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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="donations">Donations</h2>
<p>You can read OpenBLAS statement of receipts and disbursement and cash balance on <a href="https://docs.google.com/spreadsheet/ccc?key=0AghkTjXe2lDndE1UZml0dGpaUzJmZGhvenBZd1F2R1E&amp;usp=sharing">google doc</a>. A backer list is available <a href="https://github.com/OpenMathLib/OpenBLAS/blob/develop/BACKERS.md">on GitHub</a>.</p>
<p>We welcome the hardware donation, including the latest CPU and boards.</p>
<h2 id="acknowledgements">Acknowledgements</h2>
<p>This work is partially supported by
* 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)
* National High-tech R&amp;D Program of China (Grant No.2012AA010903)</p>
<h2 id="users-of-openblas">Users of OpenBLAS</h2>
<p>This work was or is partially supported by the following grants, contracts and institutions:</p>
<ul>
<li><a href='http://julialang.org/'>Julia - a high-level, high-performance dynamic programming language for technical computing</a><br /></li>
<li>Ceemple v1.0.3 (C++ technical computing environment), including OpenBLAS, Qt, Boost, OpenCV and others. The only solution with immediate-recompilation of C++ code. Available from <a href='http://www.ceemple.com'>Ceemple C++ Technical Computing</a>.</li>
<li><a href="https://github.com/fommil/netlib-java">netlib-java</a> and various upstream libraries, allowing OpenBLAS to be used from languages on the Java Virtual Machine.</li>
<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>
<!-- TODO: academia users, industry users, hpc centers deployed openblas, etc. -->
</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>
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@ -569,8 +621,15 @@
<h1>Build system</h1>
<p>This page describes the Make-based build, which is the default/authoritative
build method. Note that the OpenBLAS repository also supports building with
CMake (not described here) - that generally works and is tested, however there
may be small differences between the Make and CMake builds.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This page is made by someone who is not the developer and should not be considered as an official documentation of the build system. For getting the full picture, it is best to read the Makefiles and understand them yourself.</p>
@ -656,13 +715,23 @@ USE_OPENMP - Use OpenMP as multithreading backend
NUM_THREADS - define this to the maximum number of parallel threads you expect to need (defaults to the number of cores in the build cpu)
NUM_PARALLEL - define this to the number of OpenMP instances that your code may use for parallel calls into OpenBLAS (default 1,see below)
</code></pre></div>
<p>OpenBLAS uses a fixed set of memory buffers internally, used for communicating and compiling partial results from individual threads.
For efficiency, the management array structure for these buffers is sized at build time - this makes it necessary to know in advance how
many threads need to be supported on the target system(s).
With OpenMP, there is an additional level of complexity as there may be calls originating from a parallel region in the calling program. If OpenBLAS gets called from a single parallel region, it runs single-threaded automatically to avoid overloading the system by fanning out its own set of threads.
In the case that an OpenMP program makes multiple calls from independent regions or instances in parallel, this default serialization is not
sufficient as the additional caller(s) would compete for the original set of buffers already in use by the first call.
So if multiple OpenMP runtimes call into OpenBLAS at the same time, then only one of them will be able to make progress while all the rest of them spin-wait for the one available buffer. Setting NUM_PARALLEL to the upper bound on the number of OpenMP runtimes that you can have in a process ensures that there are a sufficient number of buffer sets available</p>
<p>OpenBLAS uses a fixed set of memory buffers internally, used for communicating
and compiling partial results from individual threads. For efficiency, the
management array structure for these buffers is sized at build time - this
makes it necessary to know in advance how many threads need to be supported on
the target system(s).</p>
<p>With OpenMP, there is an additional level of complexity as there may be calls
originating from a parallel region in the calling program. If OpenBLAS gets
called from a single parallel region, it runs single-threaded automatically to
avoid overloading the system by fanning out its own set of threads. In the case
that an OpenMP program makes multiple calls from independent regions or
instances in parallel, this default serialization is not sufficient as the
additional caller(s) would compete for the original set of buffers already in
use by the first call. So if multiple OpenMP runtimes call into OpenBLAS at the
same time, then only one of them will be able to make progress while all the
rest of them spin-wait for the one available buffer. Setting <code>NUM_PARALLEL</code> to
the upper bound on the number of OpenMP runtimes that you can have in a process
ensures that there are a sufficient number of buffer sets available.</p>
@ -676,6 +745,38 @@ So if multiple OpenMP runtimes call into OpenBLAS at the same time, then only on
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@ -691,12 +792,42 @@ So if multiple OpenMP runtimes call into OpenBLAS at the same time, then only on
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@ -709,7 +840,7 @@ So if multiple OpenMP runtimes call into OpenBLAS at the same time, then only on
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<h1 id="developer-manual">Developer manual</h1>
<h2 id="source-codes-layout">Source codes Layout</h2>
<h2 id="source-code-layout">Source code layout</h2>
<div class="highlight"><pre><span></span><code>OpenBLAS/
├── benchmark Benchmark codes for BLAS
├── cmake CMakefiles
@ -647,73 +702,122 @@
├── test Test codes for BLAS
└── utest Regression test
</code></pre></div>
<p>A call tree for <code>dgemm</code> is as following.</p>
<p>A call tree for <code>dgemm</code> looks as follows:
<div class="highlight"><pre><span></span><code>interface/gemm.c
│
driver/level3/level3.c
│
gemm assembly kernels at kernel/
</code></pre></div>
<p>To find the kernel currently used for a particular supported cpu, please check the corresponding <code>kernel/$(ARCH)/KERNEL.$(CPU)</code> file.</p>
<p>Here is an example for <code>kernel/x86_64/KERNEL.HASWELL</code></p>
<p><div class="highlight"><pre><span></span><code>...
