The Smoking Gun: Tracing GPU Underutilization Through C++ Mutex Timing

Introduction

In any complex systems debugging effort, there comes a moment when the fog of speculation lifts and a single piece of data reveals the true nature of the problem. For the team investigating severe GPU underutilization in the cuzk proving pipeline, that moment arrived in message [msg 3028]. This message represents the critical inflection point where weeks of Rust-side instrumentation, hypothesis generation, and dead-end investigations crystallized into a clear, data-backed understanding of the bottleneck. The assistant, after tracing through hundreds of lines of C++ CUDA code, discovered that the GPU mutex held through a barrier synchronization point, and then confirmed the suspicion by extracting real-time CUZK_TIMING logs from the remote proving machine. The data was unambiguous: the ntt_msm_h phase, responsible for Host-to-Device (H2D) transfers of synthesis vectors, varied from 2.7 seconds to a staggering 8.9 seconds per partition, while actual GPU compute remained a stable ~1.2 seconds. This article examines this pivotal message in detail, unpacking the reasoning, the assumptions, the discoveries, and the technical context that made it the turning point of the entire investigation.

The Context: A Mystery of Missing GPU Cycles

To understand the significance of message [msg 3028], we must first understand what preceded it. The cuzk proving daemon was experiencing approximately 50% GPU utilization—a puzzling shortfall given that the system employed two GPU workers specifically designed to interleave PCIe transfers with GPU compute. The architecture was deliberately crafted for overlap: while Worker A held the GPU mutex and ran CUDA kernels, Worker B was supposed to perform CPU-side preprocessing and data transfer preparation, so that when Worker A released the mutex, Worker B could immediately begin its GPU work. In theory, this should have kept the GPU nearly continuously busy.

Yet the metrics told a different story. The Rust-side instrumentation—GPU_TIMING and FIN_TIMING logs—showed prove_start_ms values ranging from 4.2 seconds to 16.2 seconds per partition, while the user confirmed that actual GPU compute was only 1.5–2 seconds per partition ([msg 3007]). The gpu_ms metric, which was supposed to measure GPU kernel time, tracked almost identically with prove_start_ms, revealing that it was actually measuring the entire C++ function's wall time, not just CUDA kernel execution. Something was consuming 2–14 seconds of overhead per partition, and the GPU was sitting idle during most of it.

The initial suspects were varied. The assistant first suspected tracker lock contention, then malloc_trim overhead, then coarse-grained mutex design ([msg 3008]). The user corrected a key assumption in [msg 3009], explaining that the two GPU workers existed specifically to interleave PCIe transfer with compute—meaning the mutex was designed to be narrow, covering only CUDA kernel launches. This forced a re-examination. If the mutex was already narrow, why was the GPU still underutilized? The answer, the assistant reasoned, must lie somewhere in the C++ code's internal synchronization, not in the Rust-side orchestration.

The Message: Tracing the C++ Flow

Message [msg 3028] opens with the assistant declaring, "Now I have a complete picture of the C++ code. Let me analyze the timing." This statement reflects the culmination of a lengthy investigation that involved reading the Rust pipeline code, tracing through the FFI boundary into supraseal-c2, and finally reading the CUDA kernel implementation in groth16_cuda.cu. The assistant had spent multiple messages following the call chain from gpu_prove_start in Rust through prove_start in bellperson's supraseal module, across the FFI boundary via start_groth16_proof, and into the C++ generate_groth16_proofs_start_c function.

What follows is a meticulous breakdown of the C++ function's phases, organized into four distinct stages:

Phase 1 — Before the mutex (CPU work, not holding GPU lock): The assistant identifies that setup operations—retrieving the verification key, slicing point data, asserting sizes—are essentially instantaneous. More importantly, the function spawns a prep_msm_thread that performs CPU-intensive preprocessing: a par_map phase that classifies scalars and builds bitmasks, a merge phase that counts popcounts and resizes tail MSM arrays, a second par_map that populates scalars and bases for tail MSMs, and finally a b_g2_msm phase that runs a CPU-based Pippenger MSM on the G2 curve. Crucially, this thread runs concurrently with whatever the GPU thread will do.

Phase 2 — Acquire the GPU mutex (line 900): This is where the interleaving design comes into play. The assistant notes that while one worker holds the mutex doing GPU work, another worker's prep_msm_thread can run CPU preprocessing. This is the intended overlap mechanism.

