The Quiet Validation: How a Prometheus Metrics Check Confirmed the cuzk Proving Daemon's Readiness

In the middle of a high-stakes validation session for the cuzk proving daemon—a custom-built, gRPC-based engine for generating Filecoin PoRep Groth16 proofs on GPU hardware—the assistant issued a seemingly mundane command. After hours of building, debugging, and waiting through two full proof generations consuming over 200 GiB of memory each, it typed:

/home/theuser/curio/extern/cuzk/target/release/cuzk-bench -a http://127.0.0.1:9820 metrics

The response was a handful of Prometheus-format metric lines:

# HELP cuzk_proofs_completed_total Total proofs completed
# TYPE cuzk_proofs_completed_total counter
cuzk_proofs_completed_total 2
# HELP cuzk_proofs_failed_total Total proofs failed
# TYPE cuzk_proofs_failed_total counter
cuzk_proofs_failed_total 0
# HELP cuzk_uptime_seconds Daemon uptime
# TYPE cuzk_uptime_seconds gauge
cuzk_uptime_seconds 333
# HELP cuzk_queue_depth Current queue depth
# TYPE cuzk_queue_depth gauge
cuzk_queue_depth 0

This is message <msg id=237> in the conversation. On its surface, it is nothing more than a health check—a developer querying a running service to confirm it is instrumented correctly. But in the arc of this session, this message represents something far more significant: the moment when the Phase 0 implementation of the cuzk proving daemon crossed the threshold from "it compiles and the pipeline works" to "it produces real proofs with measurable performance characteristics and is instrumented for debugging." It is the quiet capstone of a validation journey that began with a 51 MB C1 output file and ended with a 20.5% speedup measurement and a working Prometheus metrics endpoint.

The Road to This Message

To understand why this metrics query matters, one must understand what preceded it. The assistant had spent the prior hour executing a meticulous end-to-end validation of the cuzk proving pipeline on an RTX 5070 Ti (Blackwell architecture, CUDA 13.1). The journey began with building the workspace with --features cuda-supraseal to enable GPU acceleration, then starting the daemon with FIL_PROOFS_PARAMETER_CACHE=/data/zk/params to point it at the 32 GiB parameter cache directory.

The first proof submission (see <msg id=220>) was the moment of truth. A 51 MB JSON-encoded C1 output—the intermediate result from Phase 1 of the Filecoin proof-of-replication protocol—was sent over gRPC to the daemon. The daemon deserialized it, detected a cache miss on the SRS parameters (STACKED[34359738368]), and began loading the 45 GiB parameter file from disk. This took approximately 15 seconds, after which the Supraseal CUDA backend kicked in and began the Groth16 proof generation. The first proof completed in 116.8 seconds, producing a valid 1920-byte BLS12-381 Groth16 proof that passed internal verification.

But the critical insight came from the second proof (see <msg id=233>). With the SRS parameters already resident in the GROTH_PARAM_MEMORY_CACHE, the daemon skipped the disk load entirely. The second proof completed in 92.8 seconds—a 20.5% improvement from SRS residency alone. This validated the core architectural bet of the cuzk design: by keeping the SRS parameters pinned in memory across proof submissions, the daemon could eliminate one of the nine structural bottlenecks identified in the earlier optimization proposals.

Why This Metrics Check Matters

After confirming that both proofs completed successfully and that the daemon's status endpoint reported proofs completed: 2, the assistant could have declared victory and moved on. Instead, it queried the Prometheus metrics endpoint. This decision reveals a deeper priority: the assistant was not merely validating that proofs could be generated, but that the daemon was production-ready in its observability.

The metrics output confirms several things simultaneously:

First, the Prometheus instrumentation is correctly wired. The cuzk_proofs_completed_total counter reads 2, matching the two successful proof submissions. The cuzk_proofs_failed_total counter reads 0, confirming no failures. These numbers must be correct for the metrics to be useful in production—if they diverged from reality, operators would lose trust in the monitoring data.

Second, the queue depth is zero. The cuzk_queue_depth gauge reading 0 indicates that the daemon has drained its work queue completely. Both proofs were submitted, processed, and returned without backlog. This is a health signal: the single GPU worker is keeping up with demand, and there are no stuck jobs.

Third, the uptime is consistent. The cuzk_uptime_seconds gauge reads 333 seconds, which aligns with the elapsed wall-clock time since the daemon started at 15:31:48 and the metrics query at approximately 15:37:21. This confirms the daemon hasn't restarted or crashed unexpectedly.

Fourth, the metrics are accessible over the network. The cuzk-bench metrics command successfully connected to http://127.0.0.1:9820 and retrieved the Prometheus-formatted output. This validates that the gRPC server's metrics endpoint is properly exposed and that the Prometheus exposition format is correctly implemented.