</code></pre></div></p>
<p>To find the kernel currently used for a particular supported CPU, please check the corresponding <code>kernel/$(ARCH)/KERNEL.$(CPU)</code> file.</p>
<p>Here is an example for <code>kernel/x86_64/KERNEL.HASWELL</code>:
<div class="highlight"><pre><span></span><code>...
DTRMMKERNEL = dtrmm_kernel_4x8_haswell.c
DGEMMKERNEL = dgemm_kernel_4x8_haswell.S
...
</code></pre></div>
According to the above <code>KERNEL.HASWELL</code>, OpenBLAS Haswell dgemm kernel file is <code>dgemm_kernel_4x8_haswell.S</code>.</p>
<h2 id="optimizing-gemm-for-a-given-hardware">Optimizing GEMM for a given hardware</h2>
<p>Read the Goto paper to understand the algorithm.</p>
<p>Goto, Kazushige; van de Geijn, Robert A. (2008). <a href="http://delivery.acm.org/10.1145/1360000/1356053/a12-goto.pdf?ip=155.68.162.54&amp;id=1356053&amp;acc=ACTIVE%20SERVICE&amp;key=A79D83B43E50B5B8%2EF070BBE7E45C3F17%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&amp;__acm__=1517932837_edfe766f1e295d9a7830812371e1d173">"Anatomy of High-Performance Matrix Multiplication"</a>. ACM Transactions on Mathematical Software 34 (3): Article 12
(The above link is available only to ACM members, but this and many related papers is also available on the pages
of van de Geijn's FLAME project, http://www.cs.utexas.edu/~flame/web/FLAMEPublications.html )</p>
<p>The <code>driver/level3/level3.c</code> is the implementation of Goto's algorithm. Meanwhile, you can look at <code>kernel/generic/gemmkernel_2x2.c</code>, which is a naive <code>2x2</code> register blocking gemm kernel in C.</p>
<p>Then,
* Write optimized assembly kernels. consider instruction pipeline, available registers, memory/cache accessing
* Tuning cache block size, <code>Mc</code>, <code>Kc</code>, and <code>Nc</code> </p>
<p>Note that not all of the cpu-specific parameters in param.h are actively used in algorithms. DNUMOPT only appears as a scale factor in profiling output of the level3 syrk interface code, while its counterpart SNUMOPT (aliased as NUMOPT in common.h) is not used anywhere at all.
SYMV_P is only used in the generic kernels for the symv and chemv/zhemv functions - at least some of those are usually overridden by cpu-specific implementations, so if you start by cloning the existing implementation for a related cpu you need to check its KERNEL file to see if tuning SYMV_P would have any effect at all.
GEMV_UNROLL is only used by some older x86_64 kernels, so not all sections in param.h define it.
Similarly, not all of the cpu parameters like L2 or L3 cache sizes are necessarily used in current kernels for a given model - by all indications the cpu identification code was imported from some other project originally.</p>
<h2 id="run-openblas-test">Run OpenBLAS Test</h2>
<p>We use netlib blas test, cblas test, and LAPACK test. Meanwhile, we use <a href="https://github.com/xianyi/BLAS-Tester">BLAS-Tester</a>, a modified test tool from ATLAS.</p>
<div class="admonition abstract">
<p class="admonition-title">Read the Goto paper to understand the algorithm</p>
<p>Goto, Kazushige; van de Geijn, Robert A. (2008).
<a href="http://delivery.acm.org/10.1145/1360000/1356053/a12-goto.pdf?ip=155.68.162.54&amp;id=1356053&amp;acc=ACTIVE%20SERVICE&amp;key=A79D83B43E50B5B8%2EF070BBE7E45C3F17%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&amp;__acm__=1517932837_edfe766f1e295d9a7830812371e1d173">"Anatomy of High-Performance Matrix Multiplication"</a>.
ACM Transactions on Mathematical Software 34 (3): Article 12</p>
<p>(The above link is available only to ACM members, but this and many related
papers is also available on <a href="http://www.cs.utexas.edu/~flame/web/FLAMEPublications.html">the pages of van de Geijn's FLAME project</a>)</p>
</div>
<p>The <code>driver/level3/level3.c</code> is the implementation of Goto's algorithm.
Meanwhile, you can look at <code>kernel/generic/gemmkernel_2x2.c</code>, which is a naive
<code>2x2</code> register blocking <code>gemm</code> kernel in C. Then:</p>
<ul>
<li>Run <code>test</code> and <code>ctest</code> at OpenBLAS. e.g. <code>make test</code> or <code>make ctest</code>.</li>
<li>Run regression test <code>utest</code> at OpenBLAS.</li>
<li>Run LAPACK test. e.g. <code>make lapack-test</code>.</li>
<li>Clone <a href="https://github.com/xianyi/BLAS-Tester">BLAS-Tester</a>, which can compare the OpenBLAS result with netlib reference BLAS.</li>
<li>Write optimized assembly kernels. Consider instruction pipeline, available registers, memory/cache access.</li>
<li>Tune cache block sizes (<code>Mc</code>, <code>Kc</code>, and <code>Nc</code>)</li>
</ul>
<p>The project makes use of several Continuous Integration (CI) services conveniently interfaced with github to automatically check compilability on a number of platforms.
Lastly, the testsuites included with "numerically heavy" projects like Julia, NumPy, Octave or QuantumEspresso can be used for regression testing.</p>
<p>Note that not all of the CPU-specific parameters in <code>param.h</code> are actively used in algorithms.
