The Config File That Validated an Architecture: Reading `/tmp/cuzk-pipeline-test.toml`

In the middle of a sprawling coding session spanning ten segments and hundreds of messages, one message stands out for its deceptive simplicity. At message index 640, the assistant performs a single action:

[assistant] [read] /tmp/cuzk-pipeline-test.toml

```toml [daemon] listen = "0.0.0.0:9821"

[srs] param_cache = "/data/zk/params" preload = ["porep-32g"]

[memory] pinned_budget = "50GiB" working_memory_budget = "200GiB"

[gpus] devices = []

[scheduler] max_batch_size = 1 max_batch_wait_ms = 10000 sort_by_type = true

[synthesis] threads = 0

[pipeline] enabled = true synthesis_lookahead = 1 ```

On its surface, this is trivial: a developer reading a configuration file before running a test. But in the context of the session, this message represents the culmination of weeks of architectural work — the moment before validation. It is the quiet pause before the proof.

The Architecture That Led Here

To understand why this message matters, one must understand what the assistant had just accomplished. The cuzk project is building a proving engine for Filecoin's Proof-of-Replication (PoRep) — a cryptographic proof that storage providers are actually storing the data they claim to store. The proving pipeline is computationally brutal: a single 32 GiB PoRep C2 proof requires ~200 GiB of peak memory, ~55 seconds of CPU-bound circuit synthesis across 142 cores, and ~35 seconds of GPU-bound proving on a modern GPU like the RTX 5070 Ti.

The original architecture (Phase 1) was monolithic: each GPU worker would pull a proof request from the scheduler, load the SRS (Structured Reference String) parameters, run CPU synthesis, run GPU proving, complete the job, and then move to the next one. This was simple and correct, but it left the GPU idle during synthesis and the CPU idle during GPU proving. For a single proof, this was fine. For continuous operation — the real-world use case where a storage provider needs to generate proofs back-to-back — it meant wasting half the hardware's potential.

The Phase 2 pipeline architecture, which the assistant had just committed as 5ba4250f minutes before this message, split the monolithic worker into two stages connected by a bounded asynchronous channel. A dedicated synthesis task pulls proof requests from the scheduler, runs the CPU-bound circuit synthesis on a blocking thread, and pushes the synthesized intermediate state into a tokio::sync::mpsc channel. Per-GPU workers, each pinned to a physical GPU via CUDA_VISIBLE_DEVICES, pull synthesized jobs from the channel and run the GPU-bound proving phase. The channel's capacity — controlled by the synthesis_lookahead configuration parameter — provides backpressure: when the GPU is busy and the channel is full, the synthesis task blocks, preventing unbounded memory consumption from pre-synthesized proofs piling up.

This is a textbook producer-consumer pipeline, and it enables a critical overlap: synthesis of proof N+1 can begin while the GPU is still proving proof N. In steady state, the per-proof latency drops from ~90 seconds (synthesis + GPU) to ~55 seconds (synthesis-bound, with GPU time fully hidden behind the next synthesis).

Why Read a Config File?

The message at index 640 is the bridge between code and reality. The assistant had written the architecture, committed it, and built the release binary. But before launching the daemon and submitting real proofs, it needed to verify that the test configuration was correct. This is not a casual glance — it is a deliberate act of validation.

The config file encodes the assumptions and parameters of the entire test. Every value tells a story:

[pipeline] enabled = true — This is the critical flag. It activates the two-stage producer-consumer architecture that the assistant had just implemented. Without it, the engine falls back to the Phase 1 monolithic workers. The test would be meaningless.

synthesis_lookahead = 1 — This sets the channel capacity to exactly one pre-synthesized proof. For a single-GPU system (the RTX 5070 Ti in this test), this is the optimal value. A lookahead of 0 would mean no overlap (the GPU worker blocks until synthesis completes). A lookahead of 2 or more would allow multiple proofs to be pre-synthesized, consuming memory without benefit since only one GPU can prove at a time. The value of 1 means exactly one proof is always ready in the channel, so the GPU never waits, but no more than one extra proof is ever held in memory.

[memory] working_memory_budget = "200GiB" — This reflects the hard reality of PoRep C2 proving. The synthesis phase alone requires ~200 GiB of RAM for the 32 GiB sector size. This budget must be available, or the process will OOM. The pinned_budget = "50GiB" reserves host memory that is pinned for GPU DMA transfers, a separate pool used during the GPU proving phase.

