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<h1>About</h1>
<h2 id="mailing-list">Mailing list</h2>
<p>We have a <a href="https://github.com/OpenMathLib/OpenBLAS/discussions/">GitHub discussions</a> forum to discuss usage and development of OpenBLAS. We also have a <a href="https://groups.google.com/forum/#!forum/openblas-users">Google group for <em>users</em></a> and a <a href="https://groups.google.com/forum/#!forum/openblas-dev">Google group for <em>development of</em></a> OpenBLAS. </p>
<h2 id="acknowledgements">Acknowledgements</h2>
<p>This work was or is partially supported by the following grants, contracts and institutions:</p>
<ul>
<li>Research and Development of Compiler System and Toolchain for Domestic CPU, National S&amp;T Major Projects: Core Electronic Devices, High-end General Chips and Fundamental Software (No.2009ZX01036-001-002)</li>
<li>National High-tech R&amp;D Program of China (Grant No.2012AA010903)</li>
<li><a href="http://www.perfxlab.com/">PerfXLab</a></li>
<li>Chan Zuckerberg Initiative's Essential Open Source Software for Science program:<ul>
<li>Cycle 1 grant: <a href="https://figshare.com/articles/journal_contribution/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167">Strengthening NumPy's foundations - growing beyond code</a> (2019-2020)</li>
<li>Cycle 3 grant: <a href="https://chanzuckerberg.com/eoss/proposals/improving-usability-and-sustainability-for-numpy-and-openblas/">Improving usability and sustainability for NumPy and OpenBLAS</a> (2020-2021)</li>
</ul>
</li>
<li>Sovereign Tech Fund funding: <a href="https://www.sovereigntechfund.de/tech/openblas">Keeping high performance linear algebra computation accessible and open for all</a> (2023-2024)</li>
</ul>
<p>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
<a href="https://docs.google.com/spreadsheet/ccc?key=0AghkTjXe2lDndE1UZml0dGpaUzJmZGhvenBZd1F2R1E&amp;usp=sharing">this Google doc</a> (covers 2013-2016).
A list of backers is available <a href="https://github.com/OpenMathLib/OpenBLAS/blob/develop/BACKERS.md">in BACKERS.md</a> in the main repo.</p>
<h3 id="donations">Donations</h3>
<p>We welcome hardware donations, including the latest CPUs and motherboards.</p>
<h2 id="open-source-users-of-openblas">Open source users of OpenBLAS</h2>
<p>Prominent open source users of OpenBLAS include:</p>
<ul>
<li><a href="https://julialang.org">Julia</a> - a high-level, high-performance dynamic programming language for technical computing</li>
<li><a href="https://numpy.org">NumPy</a> - the fundamental package for scientific computing with Python</li>
<li><a href="https://scipy.org">SciPy</a> - fundamental algorithms for scientific computing in Python</li>
<li><a href="https://www.r-project.org/">R</a> - a free software environment for statistical computing and graphics</li>
<li><a href="https://opencv.org/">OpenCV</a> - the world's biggest computer vision library</li>
</ul>
<p>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.</p>
<p>OpenBLAS is used directly by libraries written in C, C++ and Fortran (and
probably other languages), and directly by end users in those languages.</p>
<h2 id="publications">Publications</h2>
<h3 id="2013">2013</h3>
<ul>
<li>Wang Qian, Zhang Xianyi, Zhang Yunquan, Qing Yi, <strong>AUGEM: Automatically Generate High Performance Dense Linear Algebra Kernels on x86 CPUs</strong>, In the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'13), Denver CO, November 2013. [<a href="http://xianyi.github.io/paper/augem_SC13.pdf">pdf</a>]</li>
</ul>
<h3 id="2012">2012</h3>
<ul>
<li>Zhang Xianyi, Wang Qian, Zhang Yunquan, <strong>Model-driven Level 3 BLAS Performance Optimization on Loongson 3A Processor</strong>, 2012 IEEE 18th International Conference on Parallel and Distributed Systems (ICPADS), 17-19 Dec. 2012.</li>
</ul>
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