Chunk 2.0

In this chunk, I investigated why PoRep partitioned proofs are failing on the remote test host (10.1.16.218) despite working correctly on the local dev machine. I started by checking the remote service logs, which revealed a 100% failure rate — every single proof was invalid, with 0/10 valid partitions. I initially suspected the PCE path since we had just modified `WitnessCS::new()` and `RecordingCS::new()` as part of the WindowPoSt fix. To test this, I disabled PCE via `CUZK_DISABLE_PCE=1` and restarted the service. Even with PCE disabled, proofs continued to fail at the same 100% rate, conclusively ruling out the PCE changes as the cause. I then ran the same partitioned pipeline locally (single RTX 5070 Ti GPU) and confirmed it works correctly — the proof completed successfully with status COMPLETED. The critical difference between the two environments is that the remote host has 2 GPUs (RTX 4000 Ada) while the local machine has 1 GPU. This led me to discover the root cause: the `CUDA_VISIBLE_DEVICES` environment variable approach for GPU selection is fundamentally broken. The C++ code (sppark's `gpu_t.cuh`) reads `CUDA_VISIBLE_DEVICES` once at static initialization time, so `std::env::set_var()` calls from Rust have no effect on the CUDA runtime. Inside `generate_groth16_proofs_start_c`, with `num_circuits=1` (single partition), the code always selects GPU 0 via `select_gpu(0)` regardless of which Rust worker picks up the job. However, the Rust engine creates separate mutexes per GPU, so workers assigned to "GPU 1" use a different mutex than workers on "GPU 0" — yet all of them actually target the same physical GPU 0, allowing concurrent CUDA kernel execution without mutual exclusion and causing data races on device memory. The fix is to use a single shared mutex for all workers when `num_circuits=1` (the partitioned proof case), since the C++ code internally serializes all GPU work to the same physical GPU. I applied an edit to `engine.rs` to implement this change, which needs to be built, deployed, and tested on the remote host.

The Multi-GPU Race Condition: Tracing a 100% Proof Failure Rate Through PCE Red Herrings, Local Validation, and a CUDA_VISIBLE_DEVICES Trap 2737 words

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