<code>DNUMOPT</code> only appears as a scale factor in profiling output of the level3 <code>syrk</code> interface code,
while its counterpart <code>SNUMOPT</code> (aliased as <code>NUMOPT</code> in <code>common.h</code>) is not used anywhere at all. </p>
<p><code>SYMV_P</code> is only used in the generic kernels for the <code>symv</code> and <code>chemv</code>/<code>zhemv</code> functions -
at least some of those are usually overridden by CPU-specific implementations, so if you start
by cloning the existing implementation for a related CPU you need to check its <code>KERNEL</code> file
to see if tuning <code>SYMV_P</code> would have any effect at all.</p>
<p><code>GEMV_UNROLL</code> is only used by some older x86-64 kernels, so not all sections in <code>param.h</code> define it.
Similarly, not all of the CPU parameters like L2 or L3 cache sizes are necessarily used in current
kernels for a given model - by all indications the CPU identification code was imported from some
other project originally.</p>
<h2 id="running-openblas-tests">Running OpenBLAS tests</h2>
<p>We use tests for Netlib BLAS, CBLAS, and LAPACK. In addition, we use
OpenBLAS-specific regression tests. They can be run with Make:</p>
<ul>
<li><code>make -C test</code> for BLAS tests</li>
<li><code>make -C ctest</code> for CBLAS tests</li>
<li><code>make -C utest</code> for OpenBLAS regression tests</li>
<li><code>make lapack-test</code> for LAPACK tests</li>
</ul>
<p>We also use the <a href="https://github.com/xianyi/BLAS-Tester">BLAS-Tester</a> tests for regression testing.
It is basically the ATLAS test suite adapted for building with OpenBLAS.</p>
<p>The project makes use of several Continuous Integration (CI) services
conveniently interfaced with GitHub to automatically run tests on a number of
platforms and build configurations.</p>
<p>Also note that the test suites included with "numerically heavy" projects like
Julia, NumPy, SciPy, Octave or QuantumEspresso can be used for regression
testing, when those projects are built such that they use OpenBLAS.</p>
<h2 id="benchmarking">Benchmarking</h2>
<p>Several simple C benchmarks for performance testing individual BLAS functions are available in the <code>benchmark</code> folder, and its <code>scripts</code> subdirectory contains corresponding versions for Python, Octave and R.
Other options include</p>
<p>A number of benchmarking methods are used by OpenBLAS:</p>
<ul>
<li>https://github.com/RoyiAvital/MatlabJuliaMatrixOperationsBenchmark (various matrix operations in Julia and Matlab)</li>
<li>https://github.com/mmperf/mmperf/ (single-core matrix multiplication)</li>
<li>Several simple C benchmarks for performance testing individual BLAS functions
are available in the <code>benchmark</code> folder. They can be run locally through the
<code>Makefile</code> in that directory. And the <code>benchmark/scripts</code> subdirectory
contains similar benchmarks that use OpenBLAS via NumPy, SciPy, Octave and R.</li>
<li>On pull requests, a representative set of functions is tested for performance
regressions with Codspeed; results can be viewed at
<a href="https://codspeed.io/OpenMathLib/OpenBLAS">https://codspeed.io/OpenMathLib/OpenBLAS</a>.</li>
<li>The <a href="https://github.com/OpenMathLib/BLAS-Benchmarks">OpenMathLib/BLAS-Benchmarks</a> repository
contains an <a href="https://github.com/airspeed-velocity/asv/">Airspeed Velocity</a>-based benchmark
suite which is run on several CPU architectures in cron jobs. Results are published
to a dashboard: <a href="http://www.openmathlib.org/BLAS-Benchmarks/">http://www.openmathlib.org/BLAS-Benchmarks/</a>.</li>
</ul>
<h2 id="adding-autodetection-support-for-a-new-revision-or-variant-of-a-supported-cpu">Adding autodetection support for a new revision or variant of a supported cpu</h2>
<p>Especially relevant for x86_64, a new cpu model may be a "refresh" (die shrink and/or different number of cores) within an existing
model family without significant changes to its instruction set. (e.g. Intel Skylake, Kaby Lake etc. still are fundamentally Haswell,
low end Goldmont etc. are Nehalem). In this case, compilation with the appropriate older TARGET will already lead to a satisfactory build.</p>
<p>Benchmarking code for BLAS libraries, and specific performance analysis results, can be found
in a number of places. For example:</p>
<ul>
<li><a href="https://github.com/RoyiAvital/MatlabJuliaMatrixOperationsBenchmark">MatlabJuliaMatrixOperationsBenchmark</a>
(various matrix operations in Julia and Matlab)</li>
<li><a href="https://github.com/mmperf/mmperf/">mmperf/mmperf</a> (single-core matrix multiplication)</li>
</ul>
<h2 id="adding-autodetection-support-for-a-new-revision-or-variant-of-a-supported-cpu">Adding autodetection support for a new revision or variant of a supported CPU</h2>
<p>Especially relevant for x86-64, a new CPU model may be a "refresh" (die shrink and/or different number of cores) within an existing
model family without significant changes to its instruction set (e.g., Intel Skylake and Kaby Lake still are fundamentally the same architecture as Haswell,
low end Goldmont etc. are Nehalem). In this case, compilation with the appropriate older <code>TARGET</code> will already lead to a satisfactory build.</p>
<p>To achieve autodetection of the new model, its CPUID (or an equivalent identifier) needs to be added in the <code>cpuid_&lt;architecture&gt;.c</code>
relevant for its general architecture, with the returned name for the new type set appropriately. For x86 which has the most complex
cpuid file, there are two functions that need to be edited - get_cpuname() to return e.g. CPUTYPE_HASWELL and get_corename() for the (broader)
core family returning e.g. CORE_HASWELL. (This information ends up in the Makefile.conf and config.h files generated by <code>getarch</code>. Failure to
set either will typically lead to a missing definition of the GEMM_UNROLL parameters later in the build, as <code>getarch_2nd</code> will be unable to
find a matching parameter section in param.h.)</p>
<p>For architectures where "DYNAMIC_ARCH" builds are supported, a similar but simpler code section for the corresponding runtime detection of the cpu exists in <code>driver/others/dynamic.c</code> (for x86) and <code>driver/others/dynamic_&lt;arch&gt;.c</code> for other architectures.<br />
relevant for its general architecture, with the returned name for the new type set appropriately. For x86, which has the most complex
<code>cpuid</code> file, there are two functions that need to be edited: <code>get_cpuname()</code> to return, e.g., <code>CPUTYPE_HASWELL</code> and <code>get_corename()</code> for the (broader)
core family returning, e.g., <code>CORE_HASWELL</code>.<sup id="fnref:1"><a class="footnote-ref" href="#fn:1">1</a></sup></p>
<p>For architectures where <code>DYNAMIC_ARCH</code> builds are supported, a similar but simpler code section for the corresponding
runtime detection of the CPU exists in <code>driver/others/dynamic.c</code> (for x86), and <code>driver/others/dynamic_&lt;arch&gt;.c</code> for other architectures.
