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Commit207cb87

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generate stars/repo history plots
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‎.gitignore‎

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# Python virtual environments
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venv/
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env/
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ENV/
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.venv/
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# Python cache
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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# Distribution / packaging
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dist/
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build/
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*.egg-info/
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.eggs/
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# Generated visualizations and data
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*.png
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*.jpg
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*.pdf
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*.json
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!requirements.txt
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# Jupyter Notebook
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.ipynb_checkpoints
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# IDE
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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# macOS
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.DS_Store
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.AppleDouble
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.LSOverride
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# Fortran
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*.mod
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*.o
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*.a
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*.so
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*.dylib
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*.exe
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# Build artifacts
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bin/
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lib/
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*.out

‎CLAUDE.md‎

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#Instructions for Claude
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This file contains guidelines and conventions to follow when working on this repository.
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##Repository Maintenance Guidelines
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###1. README.md Table of Contents
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**IMPORTANT**: Every time new content is added to the repository (directories, scripts, examples, documentation, etc.), it MUST be reflected in the main README.md file.
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**Why**: Visitors should be able to easily understand what's in the repository and navigate to relevant content from the README.
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**How**:
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- Update the "Repository Structure" section with new directories
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- Add new sections describing the purpose and contents of new additions
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- Include links to sub-READMEs or documentation when appropriate
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- Keep the structure hierarchical and easy to scan
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**IMPORTANT - Keep README.md SHORT and CLEAN**:
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- The main README.md must be concise - just titles and minimal content
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- Avoid verbose descriptions, detailed instructions, or long lists
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- Use brief one-line descriptions for new sections
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- Always link to subdirectory READMEs for detailed information
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- Detailed documentation belongs in subdirectory READMEs, not the main README
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**Example**: When the`media/stars/` directory was added with Python scripts to generate repository statistics, the README.md should be updated to include:
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- The directory in the structure tree
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- A description of what's in`media/stars/`
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- Links to the sub-README or mention of key scripts
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###2. Documentation Standards
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- Every subdirectory with scripts or code should have its own README.md
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- READMEs should include:
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- Purpose and overview
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- Installation/setup instructions
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- Usage examples
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- Expected outputs
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- Links to related resources
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###3. Code Organization
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- Keep related content in well-named directories
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- Use descriptive filenames
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- Add comments and docstrings to all scripts
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- Include requirements.txt or equivalent for dependencies
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###4. Presentation-Focused Content
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This repository is for a FortranCon 2025 presentation. Keep this context in mind:
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- Examples should be clear and demonstrative
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- Visualizations should be high-quality (300+ DPI)
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- Code should be well-commented for educational purposes
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- Include both working examples and their expected outputs
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###5. Git Workflow
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- Keep commits focused and atomic
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- Write clear commit messages
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- Don't commit generated outputs (images, data files) unless they're meant as reference examples
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##Current Repository Structure Tracking
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When adding new directories or significant content, track them here for easy reference when updating README.md:
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-[x]`examples/` - Code examples (structure defined, not yet created)
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-[x]`tests/` - Test suites (structure defined, not yet created)
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-[x]`benchmarks/` - Performance comparisons (structure defined, not yet created)
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-[x]`docs/` - Additional documentation (structure defined, not yet created)
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-[x]`media/` - Presentation materials
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-[x]`media/stars/` - Repository statistics and star history scripts
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##Quick Checklist for Adding New Content
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When adding new directories, scripts, or major features:
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-[ ] Create the content/directory
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-[ ] Add appropriate README.md in the subdirectory (if applicable)
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-[ ] Update main README.md:
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-[ ] Repository Structure section
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-[ ] Add descriptive section explaining the new content
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-[ ] Update any relevant links or references
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-[ ] Add entry to this CLAUDE.md tracking list
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-[ ] Ensure any scripts have proper documentation and usage instructions
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##Style Guidelines
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- Use clear, concise language
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- Prefer active voice
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- Keep line lengths reasonable for readability
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- Use markdown formatting consistently
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- Include code examples with proper syntax highlighting
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##Contact and Conventions
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Primary maintainer: F. Perini
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Context: FortranCon 2025 presentation materials
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Focus: Precision-agnostic BLAS/LAPACK and modern linear algebra API for stdlib

‎README.md‎

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The talk will cover design challenges, templating strategy, one-macro integration and head-to-head performance against Python front-ends, and highlights current community contributions and roadmap items. Attendees will learn how to adopt, extend and benchmark this next-generation Fortran linear-algebra stack, positioning stdlib as an everyday alternative to established numerical platforms.
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##Repository Purpose
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This repository contains:
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- Code examples demonstrating the precision-agnostic BLAS/LAPACK interface
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- Test cases showcasing various precision levels (32-, 64-, 80-, 128-bit)
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- Performance benchmarks comparing stdlib with Python front-ends
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- Integration examples with third-party libraries (OpenBLAS, MKL, Accelerate)
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- Documentation and materials for the FortranCon 2025 presentation
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##Repository Structure
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```
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├── benchmarks/ # Performance comparison scripts
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│ ├── vs_python/ # Comparisons with NumPy/SciPy
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│ └── backends/ # Backend library comparisons
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├── media/ # Presentation materials and visualizations
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│ └── stars/ # Repository statistics and star history scripts
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├── docs/ # Additional documentation and slides
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├── CLAUDE.md # Guidelines for repository maintenance
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└── README.md # This file
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```
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##Key Features Demonstrated
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1.**Precision Agnostic Interface**
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- Real and complex arithmetic in 32-, 64-, 80-, and 128-bit kinds
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- Templated implementation for type flexibility
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2.**64-bit Integer Interface**
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- Support for arrays with >2 billion elements
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- Large-scale problem demonstrations
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3.**One-Macro Backend Switching**
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- Runtime selection between reference and optimized implementations
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- Integration with OpenBLAS, MKL, Accelerate
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4.**NumPy/SciPy-style API**
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- Pure functions and allocation-free subroutines
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- Intuitive operators for common operations
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- Zero-overhead semantics
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5.**Error Handling**
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- Lightweight state handler
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- Zero-cost optional error checking
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##Getting Started
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###Prerequisites
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- Modern Fortran compiler (gfortran 9+, ifort 19+, or equivalent)
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- Fortran Standard Library (stdlib)
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- Optional: OpenBLAS, MKL, or Accelerate for backend comparisons
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- Optional: Python 3.8+ with NumPy/SciPy for benchmarks
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###Building Examples
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```bash
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# Clone the repository
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git clone<repository-url>
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cd fortran-lapack-examples
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# Build all examples
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make all
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# Run specific example
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make run-example EXAMPLE=basic/matrix_multiply
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# Run tests
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maketest
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# Run benchmarks
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make benchmark
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```
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##Examples Overview
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###Basic Operations
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- Matrix-matrix multiplication
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- Matrix-vector operations
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- Linear system solvers
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###Precision Demonstrations
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- Same code running at different precisions
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- Mixed-precision calculations
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- Precision comparison studies
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###High-Level API
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- Determinant computation
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- Matrix inversion
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- Eigenvalue/eigenvector solvers
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- QR, LU, Cholesky factorizations
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###Backend Integration
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- Switching between reference and optimized libraries
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- Performance comparison across backends
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##Performance Results
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Performance comparisons will be added as benchmarks are completed.
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##Contributing
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##Presentation Materials
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This repository is part of the Fortran Standard Library effort. Contributions, examples, and feedback are welcome!
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The`media/stars/` directory contains scripts for generating repository statistics and visualizations (star history, commit activity, contributors, etc.). See[media/stars/README.md](media/stars/README.md) for details.
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##Related Links
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