<div align="center"> <p> <img style="width: 200px" width="200" src="https://raw.githubusercontent.com/vlang/vsl/main/static/vsl-logo.png?sanitize=true" > </p> <h1>The V Scientific Library</h1>

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VSL is a V library to develop Artificial Intelligence and High-Performance Scientific Computations.

[!IMPORTANT] The pure-V QR path (geqrf/orgqr) is still being aligned; the related test is temporarily skipped. Other BLAS/LAPACK routines pass, and C backends (-d vsl_blas_cblas -d vsl_lapack_lapacke) are recommended when you need QR correctness today.

🎉 Pure V BLAS/LAPACK: High Performance, Zero Dependencies

VSL now features 100% pure V implementations of BLAS and LAPACK that deliver excellent performance while requiring zero external dependencies. These implementations are production-ready and provide a compelling alternative to C backends.

Key Benefits

✅ Zero Dependencies: No need to install OpenBLAS or LAPACK system libraries
✅ High Performance: Competitive performance with optimized C backends
✅ Cross-Platform: Works consistently across all platforms supported by V
✅ Production Ready: Stable implementations with comprehensive test coverage

Performance Benchmarks

Run comprehensive benchmarks to see performance characteristics:

# BLAS benchmarks
v run benchmarks/blas_bench.v

# LAPACK benchmarks  
v run benchmarks/lapack_bench.v

# Compare backends
v run benchmarks/compare_backends.v

Backend Options

| Backend | Description | Compilation Flag | Best For | |---------|-------------|------------------|----------| | Pure V | 100% V implementation | NONE (default) | Zero-dependency deployment, cross-platform | | OpenBLAS | Optimized C library | -d vsl_blas_cblas | Maximum performance when C libraries available | | LAPACKE | Standard LAPACK C interface | -d vsl_lapack_lapacke | Maximum performance for LAPACK operations |

Recommendation: Start with the pure V backend for simplicity and zero dependencies. Use C backends when maximum performance is critical and system libraries are available.

See Pure V BLAS/LAPACK Release Notes for detailed information.

| | | | | | :----------------------------------: | :----------------------------: | :------------: | :-------------------: | | | | | | | | | | |

📖 Documentation

Visit VSL Documentation to explore all supported features and APIs.

VSL is a comprehensive Scientific Computing Library offering a rich ecosystem of mathematical and computational modules. The library provides both pure-V implementations and optional high-performance backends through established C and Fortran libraries.

🔬 Core Capabilities

Linear Algebra: Complete matrix and vector operations, eigenvalue decomposition, linear solvers
Machine Learning: Clustering algorithms (K-means), classification (KNN), regression, and NLP tools
Numerical Methods: Differentiation, integration, root finding, polynomial operations
Data Visualization: Advanced plotting with Plotly-style API supporting 2D/3D charts
Scientific Computing: FFT, statistical analysis, probability distributions
Parallel Computing: MPI support and OpenCL acceleration
Data I/O: HDF5 integration for scientific data formats

⚡ Performance Architecture

VSL provides flexible performance options:

Pure V Implementation: High-performance, dependency-free BLAS/LAPACK implementations
Optimized Backends: Optional integration with OpenBLAS, LAPACK, and MPI for maximum performance
GPU Acceleration: OpenCL support for computationally intensive operations

Pure V BLAS/LAPACK implementations deliver competitive performance while eliminating external dependencies. Benchmark results demonstrate excellent performance characteristics across a wide range of problem sizes.

Each module clearly documents compilation flags and backend requirements, allowing users to choose the optimal configuration for their specific use case.

🚀 Installation & Quick Start

VSL supports multiple installation methods and deployment options to fit different development workflows.

📦 Package Manager Installation

Via V's built-in package manager:

v install vsl

Via vpkg:

vpkg get https://github.com/vlang/vsl

🐳 Docker Development Environment (Recommended)

For the best development experience with all optional dependencies pre-configured:

Install Docker on your system

Clone the starter template:

git clone https://github.com/ulises-jeremias/hello-vsl
cd hello-vsl

Follow the setup instructions in the template's README

This approach provides:

Pre-configured environment with V, VSL, and all optional dependencies
Cross-platform compatibility (Windows, Linux, macOS)
Isolated development environment
Access to optimized BLAS/LAPACK libraries

🔧 System Dependencies (Optional)

For enhanced performance, you can install optional system libraries:

OpenBLAS/LAPACK: Linear algebra acceleration
OpenMPI: Parallel computing support
OpenCL: GPU acceleration
HDF5: Scientific data format support

Refer to individual module documentation for specific compilation flags.

🧪 Testing

To test the module, just type the following command:

v test .

📊 Performance Benchmarks

VSL includes comprehensive performance benchmarks using V's built-in benchmark module:

# Run all BLAS benchmarks
v run benchmarks/blas_bench.v

# Run all LAPACK benchmarks
v run benchmarks/lapack_bench.v

# Compare pure V vs C backends
v -d vsl_blas_cblas run benchmarks/compare_backends.v

Benchmark results show performance characteristics for:

BLAS Level 1: Vector operations (dot product, norms, scaling)
BLAS Level 2: Matrix-vector operations (GEMV, GER)
BLAS Level 3: Matrix-matrix operations (GEMM, SYRK)
LAPACK: Linear system solvers, factorizations, eigenvalue problems

See benchmarks/README.md for detailed benchmark documentation.

👥 Contributors

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Vsl

Install / Use

README