Note that for x86 the CPUID is compared after splitting it into its family, extended family, model and extended model parts, so the single decimal
number returned by Linux in /proc/cpuinfo for the model has to be converted back to hexadecimal before splitting into its constituent
digits, e.g. 142 = 8E , translates to extended model 8, model 14.</p>
<h2 id="adding-dedicated-support-for-a-new-cpu-model">Adding dedicated support for a new cpu model</h2>
<p>Usually it will be possible to start from an existing model, clone its KERNEL configuration file to the new name to use for this TARGET and eventually replace individual kernels with versions better suited for peculiarities of the new cpu model. In addition, it is necessary to add
(or clone at first) the corresponding section of GEMM_UNROLL parameters in the toplevel param.h, and possibly to add definitions such as USE_TRMM
(governing whether TRMM functions use the respective GEMM kernel or a separate source file) to the Makefiles (and CMakeLists.txt) in the kernel
directory. The new cpu name needs to be added to TargetLists.txt and the cpu autodetection code used by the <code>getarch</code> helper program - contained in
number returned by Linux in <code>/proc/cpuinfo</code> for the model has to be converted back to hexadecimal before splitting into its constituent
digits. For example, <code>142 == 8E</code> translates to extended model 8, model 14.</p>
<h2 id="adding-dedicated-support-for-a-new-cpu-model">Adding dedicated support for a new CPU model</h2>
<p>Usually it will be possible to start from an existing model, clone its <code>KERNEL</code> configuration file to the new name to use for this
<code>TARGET</code> and eventually replace individual kernels with versions better suited for peculiarities of the new CPU model.
In addition, it is necessary to add (or clone at first) the corresponding section of <code>GEMM_UNROLL</code> parameters in the top-level <code>param.h</code>,
and possibly to add definitions such as <code>USE_TRMM</code> (governing whether <code>TRMM</code> functions use the respective <code>GEMM</code> kernel or a separate source file)
to the <code>Makefile</code>s (and <code>CMakeLists.txt</code>) in the kernel directory. The new CPU name needs to be added to <code>TargetList.txt</code>,
and the CPU auto-detection code used by the <code>getarch</code> helper program - contained in
the <code>cpuid_&lt;architecture&gt;.c</code> file amended to include the CPUID (or equivalent) information processing required (see preceding section).</p>
<h2 id="adding-support-for-an-entirely-new-architecture">Adding support for an entirely new architecture</h2>
<p>This endeavour is best started by cloning the entire support structure for 32bit ARM, and within that the ARMV5 cpu in particular as this is implemented through plain C kernels only. An example providing a convenient "shopping list" can be seen in pull request #1526.</p>
<p>This endeavour is best started by cloning the entire support structure for 32-bit ARM, and within that the ARMv5 CPU in particular,
as this is implemented through plain C kernels only. An example providing a convenient "shopping list" can be seen in pull request
<a href="https://github.com/OpenMathLib/OpenBLAS/pull/1526">#1526</a>.</p>
<div class="footnote">
<hr />
<ol>
<li id="fn:1">
<p>This information ends up in the <code>Makefile.conf</code> and <code>config.h</code> files generated by <code>getarch</code>. Failure to
set either will typically lead to a missing definition of the <code>GEMM_UNROLL</code> parameters later in the build,
as <code>getarch_2nd</code> will be unable to find a matching parameter section in <code>param.h</code>.&#160;<a class="footnote-backref" href="#fnref:1" title="Jump back to footnote 1 in the text">&#8617;</a></p>
</li>
</ol>
</div>
@ -727,6 +831,38 @@ the <code>cpuid_&lt;architecture&gt;.c</code> file amended to include the CPUID
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@ -623,6 +675,9 @@
<h1 id="redistributing-openblas">Redistributing OpenBLAS</h1>
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<p class="admonition-title">Note</p>
@ -663,7 +718,7 @@ settings):</p>
<li>Include LAPACK and LAPACKE. The LAPACK interface is also widely used, and
while it does make up a significant part of the binary size of the installed
library, that does not outweigh the regression in usability when deviating
from the default here.[^1]</li>
from the default here.<sup id="fnref:1"><a class="footnote-ref" href="#fn:1">1</a></sup></li>
<li>Always distribute the pkg-config (<code>.pc</code>) and CMake <code>.cmake</code>) dependency
detection files. These files are used by build systems when users want to
link against OpenBLAS, and there is no benefit of leaving them out.</li>
@ -671,9 +726,6 @@ settings):</p>
to provide an ILP64 interface build as well, use a symbol suffix to avoid
symbol name clashes (see the next section).</li>
</ol>
<p>[^1] All major distributions do include LAPACK as of mid 2023 as far as we
know. Older versions of Arch Linux did not, and that was known to cause
problems.</p>
<h2 id="ilp64-interface-builds">ILP64 interface builds</h2>
<p>The LP64 (32-bit integer) interface is the default build, and has
well-established C and Fortran APIs as determined by the reference (Netlib)
@ -891,6 +943,16 @@ helpful as a reference:</p>
<li><a href="https://github.com/MacPython/openblas-libs/blob/main/tools/build_openblas.sh">NumPy/SciPy</a></li>
<li><a href="https://github.com/NixOS/nixpkgs/blob/master/pkgs/development/libraries/science/math/openblas/default.nix">Nixpkgs</a></li>
</ul>
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<hr />
<ol>
<li id="fn:1">
<p>All major distributions do include LAPACK as of mid 2023 as far as we
know. Older versions of Arch Linux did not, and that was known to cause
