A Benchmark Request That Reveals the Shape of a System

The Message

In the middle of an intense Docker build session, after pushing a multi-gigabyte container image to Docker Hub, the user pauses and sends a seemingly simple request:

Can you add some benchmark.sh script which benchmarks N (default 5) porep proofs with cuzk (ideally with warmup which dispatches one and waits for pce file to appear)?

This message, at index 634 in the conversation, is a turning point. The infrastructure work—fixing pip conflicts, resolving linker errors, installing runtime libraries, pushing the image—is complete. Now the user needs to know: does it actually perform? The request for a benchmark script is the bridge between "it builds" and "it works well enough to deploy."

Context and Motivation

To understand why this message was written, we need to look at what came before. The preceding ~45 messages (589–633) were a marathon of Docker build debugging. The assistant had been iterating through a cascade of build blockers:

• A pip conflict where the supraseal build's build.sh created a venv and tried pip install --upgrade pip, which conflicted with the Debian-managed python3-pip package. The fix was to simply not install python3-pip at all, letting the venv bootstrap its own pip via ensurepip.
• A -lcudart_static linker error where the Go linker couldn't find libcudart_static.a because /usr/local/cuda/lib64 wasn't in LIBRARY_PATH. The fix was adding that path to the environment variable.
• Missing runtime libraries (libconfig++, libaio, libfuse3, libarchive) that caused curio to fail with "cannot open shared object file" errors at runtime. The Docker build succeeded, producing a ~3GB image (theuser/curio-cuzk:latest) containing four binaries: curio (163MB), sptool (210MB), cuzk (27MB), and the entrypoint script. The user then asked how to publish to Docker Hub, and the assistant tagged and pushed the image. Now the image is live. The user can pull it on any GPU-equipped host and run it. But running it well requires understanding its performance characteristics. The proving pipeline for Filecoin involves computationally expensive GPU operations, and the whole point of the cuzk project is to accelerate these proofs using CUDA. Without a benchmark script, the user has no way to:
• Verify that the proving pipeline actually works end-to-end in the container
• Measure throughput to compare against expectations or alternative configurations
• Establish a baseline before tuning parameters
• Validate that the PCE (Pre-Compiled Constraint Evaluator) extraction—a one-time cost on first run—completes successfully The request is thus deeply practical. It's the natural next step after "can I build it" becomes "does it work, and how fast?"

The Reasoning Embedded in the Request

The user's specification is remarkably precise for a casual request. They specify:

1. Default N=5 proofs: Five is a small enough number to run quickly for a smoke test, but large enough to get a meaningful average. It suggests the user wants a quick check, not a full production benchmark.
2. PoRep proofs specifically: PoRep (Proof of Replication) is the most fundamental proof type in Filecoin's proving hierarchy. It's the proof that storage providers must submit to prove they're storing data. By benchmarking PoRep, the user gets the most relevant metric for the primary use case.
3. A warmup that dispatches one proof and waits for the PCE file to appear: This is the most technically interesting requirement. The PCE (Pre-Compiled Constraint Evaluator) is a compiled form of the constraint system that cuzk uses for GPU proving. The first time a proof type is run, the system must "extract" the PCE—a process that can take 2–5 minutes. Subsequent proofs reuse the cached PCE file and are much faster. The user explicitly wants the benchmark to account for this: run one warmup proof, wait for the PCE file to materialize, then run the actual benchmarked proofs. This shows the user has a deep understanding of the proving pipeline's cold-start behavior. The user also implicitly specifies the tooling: a bash script. This is the right choice for a Docker-centric workflow—bash runs anywhere, doesn't require compilation, and can orchestrate the daemon lifecycle, HTTP calls, and file watching that the benchmark requires.

