The Research Phase: Gathering Intelligence Before Building a Docker Container for Curio/cuzk

Introduction

In the sprawling, multi-layered world of Filecoin proof generation, few tasks are as deceptively complex as containerizing a heterogeneous stack. Message 549 of this opencode session captures a pivotal moment: the transition from planning to execution. After extensive research into the Curio and cuzk build systems, after fixing a multi-GPU race condition and threading a gpu_index parameter through C++, Rust, and Go layers, the assistant now faces a concrete deliverable: write a Dockerfile and entrypoint script for a container that bundles the Curio Go binary, the cuzk Rust/CUDA proving daemon, and all dependencies for mainnet 32GiB proving.

But before writing a single line of Dockerfile, the assistant does something methodical and revealing: it reads six reference files in parallel. This message is a pure research and planning message — no code is written, no decisions are finalized. Instead, it represents the critical information-gathering phase that underpins every subsequent decision in the Docker build.

This article examines message 549 in depth: why the assistant chose to read these specific files, what knowledge it was seeking, what assumptions it made, and how this research phase shaped the Docker container that would eventually be built. It is a study in the discipline of understanding before acting, and a window into the complexity of containerizing a project that spans Go, Rust, C++, CUDA, Python build tools, and a dozen system dependencies.

Context: How We Arrived at This Moment

To understand message 549, we must first understand the journey that led to it. The broader session began with a user request: "Prepare a docker container with curio, cuzk, all curio dependencies, and fetching 32G PoRep and Snap proving params." This seemingly straightforward request landed in the middle of a complex technical landscape.

The Curio project is a Filecoin storage provider implementation. It depends on filecoin-ffi (a CGO bridge to Rust cryptographic routines), supraseal (a C++/CUDA library for GPU-accelerated proof generation), and cuzk (a Rust/CUDA proving daemon). The build system involves Go 1.24, Rust 1.86.0, GCC 13, the CUDA 13 toolkit, Python virtual environments for Meson/Ninja build tools, and a dozen system packages. The existing Dockerfile at /tmp/czk/Dockerfile used OpenCL, not CUDA, and did not build cuzk at all — it was a starting point for patterns, not a template to copy.

Prior segments of the session had been consumed with deep debugging: fixing a WindowPoSt crash caused by mismatched is_extensible() flags between RecordingCS and WitnessCS, implementing Pre-Compiled Constraint Evaluator (PCE) extraction for all proof types, and — most recently — diagnosing and fixing a GPU race condition where partition proofs on multi-GPU systems always ran on GPU 0 due to a min(ngpus(), num_circuits) computation in the C++ CUDA code. That fix, committed as bf804060, threaded a gpu_index parameter through the entire proving stack.

By message 549, the assistant had completed extensive research (via two task tool subagent sessions) into the curio build system and cuzk build requirements. It had read the existing Dockerfile, the build makefiles, the dependency lists, and the supraseal build script. The user had confirmed key decisions: use mainnet build tags, use CUDA 13 (not 12.8), fetch parameters at runtime rather than baking them into the image. The todo list showed "Write Dockerfile" and "Write entrypoint.sh" as pending.

Message 549 is the moment the assistant begins executing on those todos. But rather than diving straight into writing, it does something characteristic of thorough engineering: it re-reads the source materials one more time, in parallel, to ensure nothing is missed.

The Message: A Parallel Read of Six Files

The message itself is concise. The assistant writes:

Let me read all the reference files in parallel.

It then issues six read tool calls simultaneously, targeting:

  1. /tmp/czk/Dockerfile — the existing OpenCL-based Dockerfile
  2. /tmp/czk/scripts/makefiles/00-vars.mk — build variables and FFI/CUDA flags
  3. /tmp/czk/scripts/makefiles/10-deps.mk — dependency build targets
  4. /tmp/czk/scripts/makefiles/30-build.mk — main build targets for curio/sptool/cuzk
  5. /tmp/czk/.github/actions/install-deps/action.yml — comprehensive system dependency list from CI
  6. /tmp/czk/extern/supraseal/build.sh — the supraseal CUDA/C++ build script Each file serves a distinct purpose in the research. The existing Dockerfile provides the architectural pattern: multi-stage builds, base image choices, build argument patterns. The makefiles provide the exact build commands and environment variables needed. The CI dependency list ensures no system package is forgotten. The supraseal build script reveals the CUDA-specific requirements: GCC version constraints, architecture flags, and the BLST dependency. The assistant also reads /tmp/czk/scripts/build-blst.sh (a seventh file, though it's a short 8-line script), which shows how BLST is cloned from GitHub and built with -march=native.

