The 100% Failure Rate: Debugging a Systematic GPU Proving Bug in a Distributed Proof-of-Replication Pipeline

Introduction

In distributed systems debugging, intermittent failures are notoriously difficult to track down. But sometimes, the most revealing failure mode is the opposite: a perfect, 100% failure rate. When every single partition of a ten-partition proof-of-replication (PoRep) computation produces an invalid result, the randomness of flaky hardware or race conditions is ruled out. What remains is a systematic error—a structural bug in the code path itself.

This article examines a single message (message 1958) from an opencode coding session in which an AI assistant confronts exactly this situation. The user has just reported that after deploying a fix for a deadlock in the ProofShare system and rebuilding the Curio binary inside a Docker CUDA environment, all ten PoRep partitions are still producing invalid proofs. The benchmark passes, but the production pipeline fails consistently. The assistant's response—a detailed reasoning trace and a plan of investigation—reveals the thinking process of a developer tracing a subtle bug through a complex, multi-layered proving system spanning Go, Rust, and GPU code.

Context: The State of Play

To understand message 1958, we need to appreciate what has come before it. The session has been a marathon of production debugging across multiple subsystems of a Filecoin storage proving infrastructure called "Curio." The system involves:

The Subject Message: A Deep Reasoning Trace

Message 1958 is the assistant's response to this report. It is not a tool call or a simple acknowledgment—it is a window into the assistant's reasoning process, captured in a structured "Agent Reasoning" block followed by a todo list.

Let us quote the message in full:

## Agent Reasoning This is very interesting. The logs show:

>

1. ALL 10 partitions fail verification - not just some, ALL of them 2. The benchmark passes but run.sh + curio fails 3. Key detail: assembler not found, discarding partition result - the GPU is finishing partitions but the assembler has already been destroyed because the self-check failed earlier 4. The error message num_circuits=1 GPU proving bug is from our diagnostic code

>

The fact that benchmark passes but production fails is very telling. Let me think about what's different:

>

1. benchmark.sh likely uses a different code path (not partition_workers) 2. run.sh + curio uses partition_workers=16 which triggers Phase 7

>

So the pattern is clear: every single partition is producing invalid proofs, not intermittently but consistently. This points to a systematic GPU proving issue rather than a flaky problem. Once the self-check catches this and fails, the assembler gets torn down before the GPU can finish writing out the remaining partition results.

>

The real puzzle is why benchmark.sh succeeds while run.sh + curio fails. The benchmark probably uses a different code path entirely—maybe cuzk-bench with different settings like partition_workers=0 that would bypass the Phase 7 partitioned proving altogether. Or there's something about how num_circuits=1 is handled in the partitioned path versus batched proving that's causing the GPU to consistently generate bad proofs.

>

Let me trace through what's actually happening in the cuzk code between these two execution paths, because the fact that all 10 partitions are failing suggests this isn't a flaky issue but something systematic in the partition_workers=16 code path. 100% failure. What could cause ALL partitions to fail in the partitioned path?

>

Some possibilities: 1. A bug in how the partitioned path sets up the GPU job (wrong public inputs, wrong circuit parameters) 2. A bug in the ProofAssembler that corrupts the proof bytes 3. A bug in how num_circuits=1 is handled in the supraseal GPU code 4. A data issue - the C1 output being fed is somehow corrupted or incompatible

>

The log says num_circuits=1 - each partition is proved as a single circuit. The benchmark might prove all partitions as a batch (num_circuits=10). The supraseal GPU code path might behave differently for num_circuits=1 vs num_circuits>1.

>

I need to dig into the cuzk pipeline and engine code to understand the partitioned path better. The key questions are how partition_workers mode operates, how the benchmark proves circuits differently, and what's special about the num_circuits=1 case. Let me check the relevant Rust code.

>

This is a different issue from the self-check gating we added — all 10 partitions are systematically producing invalid proofs in the partition_workers path, while the benchmark (presumably non-partitioned) passes. Let me investigate.