Assumptions Made

The user makes several assumptions, all reasonable given the conversation history:

• The assistant knows the cuzk codebase: The assistant has been working with the cuzk source code throughout the session, reading files, making edits, and understanding the architecture. The user assumes this knowledge carries forward.
• There is existing benchmark infrastructure to leverage: The user mentions "cuzk" as the proving engine, implying there's a cuzk-bench binary or similar tool. This turns out to be correct—the codebase includes a cuzk-bench binary with a batch subcommand for running multiple proofs.
• Test data is available: The user assumes there's a way to get C1 output data (the input to the proving pipeline). The assistant discovers a publicly downloadable c1.json file (~51MB) hosted on R2.
• The script should live in the Docker context: The user asks for a "benchmark.sh script" without specifying where. The assistant places it at docker/cuzk/benchmark.sh and adds it to the Docker image, making it available inside the container at /usr/local/bin/benchmark.sh.
• The warmup is a blocking step: The user says "waits for pce file to appear," implying the script should actively watch for the file rather than just assuming the proof completed. This is a robust design choice that handles the variable duration of PCE extraction.

Input Knowledge Required

To fulfill this request, the assistant needed to understand:

1. The cuzk daemon architecture: How cuzk-daemon starts, what RPC endpoints it exposes, how to submit proof requests, and how to check status.
2. The cuzk-bench binary: Its CLI arguments, subcommands (batch, single), and output format. The assistant discovers it at /tmp/czk/extern/cuzk/cuzk-bench/src/main.rs and reads its 1823 lines.
3. Test data sources: Where to get C1 output JSON for PoRep proofs. The assistant finds a download URL in the documentation and a "golden" test data directory.
4. The PCE file naming convention: The assistant needs to know what file to watch for—pce-porep-32g.bin—which comes from understanding the cuzk internals.
5. The Dockerfile structure: To add the benchmark script and cuzk-bench binary to the image, the assistant must edit the multi-stage Dockerfile, adding a build step for cuzk-bench and a COPY instruction for the runtime stage.

Output Knowledge Created

The request produces several tangible artifacts:

1. /tmp/czk/docker/cuzk/benchmark.sh: A bash script that orchestrates the full benchmark lifecycle—downloading test data, starting the daemon, running a warmup proof with PCE file monitoring, executing N benchmark proofs via cuzk-bench batch, reporting results, and cleaning up.
2. Updated Dockerfile: The builder stage now includes cargo build --release --bin cuzk-bench alongside the existing cuzk-daemon build. The runtime stage copies the cuzk-bench binary and the benchmark.sh script.
3. A new Docker image: The rebuilt image (theuser/curio-cuzk:latest) now contains four binaries—curio, sptool, cuzk, and cuzk-bench—plus the benchmark script. The image is pushed to Docker Hub.
4. Documentation of the benchmark workflow: The assistant provides usage examples showing the script's flexibility: custom proof counts, concurrency levels, connecting to existing daemons, and skipping warmup.

The Thinking Process Visible in the Response

The assistant's response chain (messages 635–645) reveals a systematic research-driven approach. Rather than immediately writing a script, the assistant first dispatches two research tasks:

Message 635: A subagent task to "Explore cuzk benchmarking" that reads the full cuzk-bench/src/main.rs (1823 lines), understands the CLI arguments, and reports back on how proof submission and benchmarking work. This is the right first step—you can't write a benchmark script without understanding the tool it wraps.

Message 636: A second subagent task to "Find C1 test data sources" that searches the codebase for test fixtures, sample data files, and download scripts. The assistant discovers the public R2 download URL and the "golden" test data directory.

Message 637: The assistant reads the existing test-e2e.sh script to understand the established patterns for daemon lifecycle management, port selection, and cleanup.

Only after this research does the assistant write the script (message 638). The thinking is methodical: understand the tool, find the data, study existing patterns, then implement.

The assistant then goes beyond the original request: it updates the Dockerfile to build cuzk-bench and include it in the image (messages 641–642), rebuilds the image (message 643), and pushes the updated image to Docker Hub (message 644). The user asked for a script; the assistant delivers a script plus an updated container that has everything needed to run it.

Conclusion

A message that looks like a simple feature request—"add a benchmark script"—is actually a window into the entire development workflow. It marks the transition from infrastructure (building and shipping a container) to validation (measuring whether it performs). The precision of the request reveals the user's deep understanding of the proving pipeline's cold-start behavior. The assistant's methodical research-driven response shows how effective tool use can compress hours of code reading into minutes of analysis. And the final artifact—a benchmark script embedded in a Docker image on Docker Hub—represents the completion of a full development cycle: build, ship, measure.