problems.&#160;<a class="footnote-backref" href="#fnref:1" title="Jump back to footnote 1 in the text">&#8617;</a></p>
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<h1>Extensions</h1>
<ul>
<li>BLAS-like extensions</li>
</ul>
<p>OpenBLAS for the most part contains implementations of the reference (Netlib)
BLAS, CBLAS, LAPACK and LAPACKE interfaces. A few OpenBLAS-specific functions
are also provided however, which mostly can be seen as "BLAS extensions".
This page documents those non-standard APIs.</p>
<h2 id="blas-like-extensions">BLAS-like extensions</h2>
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@ -472,26 +624,26 @@
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<h2 id="bfloat16-functionality">bfloat16 functionality</h2>
<p>BLAS-like and conversion functions for <code>bfloat16</code> (available when OpenBLAS was compiled with <code>BUILD_BFLOAT16=1</code>):</p>
<ul>
<li>BLAS-like and Conversion functions for bfloat16 (available when OpenBLAS was compiled with BUILD_BFLOAT16=1)</li>
<li><code>void cblas_sbstobf16</code> converts a float array to an array of bfloat16 values by rounding</li>
<li><code>void cblas_sbdtobf16</code> converts a double array to an array of bfloat16 values by rounding</li>
<li><code>void cblas_sbf16tos</code> converts a bfloat16 array to an array of floats</li>
<li><code>void cblas_dbf16tod</code> converts a bfloat16 array to an array of doubles</li>
<li><code>float cblas_sbdot</code> computes the dot product of two bfloat16 arrays</li>
<li><code>void cblas_sbgemv</code> performs the matrix-vector operations of GEMV with the input matrix and X vector as bfloat16 </li>
<li>
<p><code>void cblas_sbgemm</code> performs the matrix-matrix operations of GEMM with both input arrays containing bfloat16</p>
</li>
<li>
<p>Utility functions</p>
</li>
<li>openblas_get_num_threads</li>
<li>openblas_set_num_threads</li>
<li><code>void cblas_sbgemv</code> performs the matrix-vector operations of GEMV with the input matrix and X vector as bfloat16</li>
<li><code>void cblas_sbgemm</code> performs the matrix-matrix operations of GEMM with both input arrays containing bfloat16</li>
</ul>
<h2 id="utility-functions">Utility functions</h2>
<ul>
<li><code>openblas_get_num_threads</code></li>
<li><code>openblas_set_num_threads</code></li>
<li><code>int openblas_get_num_procs(void)</code> returns the number of processors available on the system (may include "hyperthreading cores")</li>
<li><code>int openblas_get_parallel(void)</code> returns 0 for sequential use, 1 for platform-based threading and 2 for OpenMP-based threading</li>
<li><code>char * openblas_get_config()</code> returns the options OpenBLAS was built with, something like <code>NO_LAPACKE DYNAMIC_ARCH NO_AFFINITY Haswell</code></li>
<li><code>int openblas_set_affinity(int thread_index, size_t cpusetsize, cpu_set_t *cpuset)</code> sets the cpu affinity mask of the given thread to the provided cpuset. (Only available under Linux, with semantics identical to pthread_setaffinity_np)</li>
<li><code>int openblas_set_affinity(int thread_index, size_t cpusetsize, cpu_set_t *cpuset)</code> sets the CPU affinity mask of the given thread
to the provided cpuset. Only available on Linux, with semantics identical to <code>pthread_setaffinity_np</code>.</li>
</ul>
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<h1 id="install-openblas">Install OpenBLAS</h1>
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<p class="admonition-title">Note</p>
@ -1541,6 +1596,38 @@ the STM32F4xx.</p>
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@ -1556,12 +1643,42 @@ the STM32F4xx.</p>
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@ -190,6 +230,18 @@
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@ -281,13 +333,13 @@
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Compile the library
Compiling OpenBLAS
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@ -306,21 +358,36 @@
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Install to the directory (optional)
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@ -332,12 +399,36 @@
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Link the library
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@ -380,15 +471,6 @@
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BLAS reference manual
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@ -564,13 +646,13 @@
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@ -589,21 +671,36 @@
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@ -615,12 +712,36 @@
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@ -663,15 +784,6 @@
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BLAS reference manual
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@ -689,60 +801,137 @@
<h1>User manual</h1>
<h2 id="compile-the-library">Compile the library</h2>
<p>This user manual covers compiling OpenBLAS itself, linking your code to OpenBLAS,
example code to use the C (CBLAS) and Fortran (BLAS) APIs, and some troubleshooting
tips. Compiling OpenBLAS is optional, since you may be able to install with a
package manager.</p>
<div class="admonition note blas api reference documentation">
<p class="admonition-title">Note</p>
<p>The OpenBLAS documentation does not contain API reference documentation for
BLAS or LAPACK, since these are standardized APIs, the documentation for
which can be found in other places. If you want to understand every BLAS
and LAPACK function and definition, we recommend reading the
<a href="http://netlib.org/blas/">Netlib BLAS </a> and <a href="http://netlib.org/lapack/">Netlib LAPACK</a>
documentation.</p>
<p>OpenBLAS does contain a limited number of functions that are non-standard,
these are documented at <a href="../extensions/">OpenBLAS extension functions</a>.</p>
</div>
<h2 id="compiling-openblas">Compiling OpenBLAS</h2>
<h3 id="normal-compile">Normal compile</h3>
<ul>
<li>type <code>make</code> to detect the CPU automatically.