>

[todo list with items about understanding benchmark vs run.sh, investigating partition_workers=16 path, checking bellperson supraseal num_circuits=1 handling, fixing root cause]

What the Message Accomplishes

This message is primarily an act of reframing. The assistant has just spent several messages deploying a fix for a job ID collision—a fix that was correct for the problem it addressed. But the user's new evidence shows that the deployed fix did not resolve the actual proving failures. The assistant could have responded defensively, or with a simple "let me investigate," but instead it performs a crucial intellectual move: it recognizes that the 100% failure rate is a different kind of problem than the intermittent failures previously debugged.

The reasoning trace reveals several key insights:

1. Distinguishing Systematic from Intermittent Failure

The assistant immediately zeroes in on the fact that all ten partitions fail. This is not a flaky GPU, not a race condition, not a memory corruption that affects some partitions and not others. A 100% failure rate in a system that otherwise works (the benchmark passes) points to a deterministic difference between two code paths. This is a classic debugging heuristic: when something works in one configuration but fails completely in another, look for what is different about the configuration, not what is broken about the hardware.

2. The "Assembler Not Found" Clue

The assistant notices a critical log line: assembler not found, discarding partition result. This is a temporal ordering clue. The GPU workers finish computing partition proofs, but by the time they return, the assembler (the component that collects and combines partition proofs) has already been destroyed. Why? Because the self-check—the diagnostic added in a previous round—detected the first invalid partition and triggered a failure, which tore down the assembler before the remaining GPU workers could deliver their results. This means the self-check, while well-intentioned, is actually masking the true failure mode by destroying evidence. The assistant recognizes this: the self-check is catching the symptom (invalid proofs) but the root cause is whatever is making the proofs invalid in the first place.

3. The Benchmark vs. Production Hypothesis

The assistant formulates a specific hypothesis: the benchmark (benchmark.sh) and the production pipeline (run.sh + curio) use different code paths. The benchmark likely runs with partition_workers=0 (no partitioned proving), while the production path uses partition_workers=16 which triggers "Phase 7" partitioned proving. This is a structural difference, not a configuration tweak—it means the two paths may execute fundamentally different code in the Rust proving engine.

4. The num_circuits=1 Observation

The logs show num_circuits=1 in the GPU timing output. The assistant hypothesizes that the benchmark might prove all partitions as a batch (num_circuits=10), while the partitioned path proves each partition individually (num_circuits=1). If the supraseal GPU code has a bug specific to the num_circuits=1 case—perhaps in how it sets up the MSM (multi-scalar multiplication) or NTT (number-theoretic transform) operations—then every single partition would fail identically.

Assumptions and Their Validity

The assistant makes several assumptions in this message, some explicit and some implicit:

Assumption 1: The benchmark uses a different code path. This is a reasonable inference from the evidence, but it is not yet confirmed. The assistant does not know what benchmark.sh actually does—it could be running the same code with different parameters, or it could be a completely separate binary (cuzk-bench). The assistant plans to check the code, which is the correct next step.

Assumption 2: The partition_workers setting is the key differentiator. The assistant assumes that partition_workers=0 (benchmark) vs partition_workers=16 (production) is the critical difference. This is a good hypothesis, but there could be other differences: different input data, different environment variables, different versions of the cuzk binary, or different GPU memory pressure.

Assumption 3: The self-check is correctly detecting invalid proofs. The assistant assumes the self-check is not itself buggy—that the proofs really are invalid, not that the verification is wrong. Given that the self-check was added in a previous round and has been tested, this is a reasonable assumption, but it is worth noting that a false positive in the self-check could also explain the 100% failure rate.

Assumption 4: The bug is in the GPU proving path, not in the input data. The assistant lists "a data issue—the C1 output being fed is somehow corrupted or incompatible" as a possibility, but the reasoning focuses more on the GPU code path. This is a reasonable prioritization given the evidence, but it is an assumption that the input data is correct.

Potential mistake: Overlooking the possibility that the fix itself introduced the bug. The assistant assumes the previous fix (job ID uniqueness) is correct and orthogonal to the new problem. But it is possible that the fix introduced a regression—for example, if changing the RequestId format somehow affected how the cuzk engine indexes or retrieves partition data. The assistant does not explicitly consider this possibility.

