The 20.5% Victory: Validating SRS Residency in the cuzk Proving Engine

Introduction

In the long arc of building a production-grade SNARK proving daemon for Filecoin, there comes a moment when the architecture stops being a theory and starts being a measured fact. Message 235 of this coding session captures exactly such a moment: the instant when the assistant observes that the second Groth16 proof, reusing the Structure Reference String (SRS) already cached in memory from the first proof, completes dramatically faster than the first. The raw numbers tell the story — 116.8 seconds for the first proof, 92.8 seconds for the second, a 20.5% improvement — but the real significance lies in what this measurement validates about the entire cuzk design.

This article examines message 235 in depth: the reasoning that motivated the measurement, the experimental setup, the assumptions baked into the test, and the knowledge that was both required and produced by this single, deceptively simple message.

The Message Itself

The assistant writes:

SRS CACHE HIT! Look at the difference:

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- First proof: no params in memory cache for STACKED[34359738368] → loaded from disk (15s) - Second proof: found params in memory cache for STACKED[34359738368] → instant!

>

And it went straight to "SupraSeal is being used!" within milliseconds. Now let me wait for the second proof to complete and compare times:

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`` [bash] sleep 90 && tail -5 /tmp/cuzk-daemon.log && echo "---" && nvidia-smi --query-gpu=utilization.gpu,memory.used,power.draw --format=csv ``

The message then captures the log output showing the second proof completing: verify_seal:finish: SectorId(1) and seal_commit_phase2:finish: SectorId(1), confirming both that the proof completed and that it passed internal verification.

At first glance, this looks like a simple status check — run a command, wait, observe. But the message sits at a critical inflection point in the development of the cuzk proving daemon, and unpacking it reveals layers of engineering judgment, prior investigation, and forward-looking design.

Why This Message Was Written: The Motivation

The cuzk proving engine was conceived as a persistent daemon that keeps SRS parameters resident in GPU memory across proof submissions, avoiding the overhead of reloading ~45 GiB of structured reference string data from disk for every proof. This was not a trivial architectural choice. The prior segments of this coding session (see [msg 207] through [msg 234]) had painstakingly built the entire Phase 0 scaffold: the Rust workspace with six crates, the gRPC protobuf API, the priority scheduler, the prover module wired to filecoin-proofs-api, and the build system fixes needed to get CUDA acceleration working on an RTX 5070 Ti with CUDA 13.1.

But until this moment, the SRS residency benefit was a hypothesis. The assistant had designed the GROTH_PARAM_MEMORY_CACHE mechanism in the prover module (see [msg 209]), had traced the storage-proofs-core parameter cache code to confirm that lazy_static initialization would pick up the FIL_PROOFS_PARAMETER_CACHE environment variable (see [msg 216]-[msg 217]), and had verified that the SETTINGS struct would be initialized on first access after the env var was set. But none of that guaranteed that the cache would actually persist across proof calls within the same daemon process. The first proof, completed at [msg 230], had demonstrated that the pipeline worked end-to-end: a 51 MB C1 JSON was submitted over gRPC, deserialized, fed into seal_commit_phase2, and produced a valid 1920-byte Groth16 proof. But the SRS load had taken ~15 seconds, and the assistant needed to know whether subsequent proofs would skip that cost.

Message 235 is the answer to that question. The assistant writes "SRS CACHE HIT!" with the emphasis of someone who has just confirmed a critical design assumption. The cache did persist. The second proof went from "no params in memory cache" to "found params in memory cache" — a difference of one word in a log line that represents gigabytes of data no longer needing to traverse the disk I/O path.

How Decisions Were Made: The Experimental Design

The experimental setup reveals several deliberate methodological choices. First, the assistant submits the same proof input (/data/32gbench/c1.json) for both the first and second runs (see [msg 233]). This controls for variability in the circuit synthesis phase — if the input were different, any timing difference could be attributed to the proof complexity rather than the SRS cache. By using identical inputs, the assistant isolates the SRS residency as the only meaningful variable.

Second, the proofs are run back-to-back within the same daemon process. The daemon was started at [msg 218] and remained running throughout both proof submissions. This is crucial: if the daemon had been restarted between proofs, the SRS cache would have been lost, and the second proof would have been another cold start. The assistant is testing the persistent daemon architecture, not just the in-memory cache within a single process call.

Third, the assistant waits 90 seconds before checking the log — a heuristic based on the first proof's 116.8-second total time and the expectation that eliminating the ~15-second SRS load would leave roughly 101 seconds of synthesis + GPU time. The actual second proof completed in 92.8 seconds, meaning the assistant's wait was sufficient but not excessive. This 90-second sleep reflects a practical engineering judgment: better to wait a bit too long and see a completed result than to check too early and see an incomplete computation.

