The Silent Proof: Monitoring the First Real C2 Computation in the cuzk Proving Engine

A Pivotal Moment in the Validation Pipeline

In the development of any complex distributed system, there comes a moment when the scaffolding falls away and the system must prove itself against real workloads. For the cuzk proving daemon — a purpose-built, persistent SNARK proving engine designed to replace Curio's ad-hoc per-proof process spawning — that moment arrived when a 51-megabyte C1 output file was submitted over gRPC and the daemon began computing its first real Groth16 proof for a 32 GiB Filecoin sector. The message at index 224 in this conversation captures a brief but deeply significant snapshot of that computation in progress. It is a message that, on its surface, reports only a handful of GPU metrics, but when read against the full context of the session, reveals the assumptions, anxieties, and architectural insights that define the proving pipeline's behavior under real load.

The Message in Full

The assistant writes:

The proof is computing (no new log lines means the supraseal prover is running GPU kernels silently). Let me check GPU utilization:

>

`` utilization.gpu [%], utilization.memory [%], memory.used [MiB], memory.total [MiB], temperature.gpu, power.draw [W] 4 %, 2 %, 1690 MiB, 16303 MiB, 55, 45.41 W ``

This is the entirety of the message. It is terse, diagnostic, and unadorned. Yet it sits at the intersection of weeks of architectural design, implementation, and debugging — and its implications ripple forward into every subsequent optimization decision.

Why This Message Was Written: The Silent Computation Problem

The immediate trigger for this message is a silence. The daemon log has stopped producing new lines. After a flurry of activity — the C1 data arriving, the JSON being deserialized, the correct proof type (StackedDrg32GiBV1_1) being registered, the parameter cache being checked, and the 45 GiB SRS file being read into SupraSeal memory — the log falls quiet. The assistant observes this silence and must interpret it.

There are two possible interpretations of a silent prover. The first is that computation is proceeding normally but the GPU kernels, being native CUDA code compiled by nvcc through the sppark library, do not emit progress logs. The second is that the process has hung, deadlocked, or crashed without producing an error. The assistant must distinguish between these possibilities, and the tool chosen for this diagnostic is nvidia-smi.

This choice reveals a key assumption: that the GPU is the ultimate arbiter of whether work is being done. If the CUDA kernels are running, the GPU will show utilization, memory consumption, and power draw. If the process has hung during CPU synthesis, the GPU will sit idle. The assistant is using the GPU as a proxy for the entire proving pipeline's health.

The GPU Metrics: A Story in Numbers

The nvidia-smi output reports: 4% GPU utilization, 2% memory utilization, 1690 MiB of 16303 MiB used, 55°C temperature, and 45.41 watts of power draw. These numbers tell a nuanced story.

The low GPU utilization (4%) is the most telling metric. It confirms that the proof is not yet in the GPU-intensive phase of the Groth16 pipeline. The typical C2 proof flow proceeds through two major computational stages: first, CPU-bound circuit synthesis using bellperson, where the R1CS constraints are generated from the witness; second, GPU-bound computation involving Number Theoretic Transforms (NTT) and Multi-Scalar Multiplications (MSM) on the elliptic curve group. The 4% utilization places the proof firmly in the first stage. The CPU is synthesizing the circuit while the GPU waits.

The memory usage of 1690 MiB is also informative. The RTX 5070 Ti has 16 GiB of VRAM, and only about 1.65 GiB is consumed. This is consistent with the SRS parameters having been loaded into host memory (the 45 GiB file) but not yet transferred to device memory for the MSM computation. The GPU memory holds only the minimal state needed for the initial kernel setup. Later, when the MSM phase begins, memory usage will spike dramatically as the SRS is uploaded and the computation proceeds.

The power draw of 45.41 watts and temperature of 55°C are baseline idle-plus-light-load values. An RTX 5070 Ti under full compute load would draw significantly more power and run hotter. These metrics confirm that the GPU is essentially idling, waiting for CPU synthesis to complete.

The Assumptions Embedded in the Diagnostic

This message rests on several assumptions, most of them correct but worth examining. The first is that the supraseal CUDA kernels are genuinely silent. This is accurate — the sppark library, which provides the GPU acceleration for the Groth16 prover, is a C++/CUDA codebase that does not instrument its inner loops with progress logging. The kernels run to completion without emitting any status to stdout or stderr. Silence is the expected behavior.

The second assumption is that nvidia-smi provides a reliable real-time window into GPU activity. This is generally true, but the polling interval matters. A single snapshot at 4% utilization could theoretically miss a burst of GPU activity that occurred between checks. The assistant implicitly trusts that the GPU-bound phase has not yet begun, and the sustained low utilization across subsequent checks (visible in the following message at index 225, which shows the same 4% after 30 seconds) validates this assumption.

