Adding `oxiblas` library as a dependency

Run this command in a terminal, in your project's directory:

cargo add oxiblas

To add it manually, edit your project's Cargo.toml file and add to the [dependencies] section:

oxiblas = "0.2.1"

The oxiblas library will be automatically available globally. Read the oxiblas library documentation.

Back to the crate overview.

Readme

oxiblas

Unified API for OxiBLAS - Pure Rust BLAS/LAPACK implementation

Overview

oxiblas is the meta-crate that re-exports all OxiBLAS functionality through a unified, convenient API. This is the recommended way to use OxiBLAS for most users.

Features

Complete BLAS - Level 1, 2, 3 operations
Extensive LAPACK - LU, QR, SVD, Cholesky, EVD, and more
Sparse matrices - 9 formats with iterative solvers
Tensor operations - Einstein summation, batched operations
Extended precision - f16, f128 support
High performance - 80-172% of OpenBLAS depending on operation
Pure Rust - No C dependencies, easy cross-compilation

Quick Start

[dependencies]
oxiblas = "0.2"

# With all features
oxiblas = { version = "0.2", features = ["full"] }

Usage

Matrix Operations

use oxiblas::prelude::*;

// Create matrices
let a = Mat::from_rows(&[
    &[1.0, 2.0, 3.0],
    &[4.0, 5.0, 6.0],
]);

let b = Mat::from_rows(&[
    &[7.0, 8.0],
    &[9.0, 10.0],
    &[11.0, 12.0],
]);

// Matrix multiplication
let mut c = Mat::zeros(2, 2);
gemm(1.0, a.as_ref(), b.as_ref(), 0.0, c.as_mut());
// c = [[58, 64], [139, 154]]

Linear Algebra

use oxiblas::prelude::*;

let a = Mat::from_rows(&[
    &[2.0, 1.0, 1.0],
    &[4.0, 3.0, 3.0],
    &[8.0, 7.0, 9.0],
]);

// LU decomposition
let lu = Lu::compute(a.as_ref())?;
let det = lu.determinant();
let inv = lu.inverse()?;

// QR decomposition
let qr = Qr::compute(a.as_ref())?;
let q = qr.q();
let r = qr.r();

// SVD
let svd = Svd::compute(a.as_ref())?;
let singular_values = svd.singular_values();

// Solve Ax = b
let b = vec![4.0, 10.0, 24.0];
let x = lu.solve(&b)?;

Sparse Matrices

use oxiblas::prelude::*;

// Create sparse matrix
let mut coo = CooMatrix::<f64>::new(1000, 1000);
coo.push(0, 0, 4.0);
coo.push(0, 1, -1.0);
// ... add more elements

let csr = CsrMatrix::from_coo(&coo);

// Solve sparse system with GMRES
let b = vec![/*...*/];
let result = gmres(&csr, &b, 1e-10, 100, 30, None)?;

Tensor Operations

use oxiblas::prelude::*;

// Einstein summation
let c = einsum("ij,jk->ik", &a, &[m, n], Some((&b, &[n, k])))?;

// Batched matrix multiplication
let a_batch = Tensor3::from_data(data_a, batch, m, k);
let b_batch = Tensor3::from_data(data_b, batch, k, n);
let c_batch = batched_matmul(&a_batch, &b_batch)?;

Module Structure

The oxiblas crate re-exports from these sub-crates:

Module	Re-exported from	Description
`oxiblas::core`	`oxiblas-core`	Core traits, SIMD, scalar types
`oxiblas::matrix`	`oxiblas-matrix`	Matrix types and views
`oxiblas::blas`	`oxiblas-blas`	BLAS operations
`oxiblas::lapack`	`oxiblas-lapack`	LAPACK decompositions
`oxiblas::sparse`	`oxiblas-sparse`	Sparse matrices and solvers
`oxiblas::ndarray`	`oxiblas-ndarray`	ndarray integration (optional)
	`oxiblas-ffi`	RETIRED (v0.2.1) - C FFI bindings

