The Critical Read: Tracing GPU Selection Through C++ Source in a Multi-GPU Debugging Session

In the midst of a high-stakes debugging session, a single read tool call to a C++ header file represents a pivotal moment of discovery. Message [msg 313] in this opencode conversation is deceptively simple on its surface: the assistant reads lines 319–329 of /tmp/czk/extern/supraseal/deps/sppark/util/gpu_t.cuh, a utility header from the Supranational sppark library. The content shown is a mundane C++ template class called dev_ptr_t that manages temporary device pointer allocations. Yet this read is the culmination of a systematic forensic investigation into why PoRep (Proof of Replication) partitioned proofs are failing catastrophically on a remote multi-GPU test host, while working perfectly on a local single-GPU machine.

The Debugging Context: A 100% Failure Rate

To understand why this message was written, we must step back into the investigation that led to it. The assistant had been implementing Pre-Compiled Constraint Evaluator (PCE) extraction for all proof types in the CuZK proving engine—a significant optimization that pre-computes constraint evaluations. After fixing a WindowPoSt crash caused by a mismatch in constraint system extensibility (see [msg 288] onward), the assistant deployed the changes to a remote test host at 10.1.16.218. But something went terribly wrong: every single PoRep partitioned proof was invalid, with 0 out of 10 partitions valid.

The assistant's first hypothesis was that the recently modified WitnessCS::new() and RecordingCS::new() constructors—part of the WindowPoSt fix—had inadvertently broken the PoRep path. To test this, the assistant disabled PCE via the CUZK_DISABLE_PCE=1 environment variable and restarted the service. Even with PCE disabled, proofs continued to fail at the same 100% rate. This conclusively ruled out the PCE changes as the cause, forcing the investigation in a new direction.

The Multi-GPU Hypothesis

The critical clue was environmental: the remote host has two NVIDIA RTX 4000 Ada GPUs, while the local development machine has a single RTX 5070 Ti GPU. The assistant confirmed that the partitioned pipeline works correctly on the local single-GPU machine, then pivoted to investigate what could go wrong specifically in a multi-GPU configuration.

This led to examining the GPU worker architecture. The CuZK engine creates multiple GPU worker tasks per device—configured as gpu_workers_per_device = 2 with 2 GPUs, yielding 4 GPU workers total. Each worker is assigned to a specific GPU index (workers 0 and 1 on GPU 0, workers 2 and 3 on GPU 1). The Rust engine uses a per-GPU mutex system to serialize CUDA kernel execution: workers on the same GPU share a mutex, preventing concurrent access to device memory.

The mechanism for directing a worker to the correct GPU relies on std::env::set_var("CUDA_VISIBLE_DEVICES", gpu_str), called before each CUDA operation. This environment variable is supposed to tell the CUDA runtime which physical GPU to use. But the assistant had a growing suspicion that this mechanism was fundamentally broken.

Tracing the Code Path: From Rust to C++

The investigation then traced the code path from the Rust engine layer into the C++ supraseal-c2 library. The assistant examined prove_start and prove_from_assignments in supraseal.rs ([msg 293]), which call into the C++ functions start_groth16_proof and generate_groth16_proof. These C++ functions, defined in groth16_cuda.cu, are responsible for the actual GPU computation.

The critical question was: how do these C++ functions determine which GPU to use? The assistant searched for CUDA_VISIBLE_DEVICES, cudaSetDevice, select_gpu, and ngpus across the codebase. The search revealed that select_gpu and ngpus are declared in gpu_t.cuh (line 21-22, seen in [msg 311]) and implemented in all_gpus.cpp.

The Read That Changed Everything

Message [msg 313] is the assistant reading deeper into gpu_t.cuh, scrolling past the declarations it had already seen to examine the utility infrastructure. The specific content—the dev_ptr_t template class—is not where the smoking gun lies. But this read is part of a methodical strategy: the assistant is consuming the entire file to build a complete mental model of the GPU abstraction layer before moving to the implementation file.

The dev_ptr_t class itself is a RAII wrapper for temporary CUDA device allocations. It handles the alignment to WARP_SZ boundaries and calls cudaMalloc with proper error checking via CUDA_OK. While this particular snippet doesn't reveal the GPU selection mechanism, it demonstrates the assistant's thoroughness. Rather than jumping to conclusions based on partial information, the assistant is reading the full source code of every relevant file.