Fourth, the assistant monitors multiple signals simultaneously: the daemon log for structured timing information, nvidia-smi for GPU utilization and memory, and ps for CPU usage. This multi-signal monitoring, visible across messages [msg 224] through [msg 227], provides redundant confirmation that the proof is progressing through its expected phases: CPU-bound circuit synthesis first (low GPU utilization, high CPU core count), then GPU-bound NTT/MSM computation (high GPU utilization), then verification.

Assumptions Embedded in the Test

Several assumptions underpin this measurement, some explicit and some implicit.

The SRS cache would persist across proof calls. This was the central hypothesis, but it depended on the GROTH_PARAM_MEMORY_CACHE implementation in filecoin-proofs and storage-proofs-core actually retaining the loaded parameters in memory rather than releasing them after each seal_commit_phase2 call. The assistant had traced the code paths (see [msg 216]) to confirm that the cache used a lazy_static SETTINGS struct, which suggested persistence, but this was the first empirical validation.

The env var timing was correct. The assistant had carefully verified that FIL_PROOFS_PARAMETER_CACHE was set in the engine code before any proof call that would trigger the lazy initialization of SETTINGS (see [msg 217]). If the initialization had been triggered earlier — say, during some import or initialization path — the env var would have been ignored, and the default /var/tmp/filecoin-proof-parameters/ path would have been used. The first proof's log showing "Found params at /data/zk/params/v28-stacked-proof-of-replication-..." confirmed this assumption held.

The GPU backend would be consistent. The assistant built with --features cuda-supraseal and confirmed at [msg 223] that "Bellperson 0.26.0 with SupraSeal is being used!" — meaning the CUDA-accelerated proving path was active. The second proof used the same binary and same GPU, so the comparison is valid.

The proving time would be dominated by SRS load + synthesis + GPU compute. The assistant's mental model, articulated at [msg 220], broke the proof into six phases: gRPC transfer, deserialization, SRS load, CPU synthesis, GPU NTT/MSM, and verification. By measuring total wall-clock time, the assistant implicitly assumed that the non-SRS phases would be roughly constant between runs. The 20.5% improvement exceeded the ~12.8% improvement that would be expected from eliminating a 15-second SRS load from a 116.8-second total, suggesting that CPU cache warmth or other secondary effects also contributed.

Mistakes and Incorrect Assumptions

The most notable discrepancy is between the expected and observed improvement. The assistant notes at [msg 236] that "The ~24s improvement (vs 15s expected from SRS alone) suggests there's also some benefit from CPU caches being warm for the synthesis phase." This is a post-hoc explanation, but it reveals that the assistant's initial model was incomplete: the SRS load was not the only variable that changed between the two runs. The CPU synthesis phase — building ~130 million constraints for a 32 GiB PoRep circuit — also benefited from the first run's warm caches, memory allocation patterns, and possibly JIT compilation or lazy initialization in the bellperson library.

A more subtle assumption was that the SRS load was purely I/O-bound. The assistant observed at [msg 223] that the 45 GiB params loaded in ~15 seconds, noting "That's fast (likely cached in page cache from the earlier copy)." This suggests the disk read was actually a page cache hit, meaning the kernel had retained the file data in RAM from a prior copy operation. In production, where the daemon might be freshly started on a machine that hasn't recently read the params file, the cold-start SRS load could be significantly slower — potentially 45+ seconds on a single NVMe drive, or even longer if the params are on spinning media. The 15-second load observed here was a best-case scenario, and the 20.5% improvement might be larger in a true cold-cache environment.

Additionally, the assistant assumed that the gRPC transfer and deserialization times would be identical between runs, but these phases also benefit from warm CPU caches and memory allocation patterns. The 51 MB C1 JSON must be base64-decoded and parsed, which involves significant allocation and string processing. The second run's deserialization likely completed faster for the same reasons the synthesis phase did.

Input Knowledge Required

To fully understand message 235, a reader needs knowledge spanning several domains:

Groth16 proof structure. The message references SRS (Structured Reference String), which is the common reference string used in the Groth16 zk-SNARK protocol. For Filecoin's 32 GiB PoRep circuit, this is a ~45 GiB data structure containing the proving key, verification key, and other parameters. The fact that it must be loaded into GPU memory before proof generation is the central bottleneck the cuzk architecture aims to mitigate.

The Filecoin proof pipeline. PoRep (Proof of Replication) is a two-phase protocol: Phase 1 (C1) produces a commitment to the sector data, and Phase 2 (C2) generates the Groth16 proof that the sector has been stored correctly. The C2 phase is computationally intensive, involving circuit synthesis (CPU-bound) and NTT/MSM operations (GPU-bound). The 51 MB C1 JSON encodes the intermediate state from Phase 1 that Phase 2 needs.

