About

Mailing list

We have a GitHub discussions forum to discuss usage and development of OpenBLAS. We also have a Google group for users and a Google group for development of OpenBLAS.

Acknowledgements

This work was or is partially supported by the following grants, contracts and institutions:

• Research and Development of Compiler System and Toolchain for Domestic CPU, National S&T Major Projects: Core Electronic Devices, High-end General Chips and Fundamental Software (No.2009ZX01036-001-002)
• National High-tech R&D Program of China (Grant No.2012AA010903)
• PerfXLab
• Chan Zuckerberg Initiative's Essential Open Source Software for Science program:
  • Cycle 1 grant: Strengthening NumPy's foundations - growing beyond code (2019-2020)
  • Cycle 3 grant: Improving usability and sustainability for NumPy and OpenBLAS (2020-2021)
• Sovereign Tech Fund funding: Keeping high performance linear algebra computation accessible and open for all (2023-2024)

Over the course of OpenBLAS development, a number of donations were received. You can read OpenBLAS's statement of receipts and disbursement and cash balance in this Google doc (covers 2013-2016). A list of backers is available in BACKERS.md in the main repo.

Donations

We welcome hardware donations, including the latest CPUs and motherboards.

Open source users of OpenBLAS

Prominent open source users of OpenBLAS include:

• Julia - a high-level, high-performance dynamic programming language for technical computing
• NumPy - the fundamental package for scientific computing with Python
• SciPy - fundamental algorithms for scientific computing in Python
• R - a free software environment for statistical computing and graphics
• OpenCV - the world's biggest computer vision library

OpenBLAS is packaged in most major Linux distros, as well as general and numerical computing-focused packaging ecosystems like Nix, Homebrew, Spack and conda-forge.

OpenBLAS is used directly by libraries written in C, C++ and Fortran (and probably other languages), and directly by end users in those languages.

Publications

2013

• Wang Qian, Zhang Xianyi, Zhang Yunquan, Qing Yi, AUGEM: Automatically Generate High Performance Dense Linear Algebra Kernels on x86 CPUs, In the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'13), Denver CO, November 2013. [pdf]

2012

• Zhang Xianyi, Wang Qian, Zhang Yunquan, Model-driven Level 3 BLAS Performance Optimization on Loongson 3A Processor, 2012 IEEE 18th International Conference on Parallel and Distributed Systems (ICPADS), 17-19 Dec. 2012.