The Deeper Significance: Hardening for Phase 1

This metrics check was the final step in what the assistant described as "hardening Phase 0 with several improvements to make Phase 1 development easier to debug." The hardening additions included tracing spans with job_id correlation, timing breakdowns separating deserialization from proving time, per proof-kind Prometheus counters and duration summaries, GPU detection via nvidia-smi, a fixed AwaitProof RPC supporting late listeners, graceful shutdown via a watch channel, and a cuzk-bench batch command for throughput measurement.

The Prometheus metrics system is the observability backbone that will support Phase 1's concurrent multi-GPU operation. Without it, diagnosing performance issues, queue buildup, or GPU failures across multiple workers would be nearly impossible. The assistant's decision to verify the metrics endpoint—even after the status endpoint already confirmed the proof counts—demonstrates a commitment to building debugging infrastructure before it is needed, rather than retrofitting it after problems arise.

Input Knowledge Required

To fully appreciate this message, one must understand several layers of context. The first is the Filecoin proof-of-replication (PoRep) protocol, which requires storage providers to periodically generate cryptographic proofs that they are storing assigned data. These proofs involve Groth16 zk-SNARKs over circuits with approximately 130 million constraints for a 32 GiB sector, requiring enormous computational resources—roughly 200 GiB of peak memory and hundreds of CPU core-minutes for synthesis alone.

The second layer is the Supraseal CUDA backend, which accelerates the NTT (Number Theoretic Transform) and MSM (Multi-Scalar Multiplication) operations that dominate the GPU phase of proof generation. The cuda-supraseal feature flag enables this backend, and its activation is confirmed in the daemon logs with the message "Bellperson 0.26.0 with SupraSeal is being used!"

The third layer is the Prometheus monitoring ecosystem, a standard in cloud-native observability. The metrics are exposed in the Prometheus exposition format (text-based, with # HELP and # TYPE lines), which can be scraped by a Prometheus server or consumed by any compatible monitoring system. The choice of Prometheus reflects an assumption that the cuzk daemon will operate in a modern infrastructure environment with established monitoring practices.

Output Knowledge Created

This message produces several pieces of actionable knowledge:

  1. The metrics system is functional. The daemon correctly exposes counters for completed and failed proofs, a gauge for uptime, and a gauge for queue depth. These are the minimal set of metrics needed to monitor a proving service.
  2. The proof counts are accurate. Two proofs completed, zero failed—matching the manual verification performed via the status endpoint and the daemon logs.
  3. The queue is empty. No proofs are stuck or pending. The system is in a steady, idle state.
  4. The daemon has been running stably for 333 seconds. No crashes, no restarts, no observable degradation.
  5. The gRPC server is correctly serving the metrics endpoint. The cuzk-bench client successfully connected and retrieved the data.

What This Message Does Not Tell Us

It is worth noting what the metrics output does not include. The hardening phase mentioned adding "per proof-kind Prometheus counters and duration summaries," but the metrics shown here only include aggregate counters. The per-proof-kind breakdowns and duration histograms may be exposed on different endpoints or may require additional Prometheus metric names to be queried. Similarly, the GPU detection information (from nvidia-smi) is exposed in the status response rather than the metrics endpoint. This suggests a design where operational metrics (counts, durations) follow the Prometheus standard, while hardware-level details are exposed through a separate status RPC.

The Broader Arc: From Validation to Instrumentation

This message sits at the intersection of two critical phases in the cuzk project. The first phase—validation—proved that the daemon could produce real, verifiable Groth16 proofs using GPU acceleration, and that SRS residency provided a measurable 20.5% speedup. The second phase—hardening—added the observability and debugging infrastructure needed to operate the daemon reliably and to diagnose issues during the more complex Phase 1 development.

The metrics check is the bridge between these phases. It confirms that the hardening work is complete and correct, and it establishes the baseline observability that will support the next stage of development. When Phase 1 introduces concurrent multi-GPU proving, priority scheduling, and dynamic worker pools, the Prometheus metrics will be the first line of defense for understanding system behavior.

Conclusion

Message <msg id=237> is, on its surface, a simple Prometheus metrics query. But in the context of the cuzk proving daemon's development, it represents the culmination of a rigorous validation and hardening process. The assistant did not merely verify that proofs could be generated—it verified that the daemon could be operated, monitored, and debugged in a production-like environment. The metrics output of 2 proofs completed, 0 failed, 333 seconds uptime, and 0 queue depth is the quiet confirmation that Phase 0 is ready, and that the foundation for Phase 1 is solid. In the world of distributed systems and cryptographic proving, observability is not a luxury—it is a necessity. This message proves that the cuzk team understands that principle and has built accordingly.