or</li>
<li>type <code>make TARGET=xxx</code> to set target CPU, e.g. <code>make TARGET=NEHALEM</code>. The full target list is in file TargetList.txt.</li>
</ul>
<p>The default way to build and install OpenBLAS from source is with Make:
<div class="highlight"><pre><span></span><code>make # add `-j4` to compile in parallel with 4 processes
make install
</code></pre></div></p>
<p>By default, the CPU architecture is detected automatically when invoking
<code>make</code>, and the build is optimized for the detected CPU. To override the
autodetection, use the <code>TARGET</code> flag:</p>
<p><div class="highlight"><pre><span></span><code># `make TARGET=xxx` sets target CPU: e.g. for an Intel Nehalem CPU:
make TARGET=NEHALEM
</code></pre></div>
The full list of known target CPU architectures can be found in
<code>TargetList.txt</code> in the root of the repository.</p>
<h3 id="cross-compile">Cross compile</h3>
<p>Please set <code>CC</code> and <code>FC</code> with the cross toolchains. Then, set <code>HOSTCC</code> with your host C compiler. At last, set <code>TARGET</code> explicitly.</p>
<p>Examples:</p>
<p>For a basic cross-compilation with Make, three steps need to be taken:</p>
<ul>
<li>On x86 box, compile the library for ARM Cortex-A9 linux.</li>
</ul>
<p>Install only gnueabihf versions. Please check https://github.com/xianyi/OpenBLAS/issues/936#issuecomment-237596847</p>
<pre><code>make CC=arm-linux-gnueabihf-gcc FC=arm-linux-gnueabihf-gfortran HOSTCC=gcc TARGET=CORTEXA9
</code></pre>
<ul>
<li>On X86 box, compile this library for loongson3a CPU.</li>
<li>Set the <code>CC</code> and <code>FC</code> environment variables to select the cross toolchains
for C and Fortran.</li>
<li>Set the <code>HOSTCC</code> environment variable to select the host C compiler (i.e. the
regular C compiler for the machine on which you are invoking the build).</li>
<li>Set <code>TARGET</code> explicitly to the CPU architecture on which the produced
OpenBLAS binaries will be used.</li>
</ul>
<h4 id="cross-compilation-examples">Cross-compilation examples</h4>
<p>Compile the library for ARM Cortex-A9 linux on an x86-64 machine
<em>(note: install only <code>gnueabihf</code> versions of the cross toolchain - see
<a href="https://github.com/OpenMathLib/OpenBLAS/issues/936#issuecomment-237596847">this issue comment</a>
for why</em>):
<div class="highlight"><pre><span></span><code>make CC=arm-linux-gnueabihf-gcc FC=arm-linux-gnueabihf-gfortran HOSTCC=gcc TARGET=CORTEXA9
</code></pre></div></p>
<p>Compile OpenBLAS for a loongson3a CPU on an x86-64 machine:
<div class="highlight"><pre><span></span><code>make BINARY=64 CC=mips64el-unknown-linux-gnu-gcc FC=mips64el-unknown-linux-gnu-gfortran HOSTCC=gcc TARGET=LOONGSON3A
</code></pre></div>
<ul>
<li>On X86 box, compile this library for loongson3a CPU with loongcc (based on Open64) compiler.</li>
</ul>
</code></pre></div></p>
<p>Compile OpenBLAS for loongson3a CPU with the <code>loongcc</code> (based on Open64) compiler on an x86-64 machine:
<div class="highlight"><pre><span></span><code>make CC=loongcc FC=loongf95 HOSTCC=gcc TARGET=LOONGSON3A CROSS=1 CROSS_SUFFIX=mips64el-st-linux-gnu- NO_LAPACKE=1 NO_SHARED=1 BINARY=32
</code></pre></div></p>
<h3 id="building-a-debug-version">Building a debug version</h3>
<p>Add <code>DEBUG=1</code> to your build command, e.g.:
<div class="highlight"><pre><span></span><code>make DEBUG=1
</code></pre></div></p>
<h3 id="install-to-a-specific-directory">Install to a specific directory</h3>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Installing to a directory is optional; it is also possible to use the shared or static
libraries directly from the build directory.</p>
</div>
<p>Use <code>make install</code> with the <code>PREFIX</code> flag to install to a specific directory:</p>
<div class="highlight"><pre><span></span><code>make install PREFIX=/path/to/installation/directory
</code></pre></div>
<h3 id="debug-version">Debug version</h3>
<pre><code>make DEBUG=1
</code></pre>
<h3 id="install-to-the-directory-optional">Install to the directory (optional)</h3>
<p>Example:</p>
<pre><code>make install PREFIX=your_installation_directory
</code></pre>
