The Moment of Proof: Measuring SRS Residency in a Filecoin Proving Daemon

In the arc of building a production-grade SNARK proving engine, there comes a moment when theory meets reality. The first proof works. The architecture is validated. But the truly meaningful measurement — the one that separates a toy prototype from a system designed for economic deployment — is the second proof. Message [msg 233] captures this exact transition: the assistant, having just watched a 116.8-second PoRep C2 proof complete successfully on an RTX 5070 Ti, immediately submits the same proof again to measure the SRS residency benefit. This single message, only a few lines long, is the culmination of weeks of architectural analysis, optimization proposal writing, and implementation effort distilled into a single bash command.

The Context: What Came Before

To understand why this message matters, one must appreciate the journey that preceded it. The cuzk project (short for "CUDA ZK") was born from a deep-dive investigation into the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin Proof-of-Replication (PoRep). Earlier segments of this conversation had mapped the full call chain from Curio's Go task layer through Rust FFI into C++/CUDA kernels, accounting for the ~200 GiB peak memory footprint and identifying nine structural bottlenecks. From that analysis, five optimization proposals emerged, including the Persistent Prover Daemon concept that became cuzk.

The core insight behind the Persistent Prover Daemon was simple but powerful: in the existing architecture, every C2 proof generation started by loading the 45 GiB Structured Reference String (SRS) parameters from disk into GPU memory. This ~15-second overhead was incurred on every single proof, even when proofs were submitted back-to-back. By keeping the daemon alive and maintaining the SRS in a GROTH_PARAM_MEMORY_CACHE, the second and subsequent proofs could skip this load entirely. The expected benefit was a 10–15% improvement in wall-clock time for consecutive proofs — a significant gain in a throughput-sensitive market where proving time directly translates to operational cost.

The Message Itself: What Was Said

The message is deceptively brief:

Now let's measure the SRS residency benefit. The SRS is already cached in GROTH_PARAM_MEMORY_CACHE from the first proof. The second proof should skip the ~15s SRS load entirely. Let me submit the same proof again:

The assistant then executes a bash command that submits the same 51 MB C1 output (/data/32gbench/c1.json) to the daemon via the cuzk-bench CLI tool, capturing the process ID for monitoring. The output confirms the second proof started at 04:35:18 PM CET, with PID 3155140.

On its surface, this looks like a routine test repetition. But the subtext is everything. The assistant is not just running a test — it is testing a hypothesis that was formulated during the architectural design phase, implemented through careful engineering, and now being validated with real hardware. The phrase "let's measure the SRS residency benefit" signals a shift from validation to characterization. The first proof answered "does it work?" The second proof answers "how much better does it work?"

The Reasoning and Motivation

The motivation for this message is rooted in the economic reality of Filecoin proving. Storage providers run proof generation continuously — every sector must be proven within its proving deadline, and the cost of proving directly affects profitability. A 15-second overhead per proof, when amortized across thousands of proofs per day, represents real operational expense. The Persistent Prover Daemon architecture was specifically designed to eliminate this recurring cost by keeping the SRS resident in memory across proof submissions.

But the hypothesis needed experimental validation. The 15-second SRS load was a known quantity from the first proof's logs, but the actual benefit could differ due to secondary effects. Would the GPU memory cache be correctly retained across gRPC calls? Would the filecoin-proofs-api library's internal caching mechanisms interact correctly with the daemon's lifecycle? Would the CPU synthesis phase benefit from warm caches as well? These questions could only be answered by running the second proof and measuring the result.

The assistant's reasoning, visible in the message's framing, demonstrates a clear understanding of the system's behavior. The statement "The SRS is already cached in GROTH_PARAM_MEMORY_CACHE from the first proof" reflects knowledge of how filecoin-proofs manages parameter caching — specifically, that the storage-proofs-core library uses a lazy_static SETTINGS object initialized from FIL_PROOFS_* environment variables, and that the GROTH_PARAM_MEMORY_CACHE is a process-global cache that persists for the lifetime of the daemon process. The assistant had verified this by reading the source code of storage-proofs-core and filecoin-proofs in earlier messages ([msg 212] through [msg 217]), tracing the exact path from environment variable to cache initialization.

Decisions Made in This Message

Although the message appears to be a simple test execution, several implicit decisions are embedded within it:

Decision 1: Measure immediately. Rather than moving on to Phase 1 planning or adding polish features, the assistant prioritized measurement while the daemon was still running and the SRS was hot. This decision reflects an experimental mindset — capture the data point now, while conditions are favorable, rather than risk having to restart the daemon and lose the cached state.

Decision 2: Use the same input. By submitting the identical C1 output (/data/32gbench/c1.json), the assistant ensures a fair comparison. Any variation in proof time would be attributable to the daemon's state (SRS cache, CPU cache warmness) rather than differences in the circuit or witness data.

Decision 3: Run as a background process. The & at the end of the bash command, combined with capturing BENCH2_PID, allows the assistant to monitor progress without blocking the interactive session. This is a practical decision — the proof would take ~90 seconds, and blocking would prevent the assistant from inspecting logs or checking GPU utilization during the run.

Decision 4: Trust the daemon's correctness. The assistant does not re-verify the daemon's configuration or check that the SRS cache is populated before submitting. This trust is earned from the first proof's successful completion and the log evidence showing "found params in memory cache for STACKED[34359738368]-verifying-key."

