docs: add detailed CUDA migration plan for core compute

[Luohaothu]

Motivation

• Provide a comprehensive, implementation-ready design for migrating the project’s core compute paths to a CUDA-capable CPU/GPU dual-backend that covers expression evaluation, stencil operators, equation assembly and solver workflows.
• Capture engineering constraints and risks while preserving existing numerical semantics and enabling incremental rollout, verification and GPU/CPU mixed execution.

Description

• Add a new design document at plan/cuda_migration_plan.md that defines goals, non-goals, success criteria, abstraction layers and migration principles.
• Specify data structure and memory organization recommendations including SoA layouts, block/chunk AMR layouts, CSR/ELL/HYB sparse abstractions, LinearOperator and a lightweight expression IR for kernel fusion and scheduling.
• Define memory lifecycle, allocator and migration strategies (cudaMallocAsync pools, pinned buffers, async H2D/D2H, streams), kernel-granularity and fusion policies, ExecutionContext/CUDA Graph usage, and Ada/new-architecture autotune and build strategies.
• Present a phased solver migration roadmap (Krylov → AMG → matrix-free), MPI+CUDA multi-GPU/auto-parallel strategy, testing and benchmarking methodology, risk mitigations, milestones (M0–M4) and PoC entry points.

Testing

• The change is documentation-only and the new file plan/cuda_migration_plan.md was added and its contents were validated successfully using nl -ba plan/cuda_migration_plan.md to inspect the file body.
• No source-code unit or integration tests were run because this is a docs-only change.

---

Codex Task

[gemini-code-assist]

Summary of Changes

Hello @Luohaothu, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

此拉取请求引入了一份全面的设计文档，旨在指导项目核心计算路径向支持 CUDA 的 CPU/GPU 双后端架构迁移。该计划详细规划了从表达式评估、模板算子到方程组装和求解器工作流的各个方面，旨在实现显著的性能提升，同时确保数值语义的保持、支持增量部署、验证以及 CPU/GPU 混合执行能力。

Highlights

• CUDA 迁移设计文档: 引入了详细的 CUDA 迁移设计文档，旨在将核心计算路径迁移至 CPU/GPU 双后端架构，覆盖表达式评估、模板算子、方程组装和求解器工作流。
• 数据结构与内存策略: 详细阐述了数据结构组织（如 SoA 布局、稀疏结构抽象）、内存管理、分配及迁移策略（如 cudaMallocAsync 内存池、异步传输和流并发）。
• 求解器与并行策略: 规划了分阶段的求解器迁移路线图（Krylov → AMG → matrix-free）、MPI+CUDA 多 GPU/自动并行策略，以及负载均衡机制。
• 优化与测试: 提出了针对 Ada 及更新架构的优化策略、功能与性能回归测试方案、性能评测方法学，并识别了潜在风险及其缓解措施。
• 里程碑与 PoC: 定义了从骨架搭建到优化稳态的详细工程落地里程碑（M0-M4），并建议了首批概念验证（PoC）切入点。

Changelog

• plan/cuda_migration_plan.md
  • 添加了 OpFlow 核心计算 CUDA 迁移的详细设计方案。

Activity

• 作者添加了 plan/cuda_migration_plan.md 文件，并使用 nl -ba 命令验证了其内容。
• 由于此更改仅涉及文档，未运行源代码单元或集成测试。

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request introduces a comprehensive and well-structured design document for migrating the project's core compute functionalities to CUDA. The plan is thorough, covering critical areas such as architecture abstraction, data structures, memory management, kernel optimization strategies, multi-GPU parallelism, and testing. My feedback focuses on enhancing a few areas for even greater clarity and completeness. I've suggested adding more detail to the data lifecycle management policy, considering the future use of Tensor Cores, including developer ramp-up as a project risk, and re-evaluating the proposed timeline for realism. Overall, this is an excellent design document that lays a solid foundation for the migration effort.

[coderabbitai]

Important

Review skipped

Draft detected.

Please check the settings in the CodeRabbit UI or the .coderabbit.yaml file in this repository. To trigger a single review, invoke the @coderabbitai review command.

You can disable this status message by setting the reviews.review_status to false in the CodeRabbit configuration file.

Use the checkbox below for a quick retry:

• 🔍 Trigger review

✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch codex/design-cuda-migration-plan-for-core-computation

---

Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out.

❤️ Share

• X
• Mastodon
• Reddit
• LinkedIn

_{Comment @coderabbitai help to get the list of available commands and usage tips.}