Phase 3 — Under the GPU mutex: The GPU thread is spawned, which runs NTT (Number Theoretic Transform) and MSM operations on the h polynomial, then waits for the CPU preprocessing to finish via a barrier, then executes batch additions and tail MSMs on the GPU. The mutex is held throughout this entire period.

Phase 4 — After mutex release: The prep_msm_thread handle is stored, r_s/s_s vectors are copied, and the pending handle is returned.

The assistant's critical insight comes next: "The critical bottleneck is that the GPU mutex remains held throughout the entire GPU thread lifecycle, including the barrier wait for CPU preprocessing. If the CPU preprocessing is slow, the GPU thread blocks at the barrier while still holding the mutex, preventing other workers from acquiring it and starting their GPU work."

This is the key architectural flaw. The mutex is designed to be narrow—covering only "CUDA kernel region"—but in practice, it covers not just kernel launches but also the barrier synchronization point where the GPU thread waits for CPU preprocessing to complete. If the CPU preprocessing (prep_msm_thread) takes longer than the GPU's NTT+MSM work, the GPU finishes its kernels, hits the barrier, and sits idle—still holding the mutex—while waiting for the CPU to catch up. Meanwhile, the other worker is blocked trying to acquire the mutex, unable to start its own GPU work even though the GPU is idle.

The Discovery: CUZK_TIMING Logs

The assistant then makes a crucial observation: "I can see there's already timing instrumentation scattered throughout the code that logs various phase durations to stderr." This is a pivotal moment. The C++ code already contained CUZK_TIMING instrumentation—the team had built-in logging that they simply hadn't checked yet. The assistant runs a remote SSH command to grep these logs from the proving machine:

CUZK_TIMING: gpu_tid=0 ntt_msm_h_ms=8860
CUZK_TIMING: gpu_tid=0 batch_add_ms=403
CUZK_TIMING: gpu_tid=0 tail_msm_ms=194 gpu_total_ms=9458
CUZK_TIMING: split_vectors_ms=0 setup_to_split_ms=0
CUZK_TIMING: async_dealloc_ms=202
CUZK_TIMING: gpu_tid=0 ntt_msm_h_ms=3290
CUZK_TIMING: prep_msm_ms=3106
CUZK_TIMING: gpu_tid=0 batch_add_ms=400
CUZK_TIMING: gpu_tid=0 tail_msm_ms=197 gpu_total_ms=3888
CUZK_TIMING: b_g2_msm_ms=995 num_circuits=1

The data is revelatory. The ntt_msm_h_ms metric—measuring the time for NTT setup and MSM on the h polynomial, which includes H2D transfers—varies wildly from 3,290 ms to 8,860 ms. Meanwhile, batch_add_ms is a stable ~400 ms, tail_msm_ms is ~197 ms, and prep_msm_ms (CPU preprocessing) ranges from 1,488 ms to 4,178 ms. The actual GPU compute (batch_add + tail_msm + the kernel portion of ntt_msm_h) is roughly 1.2 seconds, consistent with the user's claim of 1.5–2 seconds per partition.

The smoking gun is the variation in ntt_msm_h_ms. A 3x swing (2.7s to 8.9s) points directly to memory bandwidth contention. When multiple synthesis threads are running simultaneously, they compete for host memory bandwidth, which slows the H2D PCIe transfers that occur inside ntt_msm_h. The H2D copies are staging through a small pinned bounce buffer because the a/b/c synthesis vectors are standard heap allocations, not pinned memory—forcing CUDA to perform slow, staged transfers rather than direct DMA at the PCIe Gen5 x16 line rate of ~50 GB/s.

Assumptions Made and Corrected

This message is particularly interesting for the assumptions it reveals and corrects. The assistant initially assumed that the GPU mutex was coarse-grained, covering the entire gpu_prove_start function ([msg 3008]). The user corrected this in [msg 3009], explaining that the two-worker design specifically aimed to interleave PCIe transfers with compute, implying a narrow mutex. The assistant then had to reconcile this corrected understanding with the observed metrics.

A second assumption was that the Rust-side instrumentation was sufficient to diagnose the problem. The assistant had added GPU_TIMING and FIN_TIMING instrumentation to the Rust code, measuring prove_start_ms, gpu_ms, malloc_trim_ms, and tracker lock contention. But these metrics only measured the Rust-side orchestration, not the internal phases of the C++ code. The real bottleneck was invisible from Rust—it lived entirely within the C++ CUDA implementation.