This approach reflects a key principle of debugging complex systems: you must understand the infrastructure before you can diagnose the failure. The dev_ptr_t class, the gpu_t class, the thread pool, the memory pool threshold management—all of these components interact in ways that could contribute to the failure. By reading the complete file, the assistant ensures no hidden assumptions or subtle interactions are missed.

The Discovery That Followed

Immediately after message [msg 313], the assistant located the implementation of select_gpu and ngpus in all_gpus.cpp ([msg 314], [msg 315]). The critical revelation came in [msg 316]: the gpus_t singleton is constructed once at static initialization time, at which point it calls cudaGetDeviceCount() and enumerates available GPUs. This is where CUDA_VISIBLE_DEVICES is read—only once, before any Rust code runs. The std::env::set_var() calls from Rust have no effect because the CUDA runtime already initialized with all GPUs visible.

Furthermore, inside generate_groth16_proofs_start_c, the code computes n_gpus = std::min(ngpus(), num_circuits). With num_circuits=1 (the partitioned proof case, where each partition is proved separately) and 2 physical GPUs, n_gpus = 1. This means only one GPU thread runs (tid=0), which calls select_gpu(0)—always selecting GPU 0 regardless of which Rust worker picked up the job.

This is the root cause: all workers target the same physical GPU 0, but the Rust engine creates separate mutexes per GPU. Workers assigned to "GPU 1" use a different mutex than workers on "GPU 0"—yet all of them actually target GPU 0. This allows concurrent CUDA kernel execution without mutual exclusion, causing data races on device memory and producing invalid proofs.

Input Knowledge Required

To understand message [msg 313], the reader needs substantial context. First, knowledge that the CuZK proving engine uses a partitioned proof pipeline where each partition is proved independently via GPU computation. Second, familiarity with the supraseal-c2 library and its relationship to sppark, the Supranational GPU acceleration library. Third, understanding of how CUDA_VISIBLE_DEVICES interacts with the CUDA runtime—specifically that it's read at driver initialization time, not dynamically. Fourth, awareness of the Rust async runtime and how spawn_blocking tasks interact with process-wide environment variables.

The reader also needs to follow the thread of the investigation: the assistant had already examined remote service logs, confirmed the failure was not PCE-related, tested locally with success, and traced the GPU selection code path from Rust into C++. Message [msg 313] is the point where the assistant is deep in the C++ infrastructure code, methodically reading every relevant source file.

Output Knowledge Created

This message, combined with the reads that follow, produces a definitive understanding of the bug. The output knowledge is: the CUDA_VISIBLE_DEVICES-based GPU selection mechanism is fundamentally broken in this architecture. The C++ code reads the environment variable once at static initialization time, making Rust's set_var calls ineffective. Combined with the num_circuits=1 logic that always selects GPU 0, all workers target the same physical GPU regardless of their assigned index. The per-GPU mutex system, which assumes workers on different GPUs use different mutexes, fails to serialize access because all workers actually compete for the same GPU.

The fix, which the assistant subsequently implements, is to use 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, a single mutex correctly enforces mutual exclusion across all workers.

Assumptions and Their Pitfalls

The debugging process reveals several incorrect assumptions. The initial assumption was that std::env::set_var("CUDA_VISIBLE_DEVICES", ...) effectively controls which GPU a CUDA operation targets. This is a reasonable assumption—it's a documented environment variable—but it fails because the C++ code initializes the GPU enumeration before the Rust code runs. The assistant also initially assumed the PCE changes were responsible for the failure, a natural hypothesis given that PCE was the most recent modification. Testing this hypothesis by disabling PCE was a textbook debugging technique: isolate variables to confirm or eliminate causes.

The assistant's thinking process shows a disciplined approach: rule out the obvious cause first, then look for environmental differences between success and failure cases, then trace the code path from the high-level Rust layer down to the low-level C++ implementation. Each step narrows the search space and builds toward the root cause.

Conclusion

Message [msg 313] appears unremarkable—a simple file read showing a utility class for device memory allocation. But in the context of the investigation, it represents the assistant's commitment to thoroughness. Rather than stopping at the declarations of select_gpu and ngpus seen in earlier reads, the assistant reads the entire file to build complete understanding. This methodological rigor is what enables the discovery that follows: the CUDA_VISIBLE_DEVICES mechanism is a no-op, all workers target GPU 0, and the per-GPU mutex system fails to protect against concurrent access. The fix—a single shared mutex—directly addresses the root cause and restores correct proving on multi-GPU systems.