The Hardening of cuzk: From First Real Proof to Production-Ready Observability

Introduction

Every complex distributed system crosses a critical threshold at some point in its development: the transition from "it compiles and the pipeline works" to "it produces real results with measurable characteristics and is instrumented for debugging the next wave of complexity." For the cuzk pipelined SNARK proving daemon — a persistent gRPC service generating Groth16 proofs for Filecoin's Proof-of-Replication (PoRep) across GPU-accelerated hardware — this threshold was crossed in a single concentrated engineering session spanning messages 192 through 282 of a larger coding conversation.

This segment tells a story in two acts. The first act is validation: building the daemon with GPU acceleration on an RTX 5070 Ti (Blackwell architecture, CUDA 13.1), submitting a real 51 MB PoRep C1 output, and watching the full proving pipeline execute — from gRPC transfer through JSON parsing, base64 decoding, 45 GiB SRS parameter loading, Groth16 circuit synthesis, GPU NTT/MSM computation, and finally proof verification. The first proof completed in 116.8 seconds. A second proof, with SRS parameters already resident in memory, completed in 92.8 seconds — a 20.5% improvement that validated the central architectural thesis of the cuzk design.

The second act is hardening. When given the directive to focus on "all things which will make Phase 1 better grounded and easier to debug quickly" ([msg 250]), the assistant systematically instrumented every layer of the system. Tracing spans with job_id correlation ensure that every log line from the upstream filecoin-proofs library is tagged per-job. Timing breakdowns separate deserialization from proving time. Per-proof-kind Prometheus counters and duration summaries make performance data queryable. GPU detection via nvidia-smi is surfaced in the status response. The AwaitProof RPC was fixed to support late listeners. Graceful shutdown was implemented via a watch channel. A cuzk-bench batch command enables sequential and concurrent throughput measurement. A sample config file (cuzk.example.toml) documents the configuration surface.

Two git commits checkpointed the scaffold and hardening phases on the feat/cuzk branch, creating clean, revertible milestones. This article examines both acts in detail, extracting the engineering principles that guided each decision and the methodology that made the work effective.

Act I: The Moment of Truth — Validating Real Proofs

The Pre-Flight Protocol

Before the first real proof could be attempted, the assistant executed a structured pre-flight check ([msg 195]). Four bash commands verified every prerequisite in a deliberate order that reveals the assistant's risk assessment methodology:

  1. Parameter files: The 45 GiB PoRep parameter file was confirmed present at /data/zk/params/ alongside 28 other parameter files. This was the highest-risk item — the files had been manually copied from a previous location, and a missing parameter file would cause an opaque failure deep in the proving pipeline.
  2. Test data: The 51 MB C1 output at /data/32gbench/c1.json was confirmed present and its size verified. This was a known golden file from earlier testing.
  3. Build: cargo check --workspace --no-default-features completed in 0.26 seconds with zero errors and zero warnings. The workspace had been compiled before, so this was a quick verification that the source tree was consistent.
  4. GPU: nvidia-smi confirmed the RTX 5070 Ti was available with CUDA 13.1 and driver 590.48.01, with 16 GiB of VRAM. Each check eliminates a specific failure mode. If the proof failed, the debugging effort could focus on the pipeline itself rather than on missing files or build errors. This is a small but significant example of disciplined engineering: verify the environment before running the experiment.

The Blackwell Compatibility Investigation

A critical subplot in this validation arc was the investigation into whether the CUDA backends would support the RTX 5070 Ti's Blackwell architecture (sm_120, compute capability 12.0). This was not a trivial concern. The assistant discovered that rust-gpu-tools (used by bellperson's native CUDA path) relied on precompiled fatbin kernels that might not include sm_120 code paths, while supraseal-c2 delegated to nvcc for JIT compilation, which would automatically target the detected architecture ([msg 201]-[msg 203]).

The investigation was thorough: reading build scripts, examining sppark's build.rs, and tracing through the feature flag chain from cuzk-core through filecoin-proofs-api to supraseal-c2. The decision to proceed with --features cuda-supraseal was grounded in the understanding that nvcc's JIT compilation would handle Blackwell compatibility transparently. The subsequent successful build — a 21 MB daemon binary with zero errors and zero warnings — validated this decision ([msg 205]).

This investigation exemplifies a broader engineering principle: when facing uncertainty about a dependency's behavior, read the source code rather than making assumptions. The assistant did not ask "will this work?" — the assistant traced the actual code paths to determine the answer.