A third assumption was that the gpu_ms metric measured actual CUDA kernel time. The assistant discovered that gpu_ms ≈ prove_start_ms (e.g., 11,383 ms vs 11,386 ms), meaning gpu_ms was measuring the entire C++ function wall time, not just kernel execution. This misled the initial analysis by conflating mutex wait time, CPU preprocessing, and actual GPU compute into a single number.

Input and Output Knowledge

To understand this message, the reader needs several pieces of input knowledge: the architecture of the cuzk proving pipeline with its two GPU workers and interleaving design; the Rust-side instrumentation framework (GPU_TIMING, FIN_TIMING); the FFI boundary between Rust (bellperson's supraseal module) and C++ (supraseal-c2); the concept of GPU mutex synchronization in multi-worker CUDA systems; and the general structure of Groth16 proving (NTT, MSM, batch addition, tail MSM phases).

The message creates significant output knowledge. It establishes that the GPU mutex holds through the barrier synchronization point, which is the root cause of the interleaving failure. It reveals that ntt_msm_h_ms is the dominant and most variable phase, ranging from 2.7s to 8.9s. It confirms that actual GPU compute is stable at ~1.2s per partition. It demonstrates that the existing CUZK_TIMING instrumentation in the C++ code is the right tool for diagnosing the problem. Most importantly, it shifts the investigation's focus from Rust-side orchestration to C++-side memory management—specifically, the need for pinned memory to eliminate the H2D staging bottleneck.

The Thinking Process

The assistant's reasoning in this message is exemplary of systematic debugging. It begins by reconstructing the complete control flow of the C++ function from memory (having just read the source code). It organizes this flow into phases, each with a clear characterization of what work is done and whether the GPU mutex is held. It then identifies the critical architectural issue—the barrier wait inside the mutex—before even looking at the timing data. This is hypothesis-driven debugging: the assistant forms a theory of where the bottleneck must be based on the code structure, then seeks data to confirm or refute it.

The decision to check the CUZK_TIMING logs is itself a product of careful reading. The assistant noticed that the C++ code had #ifdef CUZK_TIMING instrumentation blocks scattered throughout, printing to stderr. Rather than adding new instrumentation (which would require recompiling and redeploying), the assistant checked whether the existing instrumentation was already enabled and producing logs on the remote machine. This is a textbook example of leveraging existing observability before adding new instrumentation.

The SSH command itself is worth noting: ssh -p 40612 root@141.0.85.211 'grep "CUZK_TIMING" /data/cuzk-timing.log | tail -40'. The assistant is working with a remote machine (the proving host), accessing logs that have been accumulated during real proving runs. The timing data is from actual production-like workloads, not synthetic benchmarks—making the findings immediately actionable.

Significance and Impact

Message [msg 3028] is the turning point of the entire GPU underutilization investigation. Before this message, the team was operating on hypotheses: maybe the tracker lock was contended, maybe malloc_trim was too slow, maybe the Rust hot path had overhead. After this message, they had hard data showing exactly where the time was going. The ntt_msm_h phase was consuming 3–9 seconds per partition, and its variability pointed to memory bandwidth contention from concurrent synthesis threads.

This discovery directly motivated the zero-copy pinned memory pool solution that would be implemented in subsequent chunks ([chunk 22.0], [chunk 22.1]). By allocating the a/b/c synthesis vectors in pinned memory via cudaHostAlloc, the H2D transfers could bypass the small pinned bounce buffer and achieve direct DMA at PCIe Gen5 line rates. The expected improvement was dramatic: collapsing the H2D transfer from seconds to milliseconds, allowing the NTT setup to overlap cleanly with the compute phases of other partitions, and pushing GPU utilization from ~50% toward 90%+.

Conclusion

Message [msg 3028] exemplifies the essence of systems debugging: the convergence of code reading, hypothesis formation, and empirical data collection. The assistant traced through hundreds of lines of C++ CUDA code, identified the architectural flaw in the mutex/barrier interaction, and confirmed the hypothesis with real production timing logs. The discovery that ntt_msm_h_ms varied from 2.7s to 8.9s while actual GPU compute was a stable 1.2s provided the unambiguous evidence needed to commit to the pinned memory pool solution. This message is a masterclass in systematic investigation—showing that the most important debugging tool is not any single instrumentation technique, but the disciplined application of reasoning to trace from observed symptoms to root causes.