Why These Files Were Chosen

The selection of files is not random. Each one fills a specific knowledge gap:

The existing Dockerfile answers the question: "How does the project currently containerize itself?" It reveals the multi-stage structure (a rust-toolchain stage, a curio-builder stage, a runtime stage), the base images used (rust:1.86.0-slim-bookworm, golang:1.24-trixie), and the build patterns (apt-get install lists, COPY --from stages, binary stripping with -s -w). Crucially, it also reveals what is not there: no CUDA, no cuzk, no supraseal. The new Dockerfile must add all of these.

The build makefiles answer: "What environment variables control the build, and what commands actually produce the binaries?" 00-vars.mk defines FFI_USE_CUDA, FFI_USE_CUDA_SUPRASEAL, FFI_BUILD_FROM_SOURCE, and the CGO_LDFLAGS_ALLOW_PATTERN. 10-deps.mk shows how to build the FFI and supraseal dependencies. 30-build.mk shows the exact go build command with tags and ldflags. These are essential for translating the Makefile-based build into Dockerfile RUN commands.

The CI dependency list answers: "What system packages are needed that I might forget?" The .github/actions/install-deps/action.yml is a comprehensive list maintained by the project's CI pipeline — it includes everything from build-essential and gcc-13 to libhwloc-dev, ocl-icd-opencl-dev, python3-venv, meson, ninja-build, and pyelftools. This is the most reliable source for the apt-get install list in the Dockerfile.

The supraseal build script answers: "What does the CUDA build actually require?" It reveals GCC version constraints (GCC 12 or 13 required for C++17), CUDA architecture targets (sm_75 through sm_90), and the BLST dependency that must be cloned from GitHub. It also shows the -DRUNTIME_SECTOR_SIZE flag for runtime sector size selection.

The BLST build script answers: "How is the BLST dependency obtained?" It's a simple git clone and build, but it must happen before supraseal can compile.

The Reasoning: Why Read in Parallel?

The assistant's decision to read all files in parallel, rather than sequentially, reflects an understanding of the tool environment. The read tool is stateless — it returns file contents without side effects. There is no dependency between reads. By issuing them all at once, the assistant minimizes latency and receives all results simultaneously.

This parallel approach also reveals the assistant's mental model: these files are independent reference materials that collectively paint a complete picture. The assistant is not debugging or iterating — it is gathering. The next message will synthesize this information into the Dockerfile.

There is also a subtle discipline here. The assistant had already read these files in earlier research phases (messages 538-544). But now, at the moment of execution, it re-reads them. This is not redundant — it is a deliberate refresh to ensure the details are fresh and accurate. In complex engineering tasks, the cost of misremembering a build flag or missing a dependency is high. Re-reading at the point of writing reduces that risk.

Assumptions Embedded in This Research

Every research phase rests on assumptions, and message 549 is no exception. The assistant assumes that:

  1. The existing Dockerfile patterns are transferable. The new Dockerfile will use a similar multi-stage structure, similar base images, and similar build patterns. This is reasonable — the project's build system is consistent — but the CUDA build introduces new complexities (CUDA toolkit installation, nvcc in PATH, GCC version constraints) that the existing Dockerfile does not address.
  2. The CI dependency list is complete and correct. The .github/actions/install-deps/action.yml is a reliable source, but it was written for CI runners, not Docker containers. Some packages may be unnecessary in a container context, and some may be missing (e.g., CUDA toolkit itself, which is provided by the base image, not apt).
  3. The build commands from the makefiles can be directly translated to Dockerfile RUN commands. This is mostly true, but the makefiles assume a certain directory structure and submodule state. In a Docker build, the repository is copied in at a specific point, and submodules may need explicit initialization.
  4. CUDA 13 base images exist and are compatible. The user specified "cuda13 is a thing now" as the base image choice. The assistant assumes that nvidia/cuda:13.0-devel-ubuntu22.04 (or similar) is available and that the CUDA 13 toolkit is compatible with GCC 13 and the supraseal build.
  5. The BLST dependency can be fetched during the build. The build-blst.sh script clones from GitHub. In a Docker build, this requires network access and git configuration. The assistant assumes this will work without authentication or rate limiting.
  6. The curio fetch-params command can be run at container startup. The entrypoint script will call this command, which downloads ~100GB of proving parameters. The assistant assumes the container has sufficient disk space and network bandwidth, and that the command completes successfully without interaction.