Prelude

The oxiblas::prelude module provides convenient imports:

use oxiblas::prelude::*;

// Now you have access to:
// - Mat, MatRef, MatMut (matrix types)
// - gemm, gemv, dot, axpy, etc. (BLAS operations)
// - Lu, Qr, Svd, Cholesky, etc. (LAPACK decompositions)
// - CsrMatrix, CooMatrix, etc. (sparse matrices)
// - gmres, cg, bicgstab, etc. (sparse solvers)

Feature Flags

Feature	Description	Default
`default`	Core functionality (f32, f64, complex)	✓
`parallel`	Rayon-based parallelization
`f16`	Half-precision (16-bit) floating point
`f128`	Quad-precision (~31 digits)
`sparse`	Sparse matrix operations	✓
`ndarray`	ndarray integration
`ffi`	C FFI bindings
`full`	All features enabled
`nightly`	Nightly-only optimizations

Examples

# Minimal (dense matrices only)
oxiblas = "0.2"

# With parallelization
oxiblas = { version = "0.2", features = ["parallel"] }

# With extended precision
oxiblas = { version = "0.2", features = ["f16", "f128"] }

# With ndarray support
oxiblas = { version = "0.2", features = ["ndarray"] }

# All features
oxiblas = { version = "0.2", features = ["full"] }

Examples

The repository includes comprehensive examples:

# Basic BLAS operations
cargo run --example basic_blas

# LAPACK decompositions
cargo run --example lapack_decompositions

# Extended precision
cargo run --example extended_precision --features f128

# Tensor operations
cargo run --example tensor_operations

# Sparse matrices
cargo run --example sparse_matrices --features parallel

Performance

OxiBLAS provides competitive performance with industry-standard libraries:

macOS M3 (Apple Silicon)

Operation	OxiBLAS	OpenBLAS	Ratio
DGEMM 1024×1024	40.25 ms	40.54 ms	101%
SGEMM 1024×1024	19.18 ms	32.94 ms	172%
DOT 1M elements	167 µs	279 µs	165%

Linux x86_64 (Intel Xeon)

Operation	OxiBLAS	OpenBLAS	Ratio
DGEMM 1024×1024	80.68 ms	82.51 ms	102%
SGEMM 64×64	16.60 µs	18.64 µs	112%
DGEMM 256×256	1.220 ms	1.159 ms	95%

Summary: OxiBLAS achieves 80-172% of OpenBLAS performance across different platforms and operations.

Documentation

Main README - Project overview and features
API Documentation - Complete API reference
Examples - Usage examples
Benchmarks README - Performance benchmarking guide

Sub-Crate Documentation

For more detailed documentation on specific components:

oxiblas-core - Core traits and SIMD
oxiblas-matrix - Matrix types
oxiblas-blas - BLAS operations
oxiblas-lapack - LAPACK decompositions
oxiblas-sparse - Sparse matrices
oxiblas-ndarray - ndarray integration
oxiblas-benchmarks - Benchmarking suite

Ecosystem

OxiBLAS is part of the SciRS2 scientific computing ecosystem:

SciRS2 - Scientific computing library
NumRS2 - Numerical computing
SkleaRS - Machine learning
ToRSh - Tensor operations
TrustformeRS - Transformers
QuantRS2 - Quantum computing framework
OxiRS - Semantic Web platform

Requirements

Rust: 1.85+ (Edition 2024)
No external C dependencies
Supported platforms: x86_64, AArch64 (Linux, macOS, Windows)

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Citation

If you use OxiBLAS in your research, please cite:

@software{oxiblas2026,
  author = {OxiBLAS Contributors},
  title = {OxiBLAS: Pure Rust BLAS/LAPACK Implementation},
  year = {2026},
  url = {https://github.com/cool-japan/oxiblas}
}

Adding oxiblas library as a dependency

Adding `oxiblas` library as a dependency