation. Without this knowledge, the message's reasoning would appear cryptic. The phrase "PCE extraction which runs all partition workers simultaneously" is the key technical insight, and understanding why that causes OOM requires knowing that each partition worker allocates memory for its own constraint synthesis.

Output Knowledge Created

This message produces several valuable outputs:

A confirmed root cause: The BC Canada OOM is definitively traced to the interaction between PARTITION_WORKERS count and PCE extraction memory demands. This is not a generic "out of memory" error—it is a specific, reproducible failure mode.
A concrete fix specification: The two-phase warmup approach is clearly described. This can be directly implemented in benchmark.sh without requiring changes to the CuZK daemon itself.
Operational status of the Norway instance: The benchmark has started, the GPU is fully utilized, and the system is healthy. This provides a baseline for comparison when the fix is deployed.
A diagnostic methodology: The assistant's approach—gathering state information, understanding the workflow, identifying the memory pressure point, and proposing a targeted fix—serves as a template for debugging similar issues in distributed proving systems.

The Thinking Process: A Window into Debugging

The assistant's reasoning in this message reveals a structured debugging process. It begins with a synthesis statement ("I can see the issue clearly") that signals a shift from data gathering to hypothesis formation. The diagnosis is presented as a causal chain: warmup starts with full workers → PCE doesn't exist → first proof triggers extraction → all workers run simultaneously → memory spikes → OOM on low-RAM machines.

Notably, the assistant does not list alternative hypotheses or describe eliminating them. This suggests that the evidence was already compelling: the BC Canada instance had low RAM, the Norway instance had high RAM, and the only difference in their configuration was the available memory. The partition workers setting was the same (10), so the variable was environmental, not configurational.

The fix is presented with equal confidence. The assistant does not say "we could try" or "one approach might be." It states "The fix:" as a definitive prescription. This confidence is warranted because the fix directly addresses the identified causal mechanism: reduce workers during the memory-intensive phase, then restore them for the compute-intensive phase.

Assumptions and Potential Pitfalls

While the diagnosis is sound, the message contains implicit assumptions worth examining:

That 2 partition workers is sufficient for PCE extraction: If PCE extraction requires all partitions to be processed simultaneously (e.g., because they share global state), then 2 workers might produce an incomplete cache. The assistant assumes partition-independence, which is reasonable but unverified.
That restarting the daemon mid-benchmark is safe: The warmup proof runs, then the daemon is killed and restarted with new settings. If the warmup proof's result is not properly persisted, the benchmark might start from a cold state. The assistant assumes that PCE caching is the only state that matters.
That the OOM was purely a RAM issue: While the diagnosis focuses on system RAM, GPU memory could also be a factor. The Norway instance's GPU was using 15.8GB of VRAM—close to the 24.5GB capacity. If partition workers also allocate GPU memory, reducing their count might help there too. These assumptions are not flaws in the reasoning; they are boundary conditions that would be tested when the fix is deployed. The assistant's next steps would naturally validate them.

Conclusion

Message [msg 1042] is a masterclass in targeted debugging. It takes scattered observations—a killed instance, a running benchmark, a configuration file—and synthesizes them into a precise causal explanation. The assistant demonstrates that understanding a system's architecture is more valuable than chasing generic error messages. By reasoning about why PCE extraction consumes memory and how partition workers interact with that process, it arrives at a fix that is minimal, targeted, and likely correct.

This message also illustrates the importance of operational discipline. Before implementing the fix, the assistant checks the control instance to confirm baseline behavior. It does not assume that the Norway benchmark will succeed—it verifies. This combination of deep architectural reasoning and practical operational checking is what makes the message a turning point in the session, transforming a series of failures into a clear path forward.