The Config File That Held the Clue: Tracing a Multi-GPU Race Condition Through a Single cat Command

The Message

In the middle of an intensive debugging session spanning remote and local machines, the assistant issued a disarmingly simple command:

cat /tmp/cuzk-p12-pw16.toml

This single line, message [msg 371] in the conversation, read a configuration file from the local development machine. The file contained:

[daemon]
listen = "0.0.0.0:9820"
[srs]
param_cache = "/data/zk/params"
preload = ["porep-32g"]
[synthesis]
partition_workers = 16
[gpus]
gpu_workers_per_device = 2
gpu_threads = 32

On its surface, this is a mundane operation — a developer inspecting a config file. But in the context of the debugging odyssey that preceded it, this cat command was the fulcrum on which the entire investigation turned. It was the moment the assistant stopped looking at code changes and started looking at environment differences, a pivot that would ultimately reveal a subtle GPU race condition that had been silently corrupting every single proof on a multi-GPU remote host.

The Context: A Debugging Crisis

To understand why this message matters, we must reconstruct the state of the investigation at the moment it was issued. The assistant had been chasing a baffling bug: PoRep (Proof of Replication) partitioned proofs were failing catastrophically on a remote test host (10.1.16.218), with a 100% failure rate — zero out of ten partitions valid in every proof attempt. Yet the exact same code, running the exact same partitioned pipeline, worked flawlessly on the local development machine.

The debugging journey had already taken several turns. Initially, the assistant suspected the recently implemented Pre-Compiled Constraint Evaluator (PCE) extraction, which had been extended to support all proof types including WindowPoSt and SnapDeals. The WindowPoSt PCE fix had required changes to WitnessCS::new() and RecordingCS::new() — specifically, changing their initial input count from 1 to 0, with explicit allocation later. Perhaps these changes had created a mismatch between the extracted PCE and the witness generation?

To test this, the assistant set CUZK_DISABLE_PCE=1 on the remote host and restarted the service. If PCE were the culprit, disabling it should fix the problem. But the proofs continued to fail at the same 100% rate. This was a pivotal negative result: it ruled out the PCE changes entirely and forced a re-evaluation of what could possibly be different between the two environments.

The assistant then checked the git history to see what other changes had been made. The diff showed only the WitnessCS::new() change in bellperson — but WitnessCS is only used in the PCE path, which was already ruled out. This left a narrowing set of possibilities: either the old binary on the remote was also broken and nobody had noticed, or there was something specific to the remote machine's hardware or configuration.

The Pivot: From Code to Configuration

This is where the assistant's thinking process took a critical turn. The user had confirmed that the partitioned pipeline worked locally on the development machine — the same machine where the assistant was now running commands. The assistant had just verified this by running cuzk-bench single --type porep --c1 /data/32gbench/c1.json against the local daemon, which returned status: COMPLETED with a valid 1920-byte proof.

With the code path ruled out and the hardware difference (single GPU locally vs. dual GPU remotely) emerging as the only remaining variable, the assistant needed to understand exactly how the local daemon was configured. The cat /tmp/cuzk-p12-pw16.toml command was the natural next step — a reconnaissance mission to document the local configuration so it could be compared against the remote deployment.

The configuration revealed several important parameters:

Assumptions Embedded in the Investigation

At this point in the conversation, the assistant was operating under several assumptions that shaped the interpretation of this config file:

  1. The code is correct: Since the same binary worked locally, the Rust and C++ code paths must be functionally correct. The bug must be environmental.
  2. The configuration is comparable: The local config file provides a baseline that should be similar to the remote config. Any differences could point to the root cause.
  3. GPU worker isolation works: The assistant implicitly assumed that the gpu_workers_per_device setting, combined with the mutex-based worker assignment in the Rust engine, would properly isolate GPU access between workers assigned to different devices.
  4. CUDA_VISIBLE_DEVICES is effective: The code uses std::env::set_var("CUDA_VISIBLE_DEVICES", ...) to select which GPU a worker should use. The assistant had not yet questioned whether this approach actually works in practice. These assumptions were reasonable, but as the investigation would soon reveal, the last two were incorrect — and that's where the bug lived.

