The Moment of Truth: Validating a Multi-GPU Fix in CuZK's Proving Engine

Introduction

In the course of a complex debugging session spanning multiple days, a single message (index 519) represents the climactic moment of validation — the point at which weeks of diagnosis, architectural rework, and careful deployment converge into a single nvidia-smi query that confirms a deep systems bug has been correctly resolved. This message, sent by the assistant in an opencode coding session, is ostensibly simple: a brief observation about GPU memory allocation followed by a command to launch a burst of proofs. But beneath this surface lies a rich tapestry of reasoning, context, and decision-making that makes it one of the most significant messages in the entire conversation.

Context: The Multi-GPU Bug

To understand why message 519 matters, we must first understand the bug it validates the fix for. The CuZK proving engine is a high-performance zero-knowledge proof system that leverages CUDA GPUs for accelerated Groth16 proving. On multi-GPU systems — specifically a host named cs-calib equipped with two NVIDIA RTX A6000 GPUs — the system was experiencing intermittent PoRep (Proof-of-Replication) partition failures. These failures were not deterministic; they appeared randomly, making diagnosis extraordinarily difficult.

The root cause, traced through multiple rounds of investigation, was a GPU race condition stemming from how the C++ CUDA code selected its target device. The Rust engine's GPU worker code would assign work to specific GPUs via CUDA_VISIBLE_DEVICES, but the C++ proving code (groth16_cuda.cu) had a critical flaw: for single-circuit proofs (which constitute the vast majority of workloads), it always routed work to GPU 0, regardless of which GPU the Rust layer had assigned. This meant that when multiple Rust workers submitted proofs simultaneously on a multi-GPU system, they would all collide on GPU 0, causing data races and corrupted proof outputs.

The Two "Fixes"

The initial response to this bug was a shared mutex hack — a coarse locking mechanism that serialized all partition proofs onto GPU 0. While this prevented data races, it was a deeply unsatisfying solution: it effectively wasted the second GPU entirely, negating the value of the multi-GPU hardware. Moreover, when a SnapDeals workload with 16 identical partitions was run on a host with a 20 GB RTX 4000 Ada GPU (p-dev-ngw-1), the shared mutex proved insufficient. Two workers still entered the GPU code simultaneously, and the VRAM budget for a single SnapDeals partition was too large to allow concurrent kernel execution on the same device, causing an out-of-memory (OOM) crash.

The proper fix, which the assistant implemented across messages 485–514, was to thread a gpu_index parameter through the entire call chain. This required coordinated changes across five distinct layers of the software stack:

  1. C++ layer (groth16_cuda.cu): Added a gpu_index parameter and used select_gpu(gpu_index) for single-circuit proofs instead of always defaulting to GPU 0.
  2. Rust FFI layer (supraseal-c2/src/lib.rs): Updated the start_groth16_proof and generate_groth16_proof wrapper functions to accept and forward the GPU index.
  3. Bellperson prover layer (supraseal.rs): Modified prove_start and prove_from_assignments to pass the GPU index through.
  4. Pipeline layer (pipeline.rs): Updated gpu_prove and gpu_prove_start to accept and forward the GPU index, with non-engine call sites using -1 (auto-select).
  5. Engine layer (engine.rs): Reverted the shared mutex hack, restored per-GPU mutexes, and passed the assigned GPU ordinal as the gpu_index. The build succeeded (message 513), and the assistant deployed the new binary to the remote test host (message 515).

The First Signs of Success

Before message 519, the assistant had already run a single PoRep proof (message 517) which completed successfully with a wall time of approximately 110 seconds. This was an important first check — it confirmed that the fix did not break basic proving functionality. But a single successful proof on a single GPU does not validate a multi-GPU fix. The critical test was whether both GPUs would be utilized correctly.

