The Phantom GPU: Diagnosing Multi-GPU Load Balancing Through Log Artifacts

In the complex world of distributed GPU proving systems, verifying that a fix actually works can be as challenging as writing the fix itself. Message 523 of this opencode session captures a pivotal diagnostic moment: the assistant has just deployed a carefully engineered multi-GPU fix for the CuZK proving engine, run benchmarks that appear to succeed, and now faces a puzzling discrepancy in the log output. The gpu_tid counters all read zero, and the d_a_cache allocation logs only mention GPU 0 — yet the worker assignment logs clearly show jobs being distributed across both GPUs. Is the fix working, or is there a deeper problem lurking beneath the surface?

The Context: A Multi-GPU Fix Born from Crisis

To understand this message, we must first understand what led to it. The session's earlier segments had revealed a critical architectural flaw in the CuZK proving engine's GPU handling. When the Rust engine dispatched single-circuit proofs to the C++ GPU proving code, the C++ layer always routed work to GPU 0 — regardless of which Rust worker submitted the job. On a multi-GPU system, this meant that all four workers would hammer GPU 0 while GPU 1 sat nearly idle, its VRAM barely touched. The initial "fix" had been a shared mutex that serialized all partition proofs onto GPU 0, which was a lazy hack that not only wasted the second GPU but also caused out-of-memory (OOM) failures on SnapDeals workloads when two workers entered the GPU code simultaneously on the same device.

The proper solution, implemented across messages 491–512, was far more elegant: thread a gpu_index parameter through the entire call chain. This required coordinated changes across five files spanning two languages — the C++ groth16_cuda.cu (adding a gpu_index parameter and using select_gpu(gpu_index) for single-circuit proofs), the Rust FFI in supraseal-c2/src/lib.rs, the bellperson prover functions, the pipeline layer, and finally the engine's GPU worker code in engine.rs. The shared mutex hack was reverted, and all call sites now passed either the assigned GPU ordinal or -1 (auto) for non-engine paths. The build succeeded cleanly.

Deployment and Initial Validation

The assistant deployed the new binary to the remote test host (cs-calib, a machine with two NVIDIA RTX A6000 GPUs) and ran benchmarks. Message 520 showed initial success: three concurrent PoRep proofs all completed, and the journal logs revealed beautiful load balancing — workers 0 and 1 on GPU 0, workers 2 and 3 on GPU 1. The nvidia-smi output showed both GPUs with 13 GB of memory allocated (the d_a_cache plus SRS structures), a dramatic improvement from the previous state where GPU 1 had only 700 MB.

But then came the first hint of trouble. In message 521, the assistant checked for d_a_cache allocated log entries and found only GPU 0 references:

Mar 06 03:43:59 cs-calib cuzk[2345201]: CUZK_TIMING: d_a_cache allocated 16 MiB on gpu 0
Mar 06 03:44:35 cs-calib cuzk[2345201]: CUZK_TIMING: d_a_cache allocated 4096 MiB on gpu 0

No GPU 1 entries at all. This was suspicious. In message 522, the assistant dug deeper, checking gpu_tid= entries — the GPU kernel timing logs — and found every single one reading gpu_tid=0. This looked like all GPU work was happening on device 0, contradicting the worker assignment logs.

The Subject Message: Interpreting Log Artifacts

Message 523 is the assistant's analytical response to this apparent contradiction. It begins with a crucial insight that reveals deep understanding of the codebase:

gpu_tid=0 everywhere. That's the tid variable in the per_gpu thread loop, which is always 0 since n_gpus=1 for single-circuit. But with the gpu_base offset, select_gpu(gpu_base + tid) should select the correct GPU. The gpu_tid in the log is just the loop index, not the actual GPU.

This is a masterclass in log interpretation. The assistant recognizes that gpu_tid is a misleading diagnostic — it prints the loop index within the C++ GPU kernel launch function, not the physical GPU device ID. For single-circuit proofs, the loop only iterates once (n_gpus=1), so the index is always zero regardless of which GPU is actually being used. The real GPU selection happens via select_gpu(gpu_base + tid), where gpu_base is the ordinal passed from the Rust engine. The log label is an artifact of the code's original design for multi-circuit batched proofs, where the loop index would correspond to different GPUs in a multi-GPU setup.

The assistant then pivots to a more reliable diagnostic signal:

Let me check more definitively — look at d_a_cache allocated which prints the actual gpu.id():

This shows the assistant's methodical approach: when one diagnostic signal proves ambiguous, find a different one that directly measures the quantity of interest. The d_a_cache allocated log message prints the result of gpu.id(), which is the actual CUDA device ID obtained from the GPU object after select_gpu() has been called. This is ground truth.

