MatrixM

MatrixM is a cross-platform open-source library designed for high-performance matrix and tensor computations. It provides a broad set of linear algebra routines, numerical operators, and data structures aimed at scientific computing, data analysis, and machine learning. The project emphasizes performance, interoperability, and ease of integration into existing codebases.

Key features include support for dense and sparse matrices, multi-dimensional tensors, and a range of decompositions such as LU, QR, Cholesky, eigenvalue, and singular value decompositions. It offers GPU acceleration via CUDA and other backends, automatic differentiation for gradient-based optimization, and operator fusion to reduce memory traffic. The API seeks Python and C++ bindings with interfaces compatible with common numerical libraries.

Architecture and design: MatrixM employs a modular backend architecture that abstracts CPU and GPU execution, a central tensor engine, and a flexible memory allocator for large-scale workloads. It provides data sharing with established array ecosystems, conversion utilities, and optional just-in-time compilation to optimize performance for specific workloads.

Impact and use: The library targets researchers and engineers performing numerical simulations, data analysis, and machine learning experimentation. Documentation, tutorials, and example projects accompany MatrixM, and an active community contributes backends, extensions, and bug fixes, sustaining a growing ecosystem around the project.