The Diagnostic Read: How Two Shell Scripts Held the Key to a Systematic GPU Proving Failure

Introduction

In the middle of a high-stakes debugging session targeting a production Filecoin proving system, the assistant issued a pair of seemingly mundane read tool calls. Message [msg 1959] consists of nothing more than reading two files: benchmark.sh and run.sh from the /tmp/czk/docker/cuzk/ directory. On its surface, this is the simplest possible action—fetching file contents. Yet this message represents a critical inflection point in a much larger investigation. It is the moment where the assistant pivoted from treating a symptom (all ten PoRep partitions producing invalid proofs) to systematically understanding why the same binary could succeed under one invocation and fail under another. This article unpacks the reasoning, context, assumptions, and knowledge embedded in that single diagnostic act.

The Surface: What the Message Actually Shows

The message is brief and direct. The assistant invokes the read tool twice:

[assistant] [read] /tmp/czk/docker/cuzk/benchmark.sh
[assistant] [read] /tmp/czk/docker/cuzk/run.sh

The tool returns the contents of each file. benchmark.sh is described as a script that "starts cuzk-daemon, runs a warmup proof (waits for PCE extraction), then benchmarks N sequential PoRep proofs and reports timing." run.sh is described as a script to "Start cuzk daemon (and later curio)." The full file contents are truncated in the message output, but the headers and first few lines are enough to establish their purpose.

There are no tool results to act on in this same round—the assistant is still waiting for the read results to come back. The next message (which we don't see in the subject) will contain the full file contents and the assistant's analysis of them. This is a classic pattern in the opencode session: the assistant reads, then reasons, then acts. Message [msg 1959] is the "read" phase of that cycle.

The Deep Context: A Debugging Journey

To understand why this message matters, one must understand the debugging nightmare that preceded it. In the previous messages ([msg 1946] through [msg 1958]), the assistant had been wrestling with two major production bugs in the ProofShare system.

Bug 1: Deadlock from HTTP 429 retries. The TaskRequestProofs function in Curio's proofshare system was getting stuck because CreateWorkAsk would retry HTTP 429 (Too Many Requests) responses indefinitely. This blocked the poll loop from discovering matched work and inserting it into the proofshare_queue. The fix was to return a sentinel ErrTooManyRequests immediately on 429, allowing the poll loop to continue with progress-based exponential backoff.

Bug 2: Job ID collision causing partition proof mixing. This was the more insidious bug. The user reported in [msg 1957] that even after deploying the container-built cuzk binary (which passed benchmarks), all ten PoRep partitions were still producing invalid proofs. The assistant traced this to a job ID collision: proofshare challenges all target the same miner=1000, sector=1, so concurrent tasks sent identical job_id values to cuzk. The engine's partition assembler keyed on job_id, causing partition results from different proofs to mix—confirmed by a "partition 0 already inserted" panic. The fix added the harmony task ID to the RequestId, making it unique per invocation.

But then came the perplexing observation in [msg 1957]: the benchmark passed, but production proving failed. Every single partition was invalid. The assistant's reasoning in [msg 1958] shows the gears turning:

"This is very interesting. The logs show: ALL 10 partitions fail verification - not just some, ALL of them. The benchmark passes but run.sh + curio fails."

The assistant considers several hypotheses: a bug in the partitioned path, a bug in how num_circuits=1 is handled in the supraseal GPU code, a data corruption issue, or a fundamental difference between how benchmark.sh and run.sh invoke the proving pipeline.

Why This Message Matters: The Pivot from Symptom to Root Cause

Message [msg 1959] is the pivot point. Up until this moment, the assistant had been focused on fixing symptoms: the deadlock, the job ID collision, the self-check gating. But the observation that all ten partitions fail systematically (not intermittently) in the partitioned path, while the benchmark passes, suggests something deeper is wrong.

The assistant's reasoning in [msg 1958] reveals the key insight:

"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."

This is the moment the assistant realizes: the fix for the job ID collision might be necessary, but it's not sufficient. There is a separate, deeper issue causing all proofs to fail in the partitioned path. The assistant needs to understand the exact difference between the two invocation paths.

Reading benchmark.sh and run.sh is the first step in that investigation. The assistant needs to see:

  1. What flags and arguments each script passes to the cuzk daemon
  2. Whether partition_workers is set differently between the two
  3. Whether the benchmark uses a completely different proving mode (e.g., monolithic vs. partitioned)
  4. Whether there are environment variable differences that affect GPU behavior

Input Knowledge Required

To understand this message, one needs substantial context:

The architecture of the CuZK proving system. CuZK is a GPU-accelerated proving engine for Filecoin's Proof-of-Replication (PoRep) circuits. It supports multiple proving modes: monolithic (prove all partitions as a single batch), partitioned (prove each partition separately with multiple workers), and a hybrid mode. The partition_workers configuration controls how many GPU workers are spawned to handle partitions concurrently.