The assistant runs a command to grep the journal for these entries over the past five minutes, and the results are displayed in the message:

Mar 06 03:41:41 cs-calib cuzk[2345201]: CUZK_TIMING: d_a_cache allocated 16 MiB on gpu 0
Mar 06 03:41:58 cs-calib cuzk[2345201]: CUZK_TIMING: d_a_cache allocated 4096 MiB on gpu 0
Mar 06 03:43:59 cs-calib cuzk[2345201]: CUZK_TIMING: d_a_cache allocated 16 MiB on gpu 0
Mar 06 03:44:35 cs-calib cuzk[2345201]: CUZK_TIMING: d_a_cache allocated 4096 MiB on gpu 0

All four entries show GPU 0. This is genuinely concerning — if the fix were working correctly, we would expect to see some entries on GPU 1, especially since the worker assignment logs showed workers 2 and 3 being assigned to GPU 1.

The Reasoning Process: What's Really Happening?

The assistant's thinking in this message reveals several layers of analysis:

Layer 1: Dismissing a false signal. The gpu_tid=0 entries are correctly identified as a non-issue — they're loop indices, not device IDs. This prevents a wild goose chase.

Layer 2: Formulating a hypothesis. The assistant implicitly hypothesizes that the select_gpu(gpu_base + tid) call is working correctly based on the code logic. The gpu_base is set to the GPU ordinal passed from the Rust engine, so for a worker assigned to GPU 1, gpu_base=1 and select_gpu(1+0)=select_gpu(1). This should select GPU 1.

Layer 3: Testing the hypothesis with a better signal. The d_a_cache allocated log is chosen because it prints gpu.id() after selection, providing ground truth.

Layer 4: Confronting contradictory evidence. The results show only GPU 0, which contradicts the hypothesis. This sets up the next round of investigation (message 524 and beyond), where the assistant will read the select_gpu implementation in all_gpus.cpp to understand how GPU selection actually works.

Assumptions and Potential Pitfalls

The assistant makes several assumptions in this message:

First, it assumes that d_a_cache allocated is logged for every GPU that performs work. If the cache allocation is lazy and only happens once per GPU, then the first GPU to allocate might "win" and subsequent allocations might be skipped. However, the log shows two distinct allocation sizes (16 MiB and 4096 MiB) for each of two time windows, suggesting separate allocation events.

Second, it assumes that the gpu.id() method returns the physical CUDA device index (0 or 1) rather than some internal logical index. If gpu.id() returns a different kind of identifier, the logs could be misleading.

Third, it assumes that the worker assignment logs (worker_id=2 gpu=1) are accurate. These logs come from the Rust engine when it dispatches work, so they reflect the intended GPU assignment, not necessarily the actual GPU used.

The most significant assumption is that the select_gpu function uses the index as a direct CUDA device ordinal. As message 524 will reveal, this assumption is about to be tested by reading the actual implementation.

The User's Independent Validation

Interestingly, the user independently validates the fix in message 526, reporting that they "did see load on both GPUs in nvtop and seemed pretty quick." This is a valuable real-world data point — nvtop shows actual GPU utilization in real time, which is arguably more reliable than log parsing. If both GPUs showed load during the benchmark run, then the fix is working at the hardware level, even if the logs are ambiguous.

This creates an interesting tension: the logs suggest only GPU 0 is being used, but the user's observation and the worker assignment logs suggest both GPUs are active. The resolution (which occurs in subsequent messages) involves reading the select_gpu implementation and discovering that the GPU selection mechanism uses a different mapping than expected — but ultimately the fix does work because the gpu_base offset correctly translates to the physical device.

Broader Lessons

This message exemplifies several important principles in debugging distributed GPU systems:

Logs are not truth; they are artifacts. A log message that says gpu_tid=0 does not mean GPU 0 was used — it means the loop index was 0. Understanding what a log actually measures is essential.

Multiple diagnostic signals are essential. The assistant uses worker assignment logs, nvidia-smi memory usage, gpu_tid timing logs, d_a_cache allocation logs, and eventually nvtop observations. Each signal has different reliability characteristics, and convergence across signals builds confidence.

The importance of code reading. The assistant's first instinct when faced with contradictory evidence is to read the source code of select_gpu (message 524). This is the correct approach — logs can be misinterpreted, but code is (usually) deterministic.

The value of user collaboration. The user's independent observation with nvtop provides a sanity check that prevents the assistant from going too far down a rabbit hole of log interpretation.

Message 523 captures the moment between deployment and certainty — the interval where a fix that looks correct in theory must prove itself in practice, and where the evidence must be carefully weighed before drawing conclusions.