The cuzk architecture. The daemon is designed as a persistent gRPC server that keeps SRS parameters resident across proof submissions. This is the "Persistent Prover Daemon" proposal from earlier analysis (see segment 0), now implemented as Phase 0 of the cuzk project. The GROTH_PARAM_MEMORY_CACHE is the mechanism by which filecoin-proofs retains the loaded parameters.

GPU computing concepts. The message references nvidia-smi output, GPU utilization percentages, and memory usage. Understanding that the RTX 5070 Ti (Blackwell architecture) has 16 GB of VRAM and that the SRS alone is 45 GiB explains why the SRS must be loaded into system RAM and paged to the GPU as needed, rather than fitting entirely in VRAM.

Rust and system programming. The lazy_static initialization pattern, environment variable handling, and the SETTINGS struct from storage-proofs-core are all Rust-specific concepts that underpin the cache persistence mechanism.

Output Knowledge Created

Message 235, together with its follow-up at [msg 236], produces several concrete pieces of knowledge:

Quantified SRS residency benefit: 20.5%. This is the headline number. For a 32 GiB PoRep proof on an RTX 5070 Ti, keeping the SRS in memory reduces total proving time from 116.8 seconds to 92.8 seconds. This is a meaningful improvement for any production deployment where multiple proofs are generated sequentially.

Proof that the cache persists across gRPC calls. This is perhaps the more important finding. The cuzk daemon's architecture — a long-lived process accepting proof requests over gRPC — is validated by this test. The SRS cache survives not just within a single seal_commit_phase2 call but across independent proof submissions, each with its own gRPC connection, deserialization, and job tracking.

Evidence of secondary warm-cache effects. The 24-second improvement versus the expected 15-second improvement suggests that CPU cache warmth, memory allocation patterns, and lazy initialization also contribute to faster subsequent proofs. This is valuable information for capacity planning: the second proof in a batch will be faster than the first even beyond the SRS benefit.

Validation of the env var approach. The FIL_PROOFS_PARAMETER_CACHE environment variable, set in the engine code before any proof call, successfully overrides the default parameter cache path. This confirms that the lazy_static initialization timing is correct and that the engine's startup sequence does not trigger premature initialization.

A benchmark baseline. The 92.8-second time for a cached proof on an RTX 5070 Ti establishes a baseline for future optimization work. Any optimization to the synthesis phase, GPU kernel performance, or memory transfer patterns can be measured against this number.

The Thinking Process Visible in the Message

The assistant's reasoning is visible in several dimensions of message 235. First, the structure of the message itself — starting with the exultant "SRS CACHE HIT!" — reveals the emotional arc of the investigation. The assistant had spent hours building, debugging, and validating the pipeline. The first proof's success at [msg 230] was a relief, but it was the second proof that would determine whether the architecture was sound. The cache hit confirmation is the moment of validation.

Second, the assistant's choice to format the comparison as a bullet list — "First proof: ... loaded from disk (15s)" vs "Second proof: ... instant!" — reflects a methodical, analytical mindset. The assistant is not just observing that the proof started faster; they are explicitly contrasting the log messages to prove that the cache mechanism is working as designed.

Third, the decision to wait 90 seconds before checking the log shows practical engineering judgment. The assistant could have polled every 10 seconds, but that would have produced noise and required more attention. Instead, they estimated the expected completion time (116.8s - 15s ≈ 101s) and added a margin, then checked once. This is a pattern of "measure once, measure well" rather than "measure constantly and average."

Fourth, the assistant's monitoring of multiple data sources — daemon log, nvidia-smi, ps — demonstrates a systems-thinking approach. The daemon log provides structured timing information, nvidia-smi confirms GPU activity, and ps shows CPU utilization and memory footprint. Together, these paint a complete picture of the proof's lifecycle.

Conclusion

Message 235 is, on its surface, a simple status check during a benchmark run. But it represents the culmination of weeks of design work, the validation of a core architectural hypothesis, and the production of the first empirical performance data for the cuzk proving engine. The 20.5% improvement from SRS residency is not just a number — it is proof that the persistent daemon architecture works, that the cache persistence mechanism functions correctly across gRPC calls, and that the engineering effort invested in Phase 0 has produced measurable results.

The message also demonstrates a pattern of rigorous experimental methodology: identical inputs, controlled environment, multi-signal monitoring, and careful comparison of expected versus observed results. These habits, visible in this single message, are what distinguish a proof-of-concept from a production system. The cuzk proving engine, at this moment, crossed that threshold.