<p>The default directory is /opt/OpenBLAS. Note that any flags passed to <code>make</code> during build should also be passed to <code>make install</code> to circumvent any install errors, i.e. some headers not being copied over correctly.</p>
<p>For more information, please read <a href="../install/">Installation Guide</a>.</p>
<h2 id="link-the-library">Link the library</h2>
<ul>
<li>Link shared library</li>
</ul>
<p>The default directory is <code>/opt/OpenBLAS</code>.</p>
<div class="admonition important">
<p class="admonition-title">Important</p>
<p>Note that any flags passed to <code>make</code> during build should also be passed to
<code>make install</code> to circumvent any install errors, i.e. some headers not
being copied over correctly.</p>
</div>
<p>For more detailed information on building/installing from source, please read
the <a href="../install/">Installation Guide</a>.</p>
<h2 id="linking-to-openblas">Linking to OpenBLAS</h2>
<p>OpenBLAS can be used as a shared or a static library.</p>
<h3 id="link-a-shared-library">Link a shared library</h3>
<p>The shared library is normally called <code>libopenblas.so</code>, but not that the name
may be different as a result of build flags used or naming choices by a distro
packager (see [distributing.md] for details). To link a shared library named
<code>libopenblas.so</code>, the flag <code>-lopenblas</code> is needed. To find the OpenBLAS headers,
a <code>-I/path/to/includedir</code> is needed. And unless the library is installed in a
directory that the linker searches by default, also <code>-L</code> and <code>-Wl,-rpath</code> flags
are needed. For a source file <code>test.c</code> (e.g., the example code under <em>Call
CBLAS interface</em> further down), the shared library can then be linked with:
<div class="highlight"><pre><span></span><code>gcc -o test test.c -I/your_path/OpenBLAS/include/ -L/your_path/OpenBLAS/lib -Wl,-rpath,/your_path/OpenBLAS/lib -lopenblas
</code></pre></div>
<p>The <code>-Wl,-rpath,/your_path/OpenBLAS/lib</code> option to linker can be omitted if you ran <code>ldconfig</code> to update linker cache, put <code>/your_path/OpenBLAS/lib</code> in <code>/etc/ld.so.conf</code> or a file in <code>/etc/ld.so.conf.d</code>, or installed OpenBLAS in a location that is part of the <code>ld.so</code> default search path (usually /lib,/usr/lib and /usr/local/lib). Alternatively, you can set the environment variable LD_LIBRARY_PATH to point to the folder that contains libopenblas.so. Otherwise, linking at runtime will fail with a message like <code>cannot open shared object file: no such file or directory</code></p>
<p>If the library is multithreaded, please add <code>-lpthread</code>. If the library contains LAPACK functions, please add <code>-lgfortran</code> or other Fortran libs, although if you only make calls to LAPACKE routines, i.e. your code has <code>#include "lapacke.h"</code> and makes calls to methods like <code>LAPACKE_dgeqrf</code>, <code>-lgfortran</code> is not needed.</p>
</code></pre></div></p>
<p>The <code>-Wl,-rpath,/your_path/OpenBLAS/lib</code> linker flag can be omitted if you
ran <code>ldconfig</code> to update linker cache, put <code>/your_path/OpenBLAS/lib</code> in
<code>/etc/ld.so.conf</code> or a file in <code>/etc/ld.so.conf.d</code>, or installed OpenBLAS in a
location that is part of the <code>ld.so</code> default search path (usually <code>/lib</code>,
<code>/usr/lib</code> and <code>/usr/local/lib</code>). Alternatively, you can set the environment
variable <code>LD_LIBRARY_PATH</code> to point to the folder that contains <code>libopenblas.so</code>.
Otherwise, the build may succeed but at runtime loading the library will fail
with a message like:
<div class="highlight"><pre><span></span><code>cannot open shared object file: no such file or directory
</code></pre></div></p>
<p>More flags may be needed, depending on how OpenBLAS was built:</p>
<ul>
<li>Link static library</li>
<li>If <code>libopenblas</code> is multi-threaded, please add <code>-lpthread</code>.</li>
<li>If the library contains LAPACK functions (usually also true), please add
<code>-lgfortran</code> (other Fortran libraries may also be needed, e.g. <code>-lquadmath</code>).
