The Benchmark Script: Closing the Loop on GPU Proving Validation

Introduction

In the course of building a production-grade Docker image for Filecoin's Curio proving engine with CUDA 13 support, a seemingly simple request from the user—"Can you add some benchmark.sh script"—triggered a cascade of research, design decisions, and infrastructure work that culminated in message 645. This message, a concise summary of the newly created benchmark.sh script, represents far more than a routine status update. It is the closing of a validation loop: after hours of debugging build blockers, linker errors, pip conflicts, and missing runtime libraries, the assistant finally delivers a tool that can actually prove the container works end-to-end.

The subject message reads:

Pushed. Here's the summary:

## benchmark.sh

Created at docker/cuzk/benchmark.sh — available inside the container as /usr/local/bin/benchmark.sh.

Usage: ```bash # Default: 5 proofs, sequential benchmark.sh

# 10 proofs with concurrency 2 benchmark.sh 10 -j 2

# Use existing daemon, skip warmup benchmark.sh 20 --no-start --skip-warmup -a 10.1.16.218:9820 ```

What it does: 1. Downloads c1.json (~51MB) from R2 if not present at the specified path 2. Starts cuzk-daemon, waits for it to respond to status RPC 3. Warmup proof — runs a single PoRep proof. If the PCE file (pce-porep-32g.bin) doesn't exist yet, this triggers PCE extraction (2-5 min first time). Waits for completion. 4. Benchmark run — runs N proofs via cuzk-bench batch -t porep and reports per-proof timing from cuzk-bench 5. Prints wall time, average per proof, throughput (proofs/hour), and daemon metrics 6. Cleans up daemon on exit

Key options: - -c /path/to/c1.json — custom C1 data path - -j N — concurrent proof requests - --no-start — connect to existing daemon - --skip-warmup — skip the warmup proof - -p /path — param cache directory

Why This Message Was Written: The Motivation and Context

To understand why this message exists, one must trace the conversation that led to it. The session had been intensely focused on building a Docker container capable of running Filecoin's Curio proving stack with CUDA 13 supraseal support (<msg id=612-626>). The build process was fraught with blockers: a pip upgrade conflict in the SPDK build system, a missing libcudart_static.a linker path, and several missing runtime shared libraries (libconfig++, libaio, libfuse3, libarchive). Each was diagnosed and fixed in turn.

Once the container was successfully built and pushed to Docker Hub under the theuser/curio-cuzk repository (<msg id=632-633>), the user immediately asked for a benchmarking script ([msg 634]). This request was not incidental—it was essential. A container that builds correctly is not the same as a container that runs correctly. The user needed a way to validate that the GPU proving pipeline actually functioned, that the PCE (Pre-Compiled Constraint Evaluator) extraction worked, that the daemon could accept proof requests, and that the throughput was acceptable. Without a benchmark script, the container was an untested artifact.

The assistant's response in message 645 is therefore the synthesis of several preceding research tasks. The assistant had to understand the cuzk benchmarking infrastructure ([msg 635]), locate test data sources ([msg 636]), study the existing end-to-end test script ([msg 637]), and design a script that met the user's specific requirements: warmup with PCE file detection, configurable proof counts, and clean output.

How Decisions Were Made: Design Philosophy of the Benchmark Script

The benchmark script's design reveals several deliberate choices. First, the assistant chose to make the script self-contained and idempotent. It downloads its own test data (c1.json from R2) if not present, starts its own daemon, and cleans up on exit. This means the user can run it on any machine with a GPU and get consistent results without manual setup steps.

Second, the warmup phase was specifically requested by the user: "ideally with warmup which dispatches one and waits for pce file to appear." The assistant implemented this by running a single PoRep proof and checking for the PCE file (pce-porep-32g.bin). This is critical because the first proof after a fresh deployment triggers PCE extraction, which can take 2-5 minutes. By explicitly waiting for this file, the script ensures that subsequent benchmark proofs run against a fully initialized pipeline, giving accurate timing measurements.

Third, the concurrency option (-j N) was added to allow testing of the daemon's ability to handle parallel proof requests. This is important for real-world deployment where multiple sectors may need proving simultaneously. The assistant's research into cuzk-bench had revealed the batch subcommand's concurrency support, and this was exposed as a user-facing parameter.

Fourth, the remote daemon support (--no-start, -a flags) shows forward-thinking design. The assistant anticipated that users might want to benchmark against an already-running daemon on a remote host, perhaps in a production cluster. This is not a feature the user explicitly requested, but one the assistant inferred would be valuable.

The decision to include daemon metrics in the output (step 5) is another subtle but important choice. It suggests the assistant understood that benchmarking is not just about raw proof time—it's about understanding system health. Metrics from the daemon could reveal memory pressure, queue depths, or other operational characteristics.

Assumptions Made by the Assistant

Several assumptions underpin the benchmark script's design:

C1 test data availability: The assistant assumed that the c1.json file hosted at the R2 URL (~51MB) would remain accessible and that its format would remain compatible with the cuzk-bench batch command. This is a reasonable assumption for a development tool, but it creates a dependency on an external hosting service.
Daemon startup behavior: The script assumes that cuzk-daemon will start within a reasonable timeout and respond to status RPCs. It also assumes that the daemon will bind to the default port (9826) or the one specified. If the daemon fails to start (e.g., due to GPU unavailability or parameter cache issues), the script's error handling would need to surface this clearly.
PCE file path convention: The warmup logic checks for pce-porep-32g.bin in the parameter cache directory. This assumes that the PCE extraction produces a file with exactly that name and location. If the naming convention changes or if the file is written elsewhere, the warmup detection would fail silently.
Single-GPU environment: The script does not handle multi-GPU scenarios explicitly. The -j N concurrency option may distribute work across available GPUs, but the script does not provide GPU selection or isolation controls.
No existing daemon conflict: The script starts its own daemon and kills it on exit. If the user already has a cuzk-daemon running on the same port, there could be a conflict. The --no-start flag mitigates this for advanced users, but the default behavior assumes a clean environment.