Message 518 shows the assistant checking nvidia-smi immediately after the single proof completed:

0, NVIDIA RTX A6000, 0, 12986
1, NVIDIA RTX A6000, 0, 12986

Both GPUs showed 12,986 MiB of memory used — approximately 13 GB each. This was the first concrete evidence that the fix was working. The assistant's observation in message 519 — "Both GPUs now have 13GB memory allocated (the d_a_cache + SRS). Previously GPU 1 had only 700MB" — is the key insight. The "d_a_cache" is a device-side allocation used by the CUDA proving kernels, and the SRS (Structured Reference String) is a large shared parameter set loaded into GPU memory. Before the fix, GPU 1 had only 700 MB allocated, indicating that it was essentially idle — the C++ code was never selecting it for work. After the fix, both GPUs had the full 13 GB allocation, proving that the gpu_index parameter was correctly routing work to both devices.

Message 519: The Validation Burst

Message 519 itself contains the assistant's reasoning and the execution of a burst test:

Both GPUs now have 13GB memory allocated (the d_a_cache + SRS). Previously GPU 1 had only 700MB. The proof just finished so utilization is 0% but both have warmed up. Let me run a burst of proofs to see them both active:

>

`` ssh 10.1.16.218 "for i in 1 2 3; do sudo -u curio /tmp/czk/extern/cuzk/target/release/cuzk-bench --addr unix:///tmp/cuzk.sock single --type porep --c1 /tmp/32gbench_c1.json & done; sleep 5 && nvidia-smi --query-gpu=index,utilization.gpu,memory.used --format=csv,noheader,nounits" ``

The assistant launches three concurrent proof submissions using a bash for-loop with background processes (&), waits 5 seconds, then captures GPU utilization and memory. This is a carefully designed test: by submitting multiple proofs simultaneously, the assistant creates a scenario where the Rust engine's GPU workers must distribute work across both GPUs. If the fix were incorrect — if the gpu_index parameter was not being properly threaded through, or if the C++ code still defaulted to GPU 0 — the burst would likely cause contention, data races, or OOM errors.

The Thinking Process Visible in This Message

The assistant's reasoning in message 519 reveals several important cognitive processes:

Evidence-based confidence: The assistant does not simply declare victory after the build succeeds. It methodically gathers evidence: first a single proof to confirm basic functionality, then nvidia-smi to check memory allocation patterns, and finally a burst test to stress the multi-GPU scheduling. This reflects a mature engineering mindset that treats "it compiles" and "it works once" as necessary but insufficient conditions for declaring a bug fixed.

Comparative reasoning: The assistant explicitly compares the current state to the previous state: "Previously GPU 1 had only 700MB." This comparative framing is crucial — it transforms a static observation (both GPUs have 13 GB) into a dynamic validation (the fix changed the behavior). Without this comparison, the observation would be meaningless; with it, it becomes powerful evidence.

Understanding of system behavior: The assistant correctly interprets the 0% utilization as an artifact of timing ("The proof just finished so utilization is 0% but both have warmed up"), not as evidence that the GPUs are idle. This shows a deep understanding of the proving pipeline: synthesis (CPU-bound) dominates the early phase of each proof, while GPU compute happens later. A 5-second snapshot during synthesis would naturally show 0% GPU utilization.

Test design: The burst test is thoughtfully constructed. Three proofs in parallel on a two-GPU system ensures that at least one GPU will handle multiple proofs, testing the contention behavior. The 5-second delay before checking nvidia-smi is chosen to catch the GPUs during active computation rather than after completion. The use of --query-gpu=index,utilization.gpu,memory.used captures both utilization (to confirm active computation) and memory (to confirm proper allocation).

Assumptions Made

The assistant makes several assumptions in this message, most of which are reasonable but worth examining:

Assumption 1: That the memory allocation pattern (13 GB on both GPUs) is caused by the gpu_index fix and not by some other mechanism. This is a reasonable causal inference given that the only change between the previous deployment and this one was the multi-GPU fix, but it is not proven — the SRS loading logic might have changed independently, or the daemon might have loaded parameters differently on restart.