The First Real Proof

With the build verified and the GPU backend confirmed compatible, the assistant launched the daemon and submitted the first real proof. The moment of submission was marked with appropriate ceremony: the assistant enumerated the six steps the daemon would need to execute — gRPC transfer of 51 MB, outer JSON parsing and base64 decode, loading 45 GiB SRS params from disk, Groth16 circuit synthesis, NTT/MSM on the GPU, and proof verification — and set an estimated timeline of 10-60+ minutes for the first call due to SRS loading ([msg 220]).

The monitoring strategy that followed was methodical and multi-signal. The assistant checked:

The SRS Residency Measurement

The true architectural validation came with the second proof. The assistant submitted the same C1 input to the same running daemon and observed the critical log line: found params in memory cache for STACKED[34359738368] instead of no params in memory cache. The SRS cache had persisted across gRPC calls, exactly as the architecture intended.

The second proof completed in 92.8 seconds — a 20.5% improvement over the cold start. But the assistant noted something curious: the improvement (24 seconds) exceeded the expected benefit from eliminating the 15-second SRS load alone. The additional 9 seconds of improvement suggested secondary warm-cache effects: CPU caches benefiting from the first run's synthesis phase, memory allocation patterns stabilizing, and possibly lazy initialization completing during the first proof.

This observation demonstrates analytical rigor — not just measuring the headline number, but comparing expected versus observed results and offering hypotheses for the discrepancy. The 20.5% improvement validated the central thesis of the cuzk architecture: that a persistent proving daemon keeping SRS parameters resident in memory across proof submissions would yield measurable performance benefits. This was not a theoretical projection; it was an empirical measurement on real hardware with a real proof.

Phase 0 Validation Complete

The assistant formally declared Phase 0 validated at [msg 239], presenting a concise summary: two proofs completed, zero failed, Prometheus metrics and status RPC functional, SRS residency benefit confirmed at 20.5%. The remaining polish items — timing breakdown logging and a batch benchmark command — were framed as quality-of-life improvements rather than correctness blockers, signaling that the core functionality was complete.

Act II: The Hardening Sprint — Building Observability for Phase 1

The Strategic Pivot

The validation success could have led directly to Phase 1's multi-GPU and multi-proof-type work. Instead, the user's directive at [msg 250] — "Continue with phase 0, all things which will make phase 1 better grounded and easier to debug quickly" — triggered a deliberate pivot to hardening. This decision reflects a sophisticated understanding of engineering economics: the cost of adding observability now is measured in hours; the cost of debugging a concurrent multi-GPU system without it could be measured in days or weeks.

The assistant's response at [msg 251] was immediate and structured. A todo list enumerated eight high-priority items, each explicitly linked to the Phase 1 debugging goal:

  1. Detailed timing breakdown logging — separating deserialization, SRS lookup, synthesis, GPU computation, and verification into distinct measured phases
  2. RUST_LOG-based trace logging at key boundaries — enabling fine-grained debugging without code changes
  3. cuzk-bench batch command — for sequential and concurrent throughput measurement
  4. Per-proof-type Prometheus counters and duration summaries — making performance data queryable
  5. GPU detection via nvidia-smi — surfaced in the daemon status response
  6. Fix AwaitProof RPC for late listeners — ensuring robustness when clients connect after proof completion
  7. Graceful shutdown via watch channel — clean teardown without orphaned processes
  8. Sample config file — cuzk.example.toml for easy deployment The ordering of this list is not arbitrary. Timing breakdown comes first because it is the foundational observability improvement — without knowing where time is spent, no other optimization makes sense. Trace logging comes second because it enables debugging of correctness issues orthogonal to performance. The batch command comes third because it enables performance measurement, which requires the timing breakdown to be meaningful. Per-kind metrics come fourth because they enable operational monitoring once multiple proof types exist. The remaining items — GPU detection, AwaitProof fix, graceful shutdown, sample config — are polish that makes the system production-ready.

The Bottom-Up Implementation Strategy

The assistant's implementation strategy for the hardening work was notably systematic: work from the innermost layer outward through the dependency graph. The sequence was:

  1. types.rs ([msg 254]): Define the data structures — ProofTiming with fields for queue_wait, deserialize, srs_lookup, gpu_compute, and verify, plus a ProofStats struct for per-kind counters. By defining the structures first, the assistant established a contract that all other layers would share.
  2. prover.rs ([msg 255]): Implement the actual timing instrumentation inside the proof execution path, adding tracing spans with job_id correlation. Every log line emitted by the upstream filecoin-proofs library would now be tagged with the originating job's unique ID.
  3. engine.rs ([msg 256]): Thread the job_id through to the prover, fix the AwaitProof RPC to support late listeners, add per-kind proof statistics tracking, and implement graceful shutdown via a watch channel. This is the central coordination point — the engine that holds the entire system together.
  4. service.rs ([msg 257]): Expose all the new instrumentation through the gRPC API and Prometheus metrics endpoint. Map the ProofTiming structure into the gRPC response, expose per-kind counters and duration summaries, and add GPU information to the status response.
  5. bench/src/main.rs ([msg 258]): Add the batch command for sequential and concurrent throughput measurement, completing the tooling layer. This bottom-up approach minimizes the risk of inconsistencies. Each layer's interface is grounded in what the layer below can actually deliver. The service layer cannot expose a timing field that the prover doesn't measure; the engine cannot aggregate stats that the types don't define.