Mistakes or Incorrect Assumptions

Looking critically at the message, one potential oversight is the lack of explicit error handling description. The message states what the script does but not what happens when things go wrong. What if the C1 download fails? What if the daemon crashes during warmup? What if the benchmark proofs time out? The script likely has some error handling (given the set -e pattern common in such scripts), but the summary does not communicate failure modes to the user.

Another subtle issue is the warmup-PCE coupling. The script "waits for completion" of the warmup proof, but the mechanism for detecting PCE file existence is not described. Does it poll the filesystem? Does it parse daemon logs? The message leaves this ambiguous. If the PCE file already exists (from a previous run), the warmup proof would still run but the "wait for PCE" logic would be a no-op—this is correct behavior, but the user might not understand why the warmup sometimes takes 5 minutes and sometimes 30 seconds.

The concurrency semantics also deserve scrutiny. The -j 2 flag suggests concurrent proofs, but does cuzk-bench batch truly parallelize them, or does it merely dispatch them asynchronously to a single-threaded daemon? The assistant's research into the batch subcommand ([msg 635]) would have clarified this, but the message summary does not explain the concurrency model to the user.

Input Knowledge Required

To create this message, the assistant drew on a substantial body of knowledge:

The cuzk codebase structure: The assistant had to understand the relationship between cuzk-daemon (the proving server), cuzk-bench (the benchmarking client), and the batch subcommand. This required reading source code and understanding the RPC protocol ([msg 635]).
Test data sources: The assistant discovered the c1.json download URL through codebase exploration ([msg 636]), specifically from the experimental documentation and the golden test data directory.
The existing e2e test script: The test-e2e.sh script ([msg 637]) served as a template for daemon lifecycle management. The assistant adapted its patterns (start daemon, wait for readiness, run proofs, clean up) into the more flexible benchmark script.
Docker build mechanics: The assistant needed to know how to add the benchmark script to the Docker image, which required editing the Dockerfile to copy the script and build cuzk-bench in the builder stage (<msg id=641-642>).
The user's deployment context: The assistant knew the user was using Docker on GPU-enabled hosts (likely vast.ai or similar), which informed the decision to make the script work both inside and outside the container.

Output Knowledge Created

This message creates several forms of output knowledge:

A reusable benchmarking tool: The benchmark.sh script is a standalone artifact that can be used to validate GPU proving performance on any compatible host. It abstracts away the complexity of daemon management, test data acquisition, and result collection.
A validated Docker image: The fact that the script was built into the container and pushed to Docker Hub means the image now contains a complete testing workflow. Users can pull theuser/curio-cuzk:latest and immediately run benchmarks without additional setup.
Documentation of the benchmark workflow: The message itself serves as documentation. A user reading it learns the typical benchmark sequence (download data, start daemon, warmup, run, report) and the available configuration options.
A baseline for performance comparison: By establishing a standard benchmarking procedure (N proofs, with warmup, reporting throughput), the script enables the user to compare performance across different hardware configurations, CUDA versions, or code changes.
Operational patterns: The script encodes operational best practices: always warm up the PCE pipeline before benchmarking, always clean up daemon processes, always report wall time alongside per-proof timing. These patterns transfer to production deployment scripts.

The Thinking Process Visible in the Message

The message reveals the assistant's thinking process primarily through its structure and omissions. The assistant chose to present the script as a summary rather than showing its full source code. This decision reflects an understanding that the user cares about what the script does and how to use it, not the implementation details. The assistant is communicating at the right level of abstraction for a user who will be operating the tool, not modifying it.

The inclusion of three distinct usage examples is telling. The first (benchmark.sh) is the simplest case—just run it. The second (benchmark.sh 10 -j 2) shows parameterization. The third (benchmark.sh 20 --no-start --skip-warmup -a 10.1.16.218:9820) demonstrates the most advanced use case: connecting to a remote daemon. This progression from simple to complex mirrors how a user would naturally explore the tool.

The assistant's choice to mention the PCE extraction time ("2-5 min first time") shows awareness of a key user concern. The user had specifically asked about waiting for the PCE file, and the assistant is reassuring them that this delay is expected and handled.

The final line about daemon metrics is particularly interesting. It suggests the assistant is thinking beyond the immediate benchmark run to the broader operational picture. A user running benchmarks wants to know not just "how fast" but "is the system healthy?" This is the thinking of someone who has operated proving infrastructure before and knows that raw throughput numbers can mask underlying issues.

Conclusion

Message 645 is a deceptively simple status update that caps a complex engineering effort. The benchmark.sh script it describes is the practical bridge between "the container builds" and "the container works." It encodes operational knowledge about GPU proving pipelines—the need for warmup, the importance of clean test data, the value of concurrency testing—into a reusable tool. For the user, this message transforms an abstract Docker image into a measurable, testable system. For the assistant, it represents the successful completion of a request that required deep codebase research, thoughtful design, and careful integration with the existing build pipeline.