The Pivot from Hack to Architecture: Threading GPU Awareness Through the Proving Stack

In the middle of a deep debugging session on a multi-GPU zero-knowledge proving system, a single message marks the pivot from a quick workaround to a proper architectural fix. Message 460 in the conversation is deceptively brief — just a one-line description of an edit and a confirmation that it succeeded:

Now the critical part — change the GPU selection logic inside the function. Replace the n_gpus = min(ngpus(), num_circuits) and the select_gpu(tid) call: [edit] /tmp/czk/extern/supraseal-c2/cuda/groth16_cuda.cu Edit applied successfully.

This short message is the fulcrum of the entire segment. It represents the moment when the assistant, prompted by the user's sharp critique, abandoned a stopgap measure and implemented the core of a proper multi-GPU fix. To understand why this message matters, one must understand the chain of reasoning that led to it, the architectural assumptions it overturned, and the cascade of changes it enabled.

The Context: A Lazy Hack Exposed

The story begins with a partitioned proof workload — specifically PoRep (Proof of Replication) proofs — that was failing on a multi-GPU system. The assistant had diagnosed that the C++ GPU proving code, groth16_cuda.cu, always routed single-circuit proofs to GPU 0, regardless of which Rust worker submitted them. On a two-GPU system, this meant that workers assigned to GPU 1 would still contend for GPU 0's resources, causing data races and crashes.

The initial "fix" was a shared mutex: a single std::mutex that serialized all partition proofs onto GPU 0, effectively wasting the second GPU entirely. It was a band-aid that stopped the crashes but destroyed load balancing. The assistant deployed this fix and moved on.

Then a new workload appeared: SnapDeals, consisting of 16 identical partitions, running on a host with a 20 GB RTX 4000 Ada GPU (p-dev-ngw-1). It OOM'd — out-of-memory — because two workers were still entering the GPU code simultaneously on the same device, and a single SnapDeals partition's VRAM budget was too large to allow concurrent kernel execution. The shared mutex had not actually been deployed on that host, but even if it had been, the fix was fundamentally wrong: it would have serialized all 16 partitions onto GPU 0, wasting GPU 1 entirely and doubling the total proving time.

The user's response in message 444 cut to the heart of the matter:

Why is GPU prove for the second GPU not running.. on the second GPU? That's the whole point. CuZK is meant to be a fairly sophisticated proving engine, so it must support multiple GPUs, gpu memory management, and GPU workers are supposed to be interlocking two phases of data transfer to gpu vs compute. Isn't the shared lock just a lazy hack?

This was the turning point. The assistant immediately conceded: "You're right. The shared mutex is a hack that serializes everything to GPU 0 and wastes the second GPU entirely."

Tracing the Root Cause

The assistant then re-examined the C++ code to find the actual root cause. The critical line was at position 483 in groth16_cuda.cu:

size_t n_gpus = std::min(ngpus(), num_circuits);

For partitioned proofs, num_circuits is always 1 (each partition is proven as a single circuit). Therefore n_gpus evaluates to 1, regardless of how many physical GPUs are available. The subsequent loop at line 883 spawns only one thread with tid=0, which calls select_gpu(0) — always GPU 0. The Rust engine's careful assignment of workers to different GPUs via CUDA_VISIBLE_DEVICES was completely ignored by the C++ layer.

The proper fix was conceptually simple but mechanically involved: thread a gpu_index parameter through the entire call chain so that the C++ code uses the GPU assigned by the Rust engine instead of always defaulting to GPU 0. The assistant laid out the plan in message 452:

The fix is clear: thread a gpu_index parameter through the entire call chain so the C++ code uses select_gpu(gpu_index) instead of select_gpu(0) for single-circuit proofs. Then workers assigned to GPU 1 actually prove on GPU 1, per-GPU mutexes work correctly, and the 16 SnapDeals partitions naturally load-balance across both GPUs.

The Architecture of the Fix

The fix required changes across five layers of the software stack, and the assistant worked bottom-up:

C++ (groth16_cuda.cu): Add int gpu_index parameter to both generate_groth16_proofs_start_c and generate_groth16_proofs_c. When gpu_index >= 0, force n_gpus = 1 and use select_gpu(gpu_index) instead of select_gpu(tid). When gpu_index == -1, fall back to the original auto-detection behavior for non-engine callers.
Rust FFI (supraseal-c2/src/lib.rs): Update the extern "C" declarations and the public wrapper functions (start_groth16_proof, generate_groth16_proof, generate_groth16_proofs) to accept and forward the gpu_index parameter.
Bellperson prover (supraseal.rs): Update prove_start and prove_from_assignments to accept and pass through the gpu_index.
Pipeline layer (pipeline.rs): Update gpu_prove and gpu_prove_start to accept and forward the parameter.
Engine (engine.rs): Revert the shared mutex hack, restore per-GPU mutexes, and pass the assigned GPU ordinal from each worker. Messages 457-459 handled the C++ plumbing: adding the parameter to the function signature, the forward declaration, and the sync wrapper. Message 460 is where the assistant makes the actual behavioral change — replacing the n_gpus = min(ngpus(), num_circuits) logic and the select_gpu(tid) call with the new gpu_index-aware logic. This is the heart of the fix.

