Multi-agent HPC code auto-tuning framework coordinating multiple AI CLIs via tmux to optimize parallel code across compilers and strategies without external orchestration.
git clone https://github.com/Katagiri-Hoshino-Lab/VibeCodeHPC-jp.gitVibeCodeHPC is a multi-agent framework that automatically optimizes high-performance computing code by coordinating multiple AI coding assistants (Claude Code, Cline, Gemini CLI, and others) through tmux inter-process communication. It uses a hierarchical agent structure—Project Manager, Software Engineer, Programmer agents, and Code Delivery—to explore parallel optimization strategies across different compilers (GCC, NVIDIA, Intel) and parallelization approaches (OpenMP, CUDA, OpenACC). The framework provides pluggable strategies for HPC parallelization and GPU optimization, with evolutionary exploration across a flat directory structure. It includes built-in monitoring of agent context usage and performance timelines, eliminating the need for external orchestration frameworks while enabling parallel search across optimization variants.
Prepare three items: a requirement_definition.md file (editable from template), _remote_info/ directory with site-specific hardware configuration, and BaseCode/ containing the code to optimize. Run the framework to spawn coordinated AI agents via tmux that automatically generate optimization variants, compile, benchmark, and track results. See docs/quickstart.md for detailed setup instructions.
CFD simulation code optimization across multiple compiler and parallelization combinations
Automated exploration of OpenMP, CUDA, and OpenACC implementations for existing HPC codes
Multi-strategy compiler tuning (GCC, Intel, NVIDIA) with centralized performance tracking
Parallel auto-tuning without orchestration infrastructure, using only tmux for agent coordination
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/Katagiri-Hoshino-Lab/VibeCodeHPC-jpCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
以下のHPCコードを最適化してください。[COMPANY]の[INDUSTRY]セクター向けに、[DATA]の処理を高速化するために必要な変更を提案してください。並列化の可能性とメモリ使用量の削減についても考慮してください。
# HPCコード最適化レポート ## 現在のコードの課題 - メモリ使用量が高い - 並列化の可能性が不十分に活用されていない - I/O操作がボトルネックになっている ## 推奨される最適化 ```python # 並列化の改善 from multiprocessing import Pool # メモリ使用量の削減 import numpy as np # I/O操作の最適化 import pandas as pd ``` ## 予想されるパフォーマンス向上 - 処理時間の30%削減 - メモリ使用量の40%削減 - 並列化によるスループットの向上
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