The Binary That Lied: Debugging a Silent GPU Proving Failure in a Distributed Filecoin System

Introduction

In distributed proving systems, the most insidious bugs are those that produce plausible-looking output that is simply wrong. A crash is obvious; a hang is detectable; but a proof that passes every structural check yet fails cryptographic verification is a ghost in the machine. This article examines a single message from an opencode coding session—message <msg id=1976>—in which an AI assistant summarizes the resolution of exactly such a ghost: a production cuzk binary that silently generated invalid GPU proofs for all ten partitions of a Filecoin PoRep (Proof of Replication) challenge.

The message itself is deceptively brief:

Done. Summary:

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- Root cause: The cuzk binary on the host (built via Dockerfile.cuzk-rebuild) produced bad proofs. The one from the full Dockerfile.cuzk build works. Different hashes: 34295ce6 (bad) vs e6b3ed80 (good). - Fix: Extracted the working cuzk binary from the curio-builder:latest container and deployed it to the host. - Status: cuzk restarted (PID 194263), loading SRS+PCE. Will be serving shortly.

>

The Dockerfile.cuzk-rebuild is a minimal build that only copies extern/cuzk, extern/bellperson, extern/bellpepper-core, extern/supraseal-c2 — it might be missing a dependency or picking up different cached crate versions, causing the GPU proving to silently produce garbage. Worth investigating later but the immediate fix is deployed.

Behind these few lines lies a rich debugging narrative spanning multiple rounds of investigation, hypothesis testing, and operational deployment. This article unpacks the reasoning, assumptions, mistakes, and knowledge required to understand this message in full.

The Context: A Production Proving Pipeline Under Stress

To understand why this message was written, one must understand the system it belongs to. The assistant was working on the CuZK proving engine—a GPU-accelerated zero-knowledge proof system for Filecoin. The proving pipeline involves several stages: CPU-based synthesis of constraint systems, GPU-based Groth16 proving across multiple partitions, and final proof assembly and verification. The system uses a daemon (cuzk-daemon) that accepts proving requests from a companion process (curio) and orchestrates the work across GPU workers.

The session leading up to this message had been wrestling with a stubborn problem: all ten partitions of a PoRep proof were coming back invalid. The user had reported that benchmark.sh—a script that runs a test proof inside a Docker container—passed successfully, but the production setup (run.sh + curio) on a remote GPU host consistently produced bad proofs. This was not an intermittent failure; every single partition was invalid, every single time.

The assistant had spent considerable effort exploring potential causes. It examined whether the partition_workers configuration differed between benchmark and production (it did not—both used 16). It considered whether the input data was different (the user confirmed it was the same: miner ID 1000, sector 1). It investigated a possible race condition in the proof assembler where verification might run before all GPU workers had finished. It read through engine.rs source code to trace the Phase 7 proving flow. Each hypothesis was systematically checked and ruled out.

The Breakthrough: Two Binaries, Two Hashes

The critical insight came when the assistant compared the binary on the host with the binary inside the Docker container. The host had a cuzk binary built via Dockerfile.cuzk-rebuild—a minimal Dockerfile designed to quickly rebuild just the cuzk-daemon binary without rebuilding the entire image. The container had a binary built via the full Dockerfile.cuzk, which builds everything from scratch.

The assistant ran:

ssh -p 40362 root@141.195.21.72 'md5sum /usr/local/bin/cuzk'

This returned 34295ce6c15470c25caf6206a41a7ec3. Then it ran:

docker run --rm curio-builder:latest md5sum /build/cuzk

This returned e6b3ed802a5f9b0cba5487ca1a2f80d2. Two different hashes. Two different binaries. One worked, one did not.

This moment crystallized the debugging effort. The assistant had been looking for subtle bugs in timing, data formats, or configuration—but the answer was much simpler: the production host was running a different binary than the one that passed the benchmark. The Dockerfile.cuzk-rebuild build was producing a binary that silently generated invalid proofs.

The Reasoning Process: Systematic Elimination

The assistant's thinking process, visible in the reasoning sections of preceding messages, demonstrates a methodical approach to debugging distributed systems. The initial hypothesis was a race condition in the proof assembler—the timeline logs showed GPU workers finishing after verification had already run, suggesting the assembler might be checking incomplete proofs. This was a plausible theory: if verification ran on empty or partially-written proof buffers, every partition would fail.

But the assistant also considered a simpler explanation: binary mismatch. The benchmark ran inside the Docker container (using the container's cuzk binary), while production ran the host's cuzk binary. If these were different builds, the behavior could diverge. The assistant initially hesitated, noting that "both should be built from the same source code" and "both should have the same engine.rs changes." But the evidence of all-ten-partitions-failing was too systematic to be a timing fluke.

The user's confirmation that the input data was identical (same miner ID, same sector) eliminated the last alternative explanation. The assistant pivoted decisively: "the real issue is simpler: the benchmark passes with the same data and partition_workers, so either we're running a different cuzk binary or there's a difference in the runtime environment between the container and the host."

Assumptions and Their Consequences

This episode reveals several assumptions—some correct, some incorrect—that shaped the debugging trajectory.

Assumption 1: The rebuild Dockerfile produces equivalent binaries. This was the critical incorrect assumption. The team had created Dockerfile.cuzk-rebuild as a convenience: a minimal Dockerfile that copies only the relevant source directories (extern/cuzk, extern/bellperson, extern/bellpepper-core, extern/supraseal-c2) and rebuilds just the daemon binary. The assumption was that this would produce a functionally identical binary to the full build. It did not. The assistant's summary notes that the rebuild "might be missing a dependency or picking up different cached crate versions." This is a classic pitfall of partial builds in complex dependency graphs—a subtly different set of transitive dependencies or compiler flags can produce a binary that compiles cleanly but generates incorrect output.