[gpus] devices = [] — An empty list means "auto-detect." This is both a convenience and a risk. On the test machine with a single RTX 5070 Ti, auto-detection works perfectly. But it also means the assistant is trusting the CUDA runtime to discover the correct device. If the machine had multiple GPUs or if CUDA_VISIBLE_DEVICES was set incorrectly, the test could fail in confusing ways.

[srs] preload = ["porep-32g"] — The SRS parameters for 32 GiB PoRep are preloaded at daemon startup. This is a ~45 GiB file that takes ~15 seconds to load from disk. Preloading ensures the first proof doesn't pay this latency, which is important for the throughput measurement.

[scheduler] max_batch_size = 1 — Each proof is handled individually, not batched. This is correct for the pipeline test because the pipeline overlap works at the individual proof level. Batching would complicate the timing analysis.

The Assumptions Embedded in the Config

The config file reveals several assumptions the assistant is making:

The hardware is stable: The RTX 5070 Ti has sufficient GPU memory and compute capacity. The 200 GiB working memory budget assumes the host has at least that much RAM available.
The test data is valid: The C1 JSON file at /data/32gbench/c1.json (51 MB, verified in the previous message) contains valid pre-processed circuit data for a 32 GiB sector. If this file were corrupted or from a different sector size, the test would fail silently or produce incorrect proofs.
The SRS cache is populated: The directory /data/zk/params contains 29 parameter files (verified in message 639). The porep-32g parameters must be among them.
The pipeline architecture is correct: The assistant assumes that the two-stage pipeline will work correctly with real CUDA kernels, not just in unit tests. The unit tests (15 passing) validate the Rust-level logic, but they cannot test the actual GPU proving path because they run without the cuda-supraseal feature.
The bounded channel provides sufficient backpressure: With synthesis_lookahead = 1, the assistant assumes that the channel capacity is enough to prevent OOM while keeping the GPU fed. This is a reasonable assumption for single-GPU, but it would need re-evaluation for multi-GPU configurations.

What Happens Next

The message at index 640 is immediately followed by the E2E GPU test. The assistant launches the daemon, which loads the SRS in 15.3 seconds, reports "1 GPU, pipeline mode enabled," and begins listening on port 9821. Then the assistant submits a single proof (90 seconds, confirming the pipeline works), followed by a batch of 3 proofs submitted concurrently.

The results are striking:

| Metric | Sequential (no pipeline) | Pipeline (this test) | |---|---|---| | Single proof | ~91s | 90s (same — no overlap for 1st) | | 3 proofs total | ~270s (estimated) | 212.7s | | Steady-state/proof | 90s | ~60s (synthesis-bound) | | Throughput | 0.67 proofs/min | 0.85 proofs/min | | Speedup | — | 1.27x |

The daemon logs confirm the overlap: synthesis of proof N+1 begins while the GPU is still proving proof N. The second proof completes at 153.0s (61.9s incremental, synthesis-bound), and the third at 212.7s (59.7s incremental). The steady-state throughput is bounded by synthesis time, with GPU compute fully hidden behind the pipeline.

The Deeper Significance

This message — a simple file read — represents the moment when architecture meets reality. The config file is the interface between the abstract design (producer-consumer pipeline, bounded channels, backpressure) and the concrete execution environment (a specific GPU, specific memory budgets, specific file paths). Reading it is an act of verification: "Is the world ready for this test?"

In software engineering, we often focus on the dramatic moments — the commit, the merge, the deployment. But the quiet moments of preparation are equally important. The assistant could have launched the daemon without reading the config, trusting that the file from a previous test session was still correct. Instead, it paused to verify. This discipline — check your assumptions before running the experiment — is what separates reliable engineering from guesswork.

The config file itself is also a form of documentation. It captures the exact parameters of the test: which GPU, which memory budget, which pipeline settings, which SRS parameters. Anyone reading this message six months later can reconstruct the test environment precisely. The file is a snapshot of the engineering decisions made at this moment.

Conclusion

Message 640 is a single read tool call retrieving a 26-line TOML configuration file. But in the narrative of this coding session, it is the hinge point — the moment when weeks of architectural work, from the initial analysis of the Supraseal C2 pipeline through the bellperson fork, the batch-mode rewrite, and the async overlap implementation, all converge on a single test. The config file is the checklist: pipeline enabled, lookahead set, memory budgeted, GPU auto-detected, SRS preloaded. Everything is in place.

The test that follows validates not just the code but the architecture itself. The 1.27x throughput improvement is real, measurable, and confirmed by the logs. And it all starts with reading a file.

The Config File That Validated an Architecture: Reading /tmp/cuzk-pipeline-test.toml