The Tracing Span Architecture

Perhaps the most architecturally significant decision in the hardening sprint was the choice to use the tracing crate's span mechanism for job_id correlation. The assistant created a parent span at the engine level (gpu_worker{job_id=..., proof_kind=...}) and a child span in the prover (prove_porep_c2{job_id="..."}). Because the upstream filecoin-proofs crate also uses the tracing crate, its internal log lines would inherit the parent span's context — meaning every log from the FFI boundary, the parameter cache, and the GPU backend would be automatically tagged with the originating job's ID.

This was validated at [msg 272], where the assistant observed: "Every log line from the proof — including the filecoin-proofs internal logs — is now prefixed with prove_porep_c2{job_id=\"c1a0d8a5-...\"}." For debugging concurrent proofs in Phase 1, this is indispensable. Without it, log lines from different jobs would be interleaved and indistinguishable.

The beauty of this approach is its leverage. The tracing crate's span propagation is automatic — once the span is entered, any code that runs within it (including deep library code that knows nothing about cuzk) inherits the span context. This means the upstream filecoin-proofs library, storage-proofs-core, bellperson, and even the CUDA runtime logs all get tagged with the job_id without any modifications to those libraries. It is a zero-touch instrumentation strategy that extracts maximum value from a minimal code change.

The Timing Breakdown Validation

The hardening work was validated with another real proof at [msg 271]-[msg 273]. The timing breakdown revealed:

| Phase | Duration | |-------|----------| | Queue wait | 32 ms | | Deserialization | 172 ms | | Proving (SRS + synthesis + GPU + verify) | 109,993 ms | | Total | 110,198 ms |

This granularity — impossible before the hardening — immediately revealed that deserialization of the 51 MB C1 JSON was taking only 172 milliseconds, a negligible fraction of the total. The proving phase dominated at 110 seconds, and within that monolithic block, the assistant knew from earlier analysis that CPU synthesis (building ~130 million constraints) was the primary consumer. This knowledge would be essential for Phase 1 optimization decisions.

The per-kind Prometheus metrics were also confirmed working, with cuzk_proofs_completed{proof_kind="porep_c2"} 1 and cuzk_proof_duration_seconds_sum{proof_kind="porep_c2"} 110.198 appearing in the metrics output. The Prometheus exposition format was valid and parseable, meaning the daemon could be integrated into existing monitoring infrastructure.

The AwaitProof Fix and Graceful Shutdown

Two correctness fixes in the hardening sprint deserve special attention because they address subtle failure modes that would become critical in Phase 1.

The AwaitProof RPC was originally implemented as a simple lookup: if the proof result was already in the completed map, return it; otherwise, return a 404 error. This meant that a client that connected after the proof completed — perhaps due to a network retry or a reconnection — would never receive the result. The fix registered a late listener: if the proof was already completed, the result was returned immediately; if the proof was still in progress, the listener was added to a notification channel that would fire when the proof completed. This is a textbook observer pattern implementation, and it ensures that clients can reliably receive proof results regardless of when they connect.

The graceful shutdown mechanism used a watch channel — a tokio watch::Receiver that signals when shutdown is requested. When the daemon receives a SIGINT or SIGTERM, it sets the watch to true, which causes the engine to drain the queue (rejecting new submissions with a "shutting down" error) and finish the current in-progress proof before exiting. Without this mechanism, killing the daemon during a proof would orphan the GPU computation, potentially leaving the GPU in an inconsistent state and wasting the work already done.

The Git Discipline

Throughout this chunk, the assistant demonstrated disciplined git hygiene. The Phase 0 scaffold was committed at [msg 247] with a detailed commit message enumerating the workspace structure, the gRPC API, the real PoRep C2 proving, the SRS residency mechanism, and the priority scheduler. The hardening work was committed separately at [msg 278] with a message that captured the full scope of improvements: tracing spans, timing breakdowns, per-kind metrics, GPU detection, AwaitProof fix, graceful shutdown, and the batch command.