The third assumption is that the pipeline is not deadlocked. The assistant does not check CPU utilization or process state directly. The reasoning is that if the GPU is at 4% and the process hasn't crashed, the CPU must be doing work. This is a reasonable inference given the known architecture, but it is an inference nonetheless.

Input Knowledge Required to Understand This Message

To interpret this message fully, one must understand several layers of the system. The Groth16 proving pipeline must be understood as a two-phase process: CPU synthesis followed by GPU computation. The supraseal backend must be understood as a CUDA-native implementation that delegates to nvcc-compiled kernels through the sppark library, producing no progress output. The cuzk daemon architecture must be understood as a persistent process that holds SRS parameters in memory across proof submissions, as opposed to the original Curio model of spawning a fresh process for each proof. The nvidia-smi tool must be understood as a diagnostic interface to the NVIDIA driver that reports instantaneous utilization, memory allocation, and power state.

Without this context, the message reads as a trivial status check. With it, the message becomes a critical validation point — the first real proof has not crashed, the GPU is recognized and accessible, the SRS has loaded successfully, and the pipeline is progressing through its expected phases.

Output Knowledge Created

This message produces several pieces of knowledge that shape subsequent development. First, it confirms that the cuzk daemon can successfully submit a real PoRep C2 proof and that the supraseal CUDA backend initializes correctly on an RTX 5070 Ti (Blackwell architecture, compute capability 12.0). This was not a foregone conclusion — earlier in the session, the assistant investigated whether the CUDA fatbin kernels in ec-gpu-gen and rust-gpu-tools supported sm_120, and whether the sppark build system would JIT-compile correctly for the new architecture. The fact that the GPU is recognized and executing kernels validates those build decisions.

Second, the message establishes a baseline for the CPU synthesis phase duration. The assistant now knows that the proof takes significant time in CPU-bound synthesis before the GPU becomes active. This observation directly informs later optimization proposals, particularly the analysis of bellperson's synthesis hotpath and the investigation of constraint-structure-aware precomputation.

Third, the message reveals the GPU memory baseline. With 1690 MiB consumed before the heavy GPU computation begins, the assistant can estimate the peak memory footprint when the SRS and working state are fully loaded. This feeds into the memory accounting work done in earlier segments.

The Thinking Process Visible in the Reasoning

The assistant's reasoning is visible in the structure of the diagnostic. The opening phrase — "The proof is computing (no new log lines means the supraseal prover is running GPU kernels silently)" — reveals a chain of inference. The absence of log output is the observed fact. The explanation is that the GPU kernels are silent. The conclusion is that the proof is computing. Each link in this chain is an implicit hypothesis tested against the assistant's knowledge of the system.

The decision to check GPU utilization rather than CPU utilization or process status is itself a reasoning choice. The assistant could have run ps to verify the process is alive, or checked /proc for CPU time, or examined the daemon's internal metrics endpoint. The choice of nvidia-smi reflects a mental model in which the GPU is the critical and opaque component — the CPU synthesis is well-understood and instrumented by bellperson's logging, but the GPU phase is a black box. The assistant is probing the black box.

The query format — --query-gpu=utilization.gpu,utilization.memory,memory.used,memory.total,temperature.gpu,power.draw --format=csv — is also revealing. The assistant requests a broad set of metrics in a machine-readable format, suggesting an intent to parse and compare these values across multiple monitoring iterations. This is not a one-off check; it is the first sample in a monitoring loop that will continue across subsequent messages.

The Broader Significance

This message matters because it represents the transition from "it compiles" to "it computes." The cuzk project, across five segments of development, had built a gRPC API, a priority scheduler, a prover module wrapping filecoin-proofs-api, and a configuration system. But until this moment, the pipeline had only been tested with synthetic inputs or in unit tests. The 51 MB C1 file from a real 32 GiB sector is the first genuine workload, and the assistant's cautious monitoring reflects the weight of this validation.

The low GPU utilization also plants a seed for the optimization work that follows. In later segments, the assistant will investigate the CPU synthesis hotpath in bellperson, identify 18 specific micro-optimizations, design a Pre-Compiled Constraint Evaluator, and ultimately propose a complete pipelined proving architecture. All of this traces back to the observation in this message: the GPU is waiting on the CPU. The bottleneck is synthesis, not computation.

Conclusion

Message 224 is a diagnostic snapshot that, in six lines of nvidia-smi output, captures the state of a complex distributed proving system at a critical juncture. It reveals the assistant's mental model of the pipeline, the assumptions about silent GPU kernels, the inference chain from absent logs to active computation, and the baseline performance characteristics that will drive weeks of optimization work. It is a reminder that in systems engineering, the most valuable observations often come not from elaborate instrumentation but from a simple question: is the hardware doing what we expect?