Potential Gaps and Blind Spots

No research is perfect, and message 549 has blind spots that would only become apparent during the actual build:

The CUDA base image's Python environment. The supraseal build requires Python 3 with venv, meson, ninja, and pyelftools for its SPDK dependency. The CUDA devel base images from NVIDIA ship with Python, but the environment may have quirks — as the assistant would later discover when a pip uninstall command failed due to a missing RECORD file. This was not visible from the files read in message 549.

The libcuda.so.1 symlink issue. The bellperson build script probes for GPU availability by checking for libcuda.so. In the CUDA devel image, the library is in the stubs directory and may not be in the default library path. This would cause the build to skip GPU code paths silently. The assistant would discover this only when the build failed or produced incorrect binaries.

The protobuf-compiler dependency. The filecoin-ffi build requires protoc (the protobuf compiler). This is listed in the CI dependencies but is easy to miss among the dozens of packages. If forgotten, the FFI build would fail with an obscure error.

The sheer size of the build. The Dockerfile would need to compile filecoin-ffi (which itself builds Rust crates via rustup), supraseal (C++/CUDA with BLST), and cuzk (Rust with CUDA bindings). Each of these is a substantial compilation. The assistant assumes this will fit within a reasonable Docker build timeout and disk space, but the actual build would take hours and consume tens of gigabytes.

Input Knowledge Required to Understand This Message

To fully grasp message 549, a reader needs:

Output Knowledge Created by This Message

Message 549 does not produce a Dockerfile, a script, or any artifact. Its output is purely informational: the contents of six files that will inform the next steps. However, this information is structured and focused. The assistant now has:

  1. A complete dependency list for the apt-get install command in the Dockerfile
  2. The exact build commands for curio, with all required environment variables and tags
  3. The CUDA build requirements including GCC version, architecture flags, and BLST cloning
  4. The architectural pattern from the existing Dockerfile (multi-stage, base images, COPY strategy)
  5. The build order (dependencies first, then Go binary, then cuzk binary)
  6. The param fetch mechanism (curio fetch-params 32GiB, from the earlier research) This knowledge is immediately actionable. In the next message, the assistant will write the Dockerfile and entrypoint script, synthesizing these six sources into a coherent build.

The Thinking Process: A Study in Methodical Engineering

The most striking aspect of message 549 is its methodical nature. The assistant does not guess, does not assume, does not write speculative code. It reads. It gathers. It verifies.

This is visible in the todo list that accompanies the message. The assistant maintains a structured todo list with priorities and statuses. The "Read existing Dockerfile for patterns and reference" item is marked "in_progress." The "Write entrypoint.sh script" and "Write Dockerfile.cuzk" items are "pending." The assistant is working through a checklist, and message 549 represents the final research step before execution.

The parallel read strategy is also revealing. The assistant could have read files one at a time, but it chooses to batch them. This is efficient but also reflects confidence: the assistant knows these are the right files to read, and it knows they are independent. There is no hesitation, no "let me check if I need to read X." The research phase is complete; this is a final data refresh.

There is also a subtle humility in the approach. The assistant had already read these files in earlier subagent tasks (messages 538 and 539). Yet it reads them again. This acknowledges that memory is fallible and that the cost of a mistake — a missing package, a wrong build flag — is high. Re-reading at the point of writing is cheap insurance.

Conclusion

Message 549 is a quiet moment in a noisy session. No code is written, no bugs are fixed, no proofs are generated. Yet it is essential. It represents the discipline of understanding before building, of gathering intelligence before committing to a design.

The Docker container that would eventually emerge from this session — Dockerfile.cuzk with its multi-stage build, its entrypoint script, its runtime param fetching — was shaped by the information gathered in this message. Every apt-get install package, every COPY --from directive, every environment variable in the final Dockerfile traces back to one of these six files.

In software engineering, the most expensive mistakes are those made early, when the cost of change is lowest but the visibility of consequences is also lowest. Message 549 is an antidote to that trap: a deliberate, methodical research phase that reduces the risk of late-stage surprises. It is a reminder that the most important code is sometimes the code you don't write yet.