Note that if you only make calls to LAPACKE routines, i.e. your code has
<code>#include "lapacke.h"</code> and makes calls to methods like <code>LAPACKE_dgeqrf</code>,
then <code>-lgfortran</code> is not needed.</li>
</ul>
<div class="highlight"><pre><span></span><code>gcc -o test test.c /your/path/libopenblas.a
<div class="admonition tip use pkg-config">
<p class="admonition-title">Tip</p>
<p>Usually a pkg-config file (e.g., <code>openblas.pc</code>) is installed together
with a <code>libopenblas</code> shared library. pkg-config is a tool that will
tell you the exact flags needed for linking. For example:</p>
<div class="highlight"><pre><span></span><code>$ pkg-config --cflags openblas
-I/usr/local/include
$ pkg-config --libs openblas
-L/usr/local/lib -lopenblas
</code></pre></div>
<p>You can download <code>test.c</code> from https://gist.github.com/xianyi/5780018 </p>
</div>
<h3 id="link-a-static-library">Link a static library</h3>
<p>Linking a static library is simpler - add the path to the static OpenBLAS
library to the compile command:
<div class="highlight"><pre><span></span><code>gcc -o test test.c /your/path/libopenblas.a
</code></pre></div></p>
<h2 id="code-examples">Code examples</h2>
<h3 id="call-cblas-interface">Call CBLAS interface</h3>
<p>This example shows calling cblas_dgemm in C. https://gist.github.com/xianyi/6930656
<p>This example shows calling <code>cblas_dgemm</code> in C:</p>
<!-- Source: https://gist.github.com/xianyi/6930656 -->
<div class="highlight"><pre><span></span><code><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cblas.h&gt;</span>
<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;stdio.h&gt;</span>
@ -758,11 +947,14 @@
<span class="w"> </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;%lf &quot;</span><span class="p">,</span><span class="w"> </span><span class="n">C</span><span class="p">[</span><span class="n">i</span><span class="p">]);</span>
<span class="w"> </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">);</span>
<span class="p">}</span>
</code></pre></div></p>
<div class="highlight"><pre><span></span><code>gcc -o test_cblas_open test_cblas_dgemm.c -I /your_path/OpenBLAS/include/ -L/your_path/OpenBLAS/lib -lopenblas -lpthread -lgfortran
</code></pre></div>
<p>To compile this file, save it as <code>test_cblas_dgemm.c</code> and then run:
<div class="highlight"><pre><span></span><code>gcc -o test_cblas_open test_cblas_dgemm.c -I/your_path/OpenBLAS/include/ -L/your_path/OpenBLAS/lib -lopenblas -lpthread -lgfortran
</code></pre></div>
will result in a <code>test_cblas_open</code> executable.</p>
<h3 id="call-blas-fortran-interface">Call BLAS Fortran interface</h3>
<p>This example shows calling dgemm Fortran interface in C. https://gist.github.com/xianyi/5780018</p>
<p>This example shows calling the <code>dgemm</code> Fortran interface in C:</p>
<!-- Source: https://gist.github.com/xianyi/5780018 -->
<div class="highlight"><pre><span></span><code><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;stdio.h&quot;</span>
<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;stdlib.h&quot;</span>
<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;sys/time.h&quot;</span>
@ -828,21 +1020,41 @@
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
<span class="p">}</span>
</code></pre></div>
<p>To compile this file, save it as <code>time_dgemm.c</code> and then run:
<div class="highlight"><pre><span></span><code>gcc -o time_dgemm time_dgemm.c /your/path/libopenblas.a -lpthread
./time_dgemm &lt;m&gt; &lt;n&gt; &lt;k&gt;
</code></pre></div>
You can then run it as: <code>./time_dgemm &lt;m&gt; &lt;n&gt; &lt;k&gt;</code>, with <code>m</code>, <code>n</code>, and <code>k</code> input
parameters to the <code>time_dgemm</code> executable.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When calling the Fortran interface from C, you have to deal with symbol name
differences caused by compiler conventions. That is why the <code>dgemm_</code> function
call in the example above has a trailing underscore. This is what it looks like
when using <code>gcc</code>/<code>gfortran</code>, however such details may change for different
compilers. Hence it requires extra support code. The CBLAS interface may be
more portable when writing C code.</p>
<p>When writing code that needs to be portable and work across different
platforms and compilers, the above code example is not recommended for
usage. Instead, we advise looking at how OpenBLAS (or BLAS in general, since
this problem isn't specific to OpenBLAS) functions are called in widely
used projects like Julia, SciPy, or R.</p>
</div>
<h2 id="troubleshooting">Troubleshooting</h2>
<ul>
<li>Please read <a href="../faq/">Faq</a> at first.</li>
<li>Please use gcc version 4.6 and above to compile Sandy Bridge AVX kernels on Linux/MingW/BSD.</li>
<li>Please use Clang version 3.1 and above to compile the library on Sandy Bridge microarchitecture. The Clang 3.0 will generate the wrong AVX binary code.</li>
<li>The number of CPUs/Cores should less than or equal to 256. On Linux x86_64(amd64), there is experimental support for up to 1024 CPUs/Cores and 128 numa nodes if you build the library with BIGNUMA=1.</li>
<li>OpenBLAS does not set processor affinity by default. On Linux, you can enable processor affinity by commenting the line NO_AFFINITY=1 in Makefile.rule. But this may cause <a href="https://stat.ethz.ch/pipermail/r-sig-hpc/2012-April/001348.html">the conflict with R parallel</a>.</li>
<li>On Loongson 3A. make test would be failed because of pthread_create error. The error code is EAGAIN. However, it will be OK when you run the same testcase on shell.</li>
<li>Please read the <a href="../faq/">FAQ</a> first, your problem may be described there.</li>
<li>Please ensure you are using a recent enough compiler, that supports the
features your CPU provides (example: GCC versions before 4.6 were known to
not support AVX kernels, and before 6.1 AVX512CD kernels).</li>
<li>The number of CPU cores supported by default is &lt;=256. On Linux x86-64, there
is experimental support for up to 1024 cores and 128 NUMA nodes if you build
the library with <code>BIGNUMA=1</code>.</li>
<li>OpenBLAS does not set processor affinity by default. On Linux, you can enable
processor affinity by commenting out the line <code>NO_AFFINITY=1</code> in
<code>Makefile.rule</code>.</li>
<li>On Loongson 3A, <code>make test</code> is known to fail with a <code>pthread_create</code> error
and an <code>EAGAIN</code> error code. However, it will be OK when you run the same
testcase in a shell.</li>
</ul>
<h2 id="blas-reference-manual">BLAS reference manual</h2>
<p>If you want to understand every BLAS function and definition, please read <a href="https://software.intel.com/en-us/intel-mkl/documentation">Intel MKL reference manual</a> or <a href="http://netlib.org/blas/">netlib.org</a></p>
<p>Here are <a href="../extensions/">OpenBLAS extension functions</a></p>
@ -856,6 +1068,38 @@
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