From Lazy Hack to Proper Architecture: Threading GPU Index Through the Proving Stack

"Why is GPU prove for the second GPU not running.. on the second GPU? That's the whole point."

This sharp question from the user in message [msg 444] cut to the heart of a growing architectural problem. The assistant had been debugging an out-of-memory (OOM) crash on a multi-GPU proving host, and the initial fix—a shared mutex that serialized all GPU work onto device 0—was exposed as a lazy workaround that wasted half the available hardware. The response, message [msg 457], marks the precise moment when the assistant pivoted from band-aid fixes to a proper architectural solution: threading a gpu_index parameter through the entire call chain from the Rust engine layer down to the C++ CUDA kernels, so that each worker actually proves on the GPU it was assigned to.

The Subject Message

The message reads in full:

The d_a_cache has a gpu_id field and already handles switching between GPUs — it frees on the old GPU and reallocates on the new one. But with concurrent workers on different GPUs, this will thrash. I need to make it a per-GPU array. Let me now start implementing.

>

I'll work bottom-up: C++ first, then supraseal-c2 Rust, then bellperson, then pipeline, then engine.

>

C++ changes (groth16_cuda.cu): [edit] /tmp/czk/extern/supraseal-c2/cuda/groth16_cuda.cu Edit applied successfully.

At first glance, this is a short message—just a few lines of reasoning followed by an edit command. But it represents a critical turning point in the conversation. The assistant had spent several messages diagnosing why SnapDeals partitioned proofs were crashing on a 20 GB RTX 4000 Ada host (p-dev-ngw-1). The logs showed two workers entering the GPU proving code simultaneously, both routing to GPU 0, with one allocating 4096 MiB of d_a_cache and the second immediately OOMing. The earlier "fix" had been a shared mutex in the Rust engine layer that forced all partition proofs to serialize onto a single GPU, which the user correctly identified as a lazy hack.

The Reasoning and Motivation

To understand why message [msg 457] was written, we need to trace the reasoning chain that led to it. The problem began when the assistant deployed a shared-mutex fix to prevent concurrent GPU access, only to discover that a SnapDeals workload with 16 identical partitions still OOM'd on the multi-GPU host. The logs revealed the root cause: both workers entered gpu_prove_start at the same millisecond, both reached the C++ GPU code, and both tried to allocate on GPU 0 simultaneously.

The assistant's investigation in messages [msg 445] through [msg 456] progressively uncovered the deeper issue. In [msg 446], the assistant identified the smoking gun at line 483 of groth16_cuda.cu:

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

For partitioned proofs, num_circuits=1, so n_gpus=1. The GPU loop at line 883 then spawned only one thread with tid=0, which unconditionally called select_gpu(0). This meant that every single-circuit proof—regardless of which Rust worker submitted it or which GPU that worker was configured to use—would always route to GPU 0. The second GPU sat idle while the first GPU was overloaded.

The user's challenge in [msg 444] reframed the problem entirely. The shared mutex wasn't just suboptimal; it was architecturally wrong. CuZK was designed as a sophisticated proving engine with multi-GPU support, interlocking data transfer phases, and per-GPU memory management. The shared mutex bypassed all of that, reducing a dual-GPU system to a single-GPU system with extra contention overhead.

Decisions Made in This Message

Message [msg 457] contains several key decisions:

1. Make d_a_cache per-GPU. The global g_d_a_cache singleton had a gpu_id field and could switch between GPUs by freeing on the old device and reallocating on the new one. But with concurrent workers on different GPUs, this would thrash—constantly freeing and reallocating as workers alternated. The assistant correctly identified this as a problem and decided to convert it to a per-GPU array.

2. Work bottom-up through the stack. The assistant chose to start at the lowest layer (C++ CUDA code) and move upward through supraseal-c2 Rust FFI, bellperson prover functions, the pipeline layer, and finally the engine. This is the correct order for a parameter-threading change: you modify the function signature at the bottom, then update each caller in turn, ensuring the compiler catches any mismatches at each level.

3. Use -1 as a sentinel value. The assistant had earlier decided (in [msg 455]) that gpu_index = -1 would mean "auto-detect, use all GPUs as before," while gpu_index >= 0 would force a specific GPU. This preserves backward compatibility for non-engine callers that don't care about GPU assignment.

4. Revert the shared mutex hack. Although not explicitly stated in this message, the todo list shows "Revert shared mutex hack from engine.rs — restore per-GPU mutexes" as pending. The proper fix makes the shared mutex unnecessary because each worker will prove on its assigned GPU, and per-GPU mutexes will correctly serialize access to each device independently.

Assumptions and Potential Issues

The assistant makes several assumptions in this message:

That d_a_cache thrashing is a real problem worth fixing now. With proper GPU routing, two workers on different GPUs will call get_cached_d_a concurrently. The global singleton checks gpu_id and reallocates if the GPU doesn't match. If worker A (GPU 0) allocates, then worker B (GPU 1) runs and frees GPU 0's allocation to reallocate on GPU 1, then worker A runs again and frees GPU 1's allocation... this ping-pong would be catastrophic for performance. The assistant's decision to make it per-GPU is correct, but it adds complexity to the C++ code.

That the C++ edit is the right starting point. The assistant applies the C++ edit in this very message without showing the diff. This is a leap of faith—the edit is applied but not verified until later messages. The assistant is working from memory of the code structure, having read the file multiple times in preceding messages.

That -1 is a safe sentinel. The C++ code uses size_t for GPU-related indices in some places and int in others. Passing -1 as an int and comparing it against size_t values could cause signed/unsigned comparison warnings or unexpected behavior if not handled carefully.

That the Rust engine already knows which GPU each worker should use. This is true—the engine sets CUDA_VISIBLE_DEVICES per worker—but the GPU index wasn't being passed through the call chain. The assumption is that threading the index down is straightforward once the parameter exists at each layer.

Input Knowledge Required

To understand this message, one needs:

Output Knowledge Created

This message creates:

The Thinking Process

The assistant's thinking in this message is remarkably concise but reveals several layers of analysis:

First, it evaluates the d_a_cache situation. The cache already handles GPU switching (free on old GPU, allocate on new), but the assistant recognizes that concurrent access from different GPUs will cause thrashing. This shows systems-level thinking: a component that works correctly in a single-GPU or sequential multi-GPU scenario may fail in a concurrent multi-GPU scenario.

Second, the assistant commits to a specific implementation order: bottom-up. This is classic software engineering wisdom—change the lowest-level interface first, then propagate upward. The alternative (top-down) would leave dangling references and broken builds at each intermediate step.

Third, the assistant signals the start of implementation with "Let me now start implementing" and immediately applies an edit. This is the moment of transition from analysis to action. The preceding messages ([msg 445] through [msg 456]) were purely diagnostic; this message is the first concrete step toward the fix.

Conclusion

Message [msg 457] is a hinge point in the conversation. It represents the moment when the assistant stopped applying workarounds and started building the correct architectural solution. The user's challenge in [msg 444]—"Isn't the shared lock just a lazy hack?"—forced a re-evaluation of the entire approach. The assistant's response shows the hallmarks of good systems engineering: recognizing when a quick fix has become a dead end, tracing the root cause to its architectural foundation, planning a multi-layer change, and executing from the bottom up.

The message is short, but it carries the weight of the entire multi-GPU fix. Every subsequent change—the Rust FFI wrapper updates, the bellperson prover modifications, the pipeline layer adjustments, the engine.rs reversion—flows from the decisions made in these few lines. It is a masterclass in pivoting from tactical patching to strategic architecture.