Assumptions Made

The message operates on several assumptions, most of which are well-founded but worth examining:

Assumption 1: The GROTH_PARAM_MEMORY_CACHE persists across gRPC calls. This is correct — the cache is a process-global HashMap inside the filecoin-proofs library, initialized on first access and never cleared. As long as the daemon process continues running, the cache remains populated.

Assumption 2: The second proof will skip the SRS load entirely. This is the central hypothesis being tested. The assistant correctly predicts that the "trying parameters memory cache for: STACKED[34359738368]" log line will find a hit rather than a miss.

Assumption 3: The daemon's internal state is clean. After the first proof, the daemon has completed verification and returned the result. The assistant assumes no residual locks, memory leaks, or corrupted state that would affect the second proof.

Assumption 4: The GPU is ready for another proof. The first proof used the RTX 5070 Ti's CUDA cores extensively. The assistant assumes the GPU has returned to an idle state and that GPU memory (which grew to ~3822 MiB during the first proof) is properly managed.

Assumption 5: The proving time difference will be attributable primarily to SRS loading. This assumption acknowledges that other factors (CPU cache warmness, disk cache, GPU temperature) may contribute, but the dominant effect should be the eliminated SRS load.

Input Knowledge Required

To understand this message fully, one needs:

  1. Knowledge of the cuzk architecture — that it's a persistent gRPC daemon wrapping filecoin-profs-api calls, designed to keep SRS parameters resident across proof submissions.
  2. Understanding of Groth16 proving for Filecoin PoRep — that C2 proof generation involves loading ~45 GiB of SRS parameters (structured reference strings for the elliptic curve group operations), followed by CPU-bound circuit synthesis (~130 million constraints for 32 GiB sectors) and GPU-bound NTT/MSM computation.
  3. Familiarity with the GROTH_PARAM_MEMORY_CACHE mechanism — that filecoin-proofs maintains an in-memory cache of loaded parameters, keyed by proof type and sector size (e.g., STACKED[34359738368]), and that this cache is process-global.
  4. Awareness of the first proof's results — specifically the 116.8-second total time with ~15 seconds attributed to SRS loading from disk.
  5. Knowledge of the test environment — an RTX 5070 Ti (Blackwell architecture, sm_120, CUDA 13.1), 32 GiB PoRep circuit, and the daemon running with FIL_PROOFS_PARAMETER_CACHE=/data/zk/params.

Output Knowledge Created

This message, combined with the subsequent monitoring in [msg 235] and [msg 236], produces several valuable knowledge artifacts:

  1. Quantified SRS residency benefit: The second proof completed in 92.8 seconds versus 116.8 seconds for the first — a 20.5% improvement. This exceeded the ~15% expectation based purely on the eliminated SRS load, suggesting secondary benefits from warm CPU caches.
  2. Validation of the Persistent Prover Daemon architecture: The core design hypothesis was confirmed. Keeping the daemon alive across proof submissions provides a measurable, significant performance advantage.
  3. Baseline performance data: The 92.8-second warm-prove time for a 32 GiB PoRep on an RTX 5070 Ti establishes a performance baseline for future optimization work. This data point is critical for evaluating subsequent proposals like Sequential Partition Synthesis or Cross-Sector Batching.
  4. Confidence in system correctness: Both proofs produced identical 1920-byte Groth16 proofs that passed internal verification. The daemon correctly tracked metrics (2 proofs completed, 0 failed), and the Prometheus instrumentation functioned as designed.
  5. Evidence for economic modeling: The 20.5% improvement directly translates to reduced proving costs for storage providers. In a market where proving time is a scarce resource, this validates the investment in the persistent daemon architecture.

The Thinking Process Visible

The assistant's thinking process, while not explicitly rendered in a separate reasoning section, is visible in the structure and timing of the message. The progression from "the first proof worked" to "now measure the benefit" reveals a methodical, hypothesis-driven approach to engineering. The assistant does not celebrate the first proof's success and move on; it immediately pivots to extracting maximum information from the running system.

The choice of words — "let's measure the SRS residency benefit" — frames the action as an experiment rather than a test. The assistant is acting as a scientist of its own system, designing a controlled comparison (same input, same hardware, different daemon state) to isolate a specific variable. The parenthetical explanation — "The SRS is already cached in GROTH_PARAM_MEMORY_CACHE from the first proof. The second proof should skip the ~15s SRS load entirely." — serves both as documentation for the human collaborator and as an explicit statement of the hypothesis being tested.

The bash command itself reveals practical thinking: the echo with a timestamp provides a clear marker in the log stream, the & backgrounding enables non-blocking monitoring, and the BENCH2_PID capture allows the assistant to track the process. These are the hallmarks of an engineer who has debugged enough distributed systems to know that observability is not optional.

Conclusion

Message [msg 233] is a masterclass in efficient experimental design within a live coding session. In just a few lines, the assistant transitions from validation to characterization, tests a core architectural hypothesis, and sets the stage for the quantitative comparison that would follow. The 20.5% improvement measured in the subsequent messages ([msg 235], [msg 236]) validated months of analysis and design work, proving that the Persistent Prover Daemon architecture was not just theoretically sound but practically beneficial on real hardware.

This message also exemplifies a broader engineering principle: the most important measurement is often the second run. The first run proves correctness; the second run proves improvement. By instinctively reaching for that second measurement, the assistant demonstrated an understanding that in performance engineering, a single data point is just an anecdote — it takes two to make a comparison.