The Thinking Process

The assistant's reasoning in the messages leading up to 460 reveals a careful, methodical approach. In message 455, the assistant articulates the design:

1. Add int gpu_index parameter (use -1 to mean "auto / use all GPUs like before") 2. When gpu_index >= 0, force n_gpus = 1 and use select_gpu(gpu_index) instead of select_gpu(tid) where tid=0

The -1 sentinel is a thoughtful touch. It preserves backward compatibility for callers that don't have a specific GPU assignment — for example, the older generate_groth16_proofs function used by non-engine paths, which passes null_mut() for the mutex and relies on the internal fallback. By defaulting to -1, those callers continue to work unchanged, while engine workers that pass a specific GPU index get correct routing.

The assistant also identified a secondary concern in message 456: the d_a_cache is a single global singleton. With proper GPU routing, two workers on different GPUs would try to use get_cached_d_a concurrently, causing the cache to thrash — freeing on one GPU and reallocating on the other. The assistant noted this but deferred it: "But that's a separate issue." This was a pragmatic decision — the primary bug (wrong GPU routing) needed fixing first, and the cache thrashing, while suboptimal, would not cause crashes.

Assumptions and Trade-offs

The fix makes several assumptions. First, it assumes that the Rust engine's GPU assignment is correct — that workers are properly mapped to GPUs and that CUDA_VISIBLE_DEVICES is set appropriately. Second, it assumes that the per-GPU mutexes (restored from the shared mutex hack) are sufficient to prevent data races when two workers target the same GPU. Third, it assumes that the d_a_cache thrashing, while undesirable, is tolerable for correctness.

The most significant trade-off is that the fix only handles single-circuit proofs (the partitioned case). For multi-circuit proofs where num_circuits > 1, the original min(ngpus(), num_circuits) logic still applies, and the gpu_index parameter is ignored (or rather, the gpu_base offset is used to distribute circuits across GPUs). This is correct behavior — the multi-circuit path already has its own GPU distribution logic — but it means the fix is specifically targeted at the partitioned proof workflow.

Input and Output Knowledge

To understand this message, one needs knowledge of: the CuZK proving engine's architecture (multi-GPU proving with interlocking data transfer and compute phases); the partitioned proof pipeline where each partition is a single circuit; the C++ CUDA programming model with select_gpu and ngpus(); the Rust FFI boundary and how extern "C" functions are declared; and the concept of GPU mutexes for serializing kernel execution regions.

The message creates new knowledge: the precise location and nature of the GPU routing bug (line 483's min(ngpus(), num_circuits)), the design of the fix (threading gpu_index through the call chain), and the specific edit that implements the core logic change. This edit is the foundation upon which all subsequent changes in the upper layers (FFI, bellperson, pipeline, engine) are built.

Significance

Message 460 is significant because it represents the moment when the assistant stops applying surface-level fixes and instead addresses the architectural root cause. The shared mutex was a classic "fix the symptom" approach: it prevented crashes by serializing access, but it destroyed performance and scalability. The user's challenge — "Isn't the shared lock just a lazy hack?" — forced a deeper analysis that revealed the true bug: the C++ code simply didn't know which GPU it was supposed to use.

The fix is elegant in its simplicity. Rather than adding complex GPU routing logic in C++, it simply accepts a GPU index from the caller and uses it. The Rust engine already knows which GPU each worker should target — it sets CUDA_VISIBLE_DEVICES and manages per-GPU mutexes. The missing piece was a channel to communicate that knowledge to the C++ layer. By adding a single int gpu_index parameter, the assistant bridges that gap.

This message also demonstrates a healthy engineering dynamic: the user acting as a domain expert who understands the proving engine's intended architecture, and the assistant as the implementer who traces the code, identifies the mechanical changes needed, and executes them systematically. The user's critique wasn't just a complaint — it was a correction of direction, steering the fix from a hack toward a proper architectural solution.

In the messages that follow (461-480), the assistant completes the implementation, working up through the Rust FFI, bellperson, pipeline, and engine layers. Each layer adds the gpu_index parameter and passes it through, culminating in the reversion of the shared mutex hack and the restoration of per-GPU mutexes. The build succeeds, and the next step is deployment to both the original test host and the SnapDeals host to verify correct GPU load balancing and elimination of OOMs.

Message 460, for all its brevity, is the critical commit — the one that changes the GPU selection logic and makes the entire multi-GPU fix possible. It is the difference between a system that wastes half its hardware and one that uses it correctly.