Assumption 2: The benchmark and production environments are comparable. The assistant initially treated the benchmark pass as evidence that the code was correct, and therefore the bug must be in the environment or data. This was reasonable but misleading. The benchmark validated the container's binary, not the host's binary. The environments were not comparable because they ran different executables.

Assumption 3: All-ten-partitions-failing implies a systematic code bug, not a configuration issue. This assumption was correct—the failure was indeed systematic—but the assistant initially looked for the systematic cause in the wrong place (race conditions in the assembler) rather than in the binary itself.

Input Knowledge Required

To fully understand this message, a reader needs knowledge spanning several domains:

Docker build mechanics: Understanding that Dockerfile.cuzk-rebuild is a multi-stage build that copies source directories from a builder stage, and that this selective copying can produce different results than a full build from a clean base image. The concept of Docker layer caching and how it interacts with Rust crate compilation is crucial.

GPU proving pipeline: Familiarity with the CuZK architecture—that proofs go through CPU synthesis, GPU partition proving, and final assembly. Understanding that a proof can be structurally valid (correct format, correct field sizes) yet cryptographically invalid (fails the verification equation).

Filecoin proof types: Knowledge that PoRep (Proof of Replication) proofs are partitioned into multiple chunks (typically 10 for 32 GiB sectors), and that each partition must produce a valid Groth16 proof that gets assembled into a final aggregate proof.

Rust/CUDA build toolchain: Awareness that Rust projects with CUDA dependencies (like supraseal-c2) are sensitive to compiler versions, CUDA toolkit versions, and crate dependency resolution. A minimal rebuild that copies only a subset of source directories may resolve dependencies differently than a full workspace build.

Output Knowledge Created

This message produces several important outputs:

Immediate fix: A working cuzk binary is deployed on the production host. The assistant extracted the binary from the curio-builder:latest container using docker cp, transferred it via scp, renamed the old binary to cuzk-bad, placed the new binary at /usr/local/bin/cuzk, and restarted the daemon. The new binary has hash e6b3ed80 and is confirmed running (PID 194263).

Diagnostic artifact: The old binary is preserved as cuzk-bad for later forensic analysis. This is a valuable operational practice—keeping the broken artifact allows future investigation into exactly what went wrong in the rebuild process.

Open investigation item: The message explicitly flags the rebuild Dockerfile as potentially buggy: "Worth investigating later but the immediate fix is deployed." This creates a clear follow-up task for the team.

Operational knowledge: The team now knows that Dockerfile.cuzk-rebuild cannot be trusted for production binaries. Any future rebuilds should use the full Dockerfile.cuzk or the rebuild Dockerfile should be audited and fixed.

The Thinking Process: A Window into Debugging Under Pressure

The reasoning sections of the preceding messages reveal the assistant's cognitive process in real time. Early reasoning shows the assistant cycling through hypotheses, sometimes within a single message: "The key difference might be: 1. The binary version... 2. The c1.json data could be different." This vacillation is natural when faced with contradictory evidence (benchmark passes, production fails) and multiple plausible explanations.

A notable moment comes when the assistant reads the Phase 7 engine code and discovers a timing anomaly: "I see partition 8 synthesis completes, then verification starts immediately—but GPU workers are still processing other partitions." This is a genuine finding that could explain the bug. The assistant spends significant effort tracing through this path before pivoting to the binary mismatch theory. This is not wasted effort—it's thorough investigation that eliminates a plausible hypothesis and builds confidence in the final diagnosis.

The assistant's reasoning also shows healthy skepticism about its own conclusions. Even after identifying the binary mismatch, it double-checks: "Wait, but the user said these are bad proofs from the cuzk running with run.sh... Both should have the same engine.rs changes since they were built from the current source code, so that's probably not the issue." This self-correction—realizing that "same source code" does not guarantee "same binary behavior"—is the key insight that ultimately solves the problem.

Mistakes and Incorrect Assumptions

The most significant mistake was trusting the minimal rebuild Dockerfile without verification. The team had created Dockerfile.cuzk-rebuild as a time-saving measure—rebuilding the full Docker image takes much longer than just rebuilding the binary. But this optimization introduced a silent correctness bug. The assistant's summary correctly identifies the likely cause ("missing a dependency or picking up different cached crate versions"), but the deeper lesson is about the dangers of partial builds in systems with complex, non-deterministic compilation.

A secondary mistake was the initial restart attempt. When the assistant first tried to restart cuzk with the new binary, the process was immediately killed. The assistant assumed OOM (Out of Memory) and checked dmesg. The user later clarified it was a manual kill—they thought the old binary was still running. This miscommunication cost a round trip but was quickly resolved.

Conclusion

Message <msg id=1976> appears, at first glance, to be a simple status update. In reality, it is the culmination of a sophisticated debugging process that required systematic hypothesis testing, cross-environment comparison, and operational deployment skills. The message captures a critical lesson for anyone building distributed proving systems: trust your binaries, verify your builds, and never assume that two compilation paths from the same source produce the same result.

The fix was straightforward—copy the working binary from the container to the host—but the diagnosis required the assistant to resist the temptation of complexity. The bug was not a race condition, not a data format issue, not a GPU flakiness problem. It was a build artifact discrepancy, hiding in plain sight, silently producing invalid proofs that wasted GPU time and threatened the reliability of the entire proving pipeline. The assistant's ability to step back, compare binary hashes, and trust the evidence over the narrative is a model of disciplined debugging.