Input Knowledge Required

To fully understand this message, the reader needs knowledge of:

  1. Filecoin proof-of-replication (PoRep): The concept of partitioning a proof into multiple parallel computations, each producing a partial proof that must be assembled into a final valid proof.
  2. The Curio/cuzk architecture: That Curio is a Go-based node that orchestrates proving, while cuzk is a Rust-based GPU proving engine. The two communicate via FFI (foreign function interface).
  3. The supraseal GPU library: That low-level CUDA operations (MSM, NTT, GPU batch addition) are handled by a library called supraseal, and that num_circuits is a parameter controlling how many circuits are proved in a single GPU invocation.
  4. The ProofShare system: That proofshare challenges are distributed across workers, and that the job_id is used to correlate results from different workers.
  5. The previous debugging context: That a self-check was added in a previous round, that a job ID collision was fixed, and that the binary was rebuilt inside Docker.
  6. The concept of Phase 7 partitioned proving: That there is a specific pipeline phase (Phase 7) that handles partitioned proof assembly, and that partition_workers controls how many partitions are processed concurrently.

Output Knowledge Created

This message creates several valuable outputs:

  1. A reframed problem definition: The problem is no longer "proofs are invalid" but "all partitions fail systematically in the partitioned path while the non-partitioned path succeeds." This reframing narrows the search space dramatically.
  2. A prioritized hypothesis list: The assistant lists four possible root causes, ordered by likelihood: GPU job setup, ProofAssembler corruption, num_circuits=1 GPU bug, and data corruption. This provides a roadmap for investigation.
  3. A specific investigation plan: The assistant plans to read the cuzk Rust code to understand the difference between the benchmark and production paths, check how partition_workers mode operates, and examine the num_circuits=1 handling in supraseal.
  4. A temporal ordering insight: The observation that the assembler is destroyed before GPU workers finish reveals a secondary issue: the self-check, while useful, is destroying evidence by tearing down the assembler prematurely. This could inform future diagnostic improvements.
  5. A clear distinction from previous work: The assistant explicitly notes that "this is a different issue from the self-check gating we added," preventing confusion between the earlier fix and the new investigation.

The Thinking Process: A Masterclass in Systematic Debugging

What makes this message particularly valuable is the transparency of the thinking process. The assistant does not just state conclusions—it walks through the reasoning step by step, showing how it arrives at each insight.

The process follows a classic debugging methodology:

  1. Observe the pattern: All ten partitions fail, not some. This is the critical observation.
  2. Note what is NOT happening: The failure is not intermittent, not random, not hardware-dependent. This rules out whole categories of causes.
  3. Compare passing and failing configurations: Benchmark passes, production fails. What differs? The assistant hypothesizes partition_workers and num_circuits.
  4. Generate hypotheses: Four specific possibilities, each testable by reading code or adding diagnostics.
  5. Plan the investigation: The assistant will read the cuzk Rust code to confirm or refute the hypotheses.
  6. Acknowledge the temporal dimension: The "assembler not found" clue reveals a sequence-of-events issue that complicates diagnosis. This is the thinking of an experienced systems debugger. The assistant does not jump to conclusions, does not blame the hardware, and does not assume the previous fix is wrong. Instead, it treats the new evidence as an opportunity to learn something new about the system.

Conclusion

Message 1958 is a pivotal moment in a complex debugging session. It represents the shift from fixing a known bug (job ID collision) to discovering a deeper, more systematic issue in the GPU proving pipeline. The assistant's reasoning trace reveals the intellectual discipline required to debug distributed proving systems: the ability to recognize patterns in failure modes, formulate testable hypotheses, and plan a systematic investigation.

The message also illustrates a broader truth about production debugging: the most valuable fixes are often not the ones that patch a symptom, but the ones that reveal the true nature of the problem. By recognizing that the 100% failure rate points to a structural code path difference rather than a flaky component, the assistant sets the stage for finding the real root cause—which, as we will see in subsequent messages, turns out to be a subtle issue in how the cuzk engine handles concurrent partition assembly when all proofs share the same job ID prefix.

The debugging journey continues, but message 1958 is where the trajectory shifts from symptom-fixing to root-cause analysis. It is a masterclass in how to think about systematic failures in complex distributed systems.