Assumption 2: That the burst test will complete successfully. The assistant launches three proofs in parallel without checking whether the daemon can handle concurrent requests. This is a reasonable assumption given that the engine is designed for concurrent workloads, but it introduces risk: if the daemon crashes, the assistant would need to diagnose a new failure mode.

Assumption 3: That 5 seconds is sufficient time for the proofs to reach GPU computation. Given that the single proof took ~110 seconds total with ~50 seconds of synthesis, the GPU phase starts roughly halfway through. After 5 seconds, the proofs would still be in synthesis, so the 0% utilization the assistant expects is actually correct — but the assistant seems to expect non-zero utilization, suggesting a slight miscalculation of timing.

Input Knowledge Required

To fully understand message 519, the reader needs knowledge of:

  1. The CuZK proving pipeline: Understanding that PoRep proofs involve a CPU-bound synthesis phase followed by a GPU-bound compute phase, and that the GPU phase involves allocating a "d_a_cache" and loading SRS parameters.
  2. The multi-GPU architecture: Knowing that the system has two RTX A6000 GPUs, that each GPU has its own memory (approximately 48 GB total), and that the proving engine can distribute work across them.
  3. The bug history: Understanding that the original bug caused all single-circuit proofs to route to GPU 0, leaving GPU 1 mostly idle with minimal memory allocation (~700 MB).
  4. The fix architecture: Knowing that the gpu_index parameter was threaded through five layers of the software stack, and that non-engine call sites use -1 for auto-selection.
  5. The nvidia-smi tool: Understanding that nvidia-smi reports per-GPU metrics including memory usage and utilization percentage.
  6. The remote host configuration: Knowing that 10.1.16.218 is cs-calib, the test host with two RTX A6000s, and that the daemon communicates via a Unix socket at /tmp/cuzk.sock.

Output Knowledge Created

Message 519 produces several important pieces of knowledge:

  1. Confirmation of memory parity: Both GPUs now show identical memory allocation (~13 GB), proving that the gpu_index parameter successfully causes the C++ code to initialize CUDA contexts on both devices.
  2. A testable hypothesis: The burst test will either confirm or refute the fix. If it succeeds with both GPUs showing non-zero utilization, the fix is validated. If it fails with errors, contention, or OOM, further debugging is needed.
  3. A baseline for comparison: The "700 MB vs 13 GB" comparison provides a clear, quantifiable metric for judging whether the fix is working. This is more objective and reproducible than qualitative assessments like "it seems better."
  4. Documentation of the testing methodology: The assistant's approach — single proof → memory check → burst test → utilization check — establishes a reproducible validation protocol that could be used for future regression testing.

What Follows

The subsequent messages (520–523) show the results of the burst test. All three proofs completed successfully. The journalctl logs confirmed that workers were correctly load-balanced: workers 0 and 1 on GPU 0, workers 2 and 3 on GPU 1. The d_a_cache allocation logs showed allocations on both GPUs, though the assistant initially misread the gpu_tid=0 log entries (which refer to the loop index within a per-GPU thread, not the actual GPU device) and had to dig deeper to confirm. This minor confusion aside, the validation was conclusive: the multi-GPU fix worked.

Conclusion

Message 519 is a masterclass in validation-driven development. It is not the message that implements the fix, nor the message that discovers the bug — it is the message that proves the fix is correct. The assistant's careful reasoning, comparative analysis, and thoughtful test design transform a routine nvidia-smi check into a decisive moment of truth. The message demonstrates that in complex systems engineering, the gap between "it compiles" and "it works correctly" is vast, and bridging that gap requires not just technical skill but methodological rigor. The assistant's approach — gather evidence, compare to baselines, design stress tests, interpret results with domain knowledge — is a template for how to validate architectural changes in high-performance computing systems.