The Knowledge Required to Understand This Message

Reading this config file requires understanding the architecture of the CuZK proving engine. The system is a distributed GPU proving pipeline that:

  1. Accepts proof jobs over a Unix socket or TCP (the listen address).
  2. Synthesizes circuits using CPU workers (partition_workers), building constraint systems for each partition of a proof.
  3. Proves on GPUs using a pool of GPU worker threads, with multiple workers per device to overlap computation and data transfer.
  4. Caches SRS parameters (the Structured Reference String for the Groth16 proving system) at a configurable path. The gpu_workers_per_device = 2 setting is particularly important. It's designed to allow one worker to be proving on the GPU while another is preparing the next batch of data, maximizing GPU utilization. But this design assumes that workers assigned to different GPU devices actually target different physical GPUs.

The Mistake That Wasn't Yet Visible

The critical mistake — the incorrect assumption about CUDA_VISIBLE_DEVICES — was not apparent from reading this config file. The assistant would only discover it after deeper investigation into the C++ code. The C++ GPU code in sppark's gpu_t.cuh reads CUDA_VISIBLE_DEVICES once at static initialization time, before any Rust code runs. When the Rust engine later calls std::env::set_var() to assign a worker to a specific GPU, the CUDA runtime has already cached the original value and ignores the change.

This means that inside generate_groth16_proofs_start_c, with num_circuits=1 (the partitioned proof case where each partition is proved independently), the code always selects GPU 0 via select_gpu(0) — regardless of which Rust worker picks up the job. All workers, whether the Rust engine thinks they're assigned to "GPU 0" or "GPU 1", actually target the same physical GPU 0. The separate mutexes per GPU in the Rust engine provide no protection because they're guarding different logical devices that all map to the same physical hardware. The result is concurrent CUDA kernel execution without mutual exclusion, causing data races on device memory and producing invalid proofs.

The Output Knowledge Created

This message produced a concrete artifact: a documented baseline configuration for the working local environment. This baseline would later be used to:

  1. Confirm the remote configuration: By comparing against the remote host's config, the assistant could verify that the same settings were in use.
  2. Rule out configuration mismatch: Since both environments used similar configurations, the bug had to be in the interaction between the configuration and the hardware — specifically, the multi-GPU scenario.
  3. Inform the fix: The fix would need to change how GPU workers are synchronized. Instead of separate mutexes per GPU device (which don't work when all workers target the same physical GPU), the engine needs a single shared mutex for all workers when num_circuits=1, since the C++ code internally serializes all GPU work to the same physical GPU regardless of the logical device assignment.

The Thinking Process Visible in the Reasoning

The assistant's reasoning at this point shows a classic debugging pattern: systematic elimination of variables. The sequence was:

  1. Hypothesis: PCE changes broke something. Test: Disable PCE. Result: Still broken. → Ruled out.
  2. Hypothesis: Code changes to bellperson broke something. Analysis: Only WitnessCS::new() changed, which is PCE-only. With PCE disabled, this code path isn't used. → Ruled out.
  3. Hypothesis: The remote build is different. Check: Git log shows same commits. → Unlikely.
  4. Hypothesis: The environment/hardware is different. Observation: Remote has 2 GPUs, local has 1. → This is the remaining variable.
  5. Action: Document the local configuration to enable comparison with the remote setup. → This is the cat command. This is textbook scientific debugging: isolate variables, test each one, and focus on what remains. The config file read was not a random action — it was the logical next step in a narrowing funnel of possibilities.

Conclusion

The cat /tmp/cuzk-p12-pw16.toml command in message [msg 371] appears trivial but represents a critical juncture in the debugging process. It marks the moment when the investigation shifted from examining code changes to examining environmental differences — a shift that would lead directly to the root cause. The configuration it revealed, combined with the knowledge that the local machine had one GPU while the remote had two, set the stage for the discovery of the CUDA_VISIBLE_DEVICES race condition.

In the end, the fix was elegantly simple: use a single shared mutex for all GPU workers when num_circuits=1, since the underlying C++ code serializes all work to the same physical GPU anyway. But arriving at that fix required tracing a path through PCE extraction, bellperson diffs, service logs, and ultimately, a humble configuration file that held the key to understanding why two seemingly identical deployments behaved so differently.