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