The ProofShare system. This is Curio's mechanism for distributing proof computation across a network of proving workers. The cuzk daemon runs as a service that accepts proof requests via HTTP, processes them on GPU, and returns results.

The benchmark vs. production distinction. benchmark.sh is designed for performance measurement—it runs a warmup proof (triggering PCE extraction), then benchmarks N sequential proofs. run.sh is designed for production operation—it starts the daemon and optionally launches Curio. These two scripts may pass different configuration parameters to the cuzk daemon, leading to different code paths.

The self-check mechanism. The assistant had previously added a self-check that verifies each assembled proof before accepting it. This is what produces the error message "PoRep proof self-check failed: assembled proof did not verify." The self-check is the diagnostic that catches the bad proofs, but it doesn't explain why they're bad.

Output Knowledge Created

By reading these two files, the assistant gains the raw material needed to compare the two execution paths. The output knowledge includes:

  1. The exact command-line arguments used by each script, which may reveal differences in proving mode configuration.
  2. The environment variables set by each script, which could affect GPU behavior, memory allocation, or circuit parameters.
  3. The workflow structure of each script—benchmark.sh runs proofs sequentially and measures timing, while run.sh starts a persistent daemon.
  4. Potential configuration differences such as partition_workers, num_circuits, or batch size settings that differ between the two paths. This knowledge is the foundation for the next phase of investigation: tracing through the cuzk Rust code to understand exactly how the partitioned path differs from the monolithic path, and why num_circuits=1 in the partitioned path might produce systematically invalid proofs.

The Thinking Process Visible in the Reasoning

The assistant's reasoning in [msg 1958] (immediately before the subject message) is a masterclass in systematic debugging. Let me quote key passages:

"The fact that benchmark passes but production fails is very telling."

This observation frames the entire investigation. The assistant recognizes that a binary cannot be "partially correct"—if it produces correct proofs under one invocation and incorrect proofs under another, the difference must be in the invocation parameters, not the binary itself.

"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."

This is hypothesis generation at its finest. The assistant enumerates four distinct categories of possible root causes, each pointing to a different layer of the system: the pipeline orchestration (1), the proof assembly logic (2), the GPU kernel code (3), and the data pipeline (4).

"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."

This is the most specific hypothesis. The assistant has identified a concrete parameter difference (num_circuits=1 vs num_circuits=10) that could explain the systematic failure. If the GPU code has a bug that only manifests when num_circuits=1 (perhaps an edge case in memory allocation, kernel launch parameters, or result aggregation), that would explain why all ten partitions fail identically.

Assumptions and Potential Pitfalls

The assistant makes several assumptions in this message and the surrounding reasoning:

Assumption 1: The benchmark and production paths are genuinely different. This is a safe assumption given the evidence, but it's worth verifying. The assistant assumes that benchmark.sh uses a different proving mode than run.sh + Curio. If they turn out to use identical parameters, the investigation would need to pivot to other explanations (e.g., timing-dependent race conditions, memory pressure differences).

Assumption 2: The binary is correct. The assistant assumes that since the binary passes the benchmark, it is fundamentally sound. But this could be wrong if the benchmark exercises a different code path that happens to avoid the bug. The binary could still have a bug that only manifests under specific conditions.

Assumption 3: The difference is in the proving pipeline, not the data. The assistant assumes that the C1 outputs fed to the GPU are correct and that the issue is in how they're processed. If the C1 outputs themselves are corrupted (perhaps due to a different serialization path in the production pipeline), the investigation would need to go deeper.

Potential mistake: Overlooking environment differences. The assistant focuses on script arguments and configuration, but environment variables (like CUDA_VISIBLE_DEVICES, FIL_PROOFS_USE_GPU_TREE_BUILDER, or memory limits) could also differ between the two paths. Reading the scripts is the right first step, but a thorough comparison would also need to check the environment setup in the Docker container and the Curio process.

Conclusion

Message [msg 1959] is a deceptively simple act of reading two files. But in the context of the broader debugging session, it represents a critical methodological pivot: from fixing symptoms to understanding root causes. The assistant recognizes that a binary that passes benchmarks but fails in production points to a configuration or code-path difference, not a fundamental correctness issue. By reading benchmark.sh and run.sh, the assistant gathers the raw data needed to compare the two execution paths and identify the exact parameter that triggers the bug.

This message exemplifies the most important skill in debugging complex distributed systems: knowing what to look at next. When faced with a baffling 100% failure rate in production, the assistant doesn't guess or apply random fixes. It goes back to first principles: find the difference between the working case and the failing case. The two shell scripts are the most direct source of that difference.

The investigation that follows this message will trace through the cuzk Rust code, examine the partition_workers configuration, and ultimately identify the root cause of the systematic proving failure. But message [msg 1959] is where that investigation begins—with a simple, deliberate act of reading.