The commit message for the hardening commit is worth quoting in full:

feat(cuzk): Phase 0 hardening — observability, batch bench, AwaitProof fix

Improve the cuzk daemon's debuggability and operational readiness
for Phase 1 multi-GPU work:

Observability:
- Add tracing spans (info_span) with job_id correlation throughout
  prover and engine; upstream filecoin-proofs logs now tagged per-job
- Split timing into deserialize vs proving (monolithic in Phase 0)
- Per proof-kind Prometheus counters and duration summaries
- GPU detection via nvidia-smi in GetStatus RPC (name, VRAM)
- Running job info shown in status and annotated on GPU

Correctness:
- Fix AwaitProof to register late listeners (was broken, always 404)
- Graceful shutdown via watch channel (drain, finish current proof)
- Per-kind completed/failed counters with ring buffer for durations

Tooling:
- Add 'batch' command to cuzk-bench (sequential + concurrent modes,
  throughput stats with avg/min/max/proofs-per-min)
- Refactor bench client connection into shared connect() helper
- Add cuzk.example.toml with documented configuration

E2E validated: 32GiB PoRep C2 proof completes in ~110s with full
job_id-correlated logging and per-kind metrics.

This checkpoint discipline creates clean, revertible milestones. If the hardening changes introduce a regression, the developer can bisect to exactly the validated working state. The commit messages themselves serve as documentation, capturing the architectural intent at each stage.

Themes and Engineering Principles

Several overarching themes emerge from this segment of work:

1. Validate Before Optimize

The assistant did not attempt to optimize the proving pipeline before establishing that it worked correctly. The first proof was a binary pass/fail test — does the pipeline produce a valid Groth16 proof? Only after that was confirmed did the assistant measure performance and then harden the system. This is a fundamental engineering principle: correctness first, then performance, then observability.

2. Observability as a First-Class Concern

The hardening sprint treated observability not as an afterthought but as a deliberate design investment. Every layer of the system — from the innermost data structures to the outermost API boundary — was instrumented with tracing spans, timing measurements, and metrics. The explicit criterion for each change was "will this make Phase 1 easier to debug?" This forward-looking approach recognizes that the cost of adding observability is linear in the present, but the cost of debugging without it is exponential in future complexity.

3. Systematic Methodology

Both the validation and hardening phases were executed with remarkable methodological rigor. The pre-flight check eliminated failure modes systematically. The SRS residency measurement used identical inputs to isolate the cache benefit. The hardening work proceeded bottom-up through the dependency graph. The monitoring strategy used multiple signals (log, GPU utilization, CPU usage, memory footprint) to build a complete picture of system behavior. This is not ad-hoc hacking; it is disciplined engineering.

4. Empirical Grounding

Every decision in this chunk was grounded in empirical evidence. The Blackwell compatibility investigation read actual build scripts rather than making assumptions. The SRS residency benefit was measured, not estimated. The timing breakdown was validated with a real proof. The tracing span propagation was confirmed by observing the log output. This commitment to empirical validation distinguishes the work from theoretical design exercises.

5. Strategic Prioritization

The assistant consistently made strategic tradeoffs. The decision to use supraseal-c2 over bellperson's native CUDA path was based on Blackwell compatibility. The decision to accept the monolithic seal_commit_phase2 timing rather than attempting to split it was a pragmatic concession to available instrumentation points. The decision to instrument the service layer with Prometheus metrics rather than building a custom dashboard integrated with existing infrastructure. Each decision reflects an understanding of the project's constraints and priorities.

Conclusion

This segment of the cuzk coding session represents a complete arc from validation to hardening — from "it compiles and the pipeline works" to "it produces real proofs with measurable performance characteristics and is instrumented for debugging the concurrent multi-GPU work coming in Phase 1." The first act demonstrated that the core architecture was sound: a persistent proving daemon with SRS memory residency produced valid Groth16 proofs with a 20.5% speedup on the second run. The second act transformed that working prototype into an instrumented platform: tracing spans, timing breakdowns, per-kind metrics, GPU detection, robust RPC semantics, graceful shutdown, and throughput measurement tools.

The engineering principles visible in this work — validate before optimizing, invest in observability, work systematically, ground decisions empirically, prioritize strategically — are not specific to SNARK proving or GPU computing. They are universal principles of building complex systems that must be correct, performant, and maintainable. The cuzk daemon is not finished at the end of this segment, but it has crossed the most important threshold: from "it works" to "we can understand why it works and debug it when it doesn't." That is the foundation on which Phase 1's multi-GPU, multi-proof-type future will be built.

References