OpenBLAS alternatives and similar libraries
Based on the "Math" category.
Alternatively, view OpenBLAS alternatives based on common mentions on social networks and blogs.
5.1 0.0 L1 OpenBLAS VS MIRACLMIRACL Cryptographic SDK: Multiprecision Integer and Rational Arithmetic Cryptographic Library is a C software library that is widely regarded by developers as the gold standard open source SDK for elliptic curve cryptography (ECC).
4.9 5.1 L2 OpenBLAS VS LibTomMathLibTomMath is a free open source portable number theoretic multiple-precision integer library written entirely in C.
3.7 0.0 L1 OpenBLAS VS GMTLGeneric Math Template Library (forked from http://ggt.sourceforge.net/)
3.2 2.3 OpenBLAS VS safe_numericsReplacements to standard numeric types which throw exceptions on errors
1.5 0.0 OpenBLAS VS XerusA general purpose library for numerical calculations with higher order tensors, Tensor-Train Decompositions / Matrix Product States and other Tensor Networks
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of OpenBLAS or a related project?
OpenBLAS is an optimized BLAS (Basic Linear Algebra Subprograms) library based on GotoBLAS2 1.13 BSD version.
Please read the documentation on the OpenBLAS wiki pages: https://github.com/xianyi/OpenBLAS/wiki.
For a general introduction to the BLAS routines, please refer to the extensive documentation of their reference implementation hosted at netlib: https://www.netlib.org/blas. On that site you will likewise find documentation for the reference implementation of the higher-level library LAPACK - the Linear Algebra Package that comes included with OpenBLAS. If you are looking for a general primer or refresher on Linear Algebra, the set of six 20-minute lecture videos by Prof. Gilbert Strang on either MIT OpenCourseWare https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/ or Youtube https://www.youtube.com/playlist?list=PLUl4u3cNGP61iQEFiWLE21EJCxwmWvvek may be helpful.
We provide official binary packages for the following platform:
- Windows x86/x86_64
Installation from Source
Download from project homepage, https://xianyi.github.com/OpenBLAS/, or check out the code using Git from https://github.com/xianyi/OpenBLAS.git. (If you want the most up to date version, be sure to use the develop branch - master is several years out of date due to a change of maintainership.) Buildtime parameters can be chosen in Makefile.rule, see there for a short description of each option. Most can also be given directly on the make or cmake command line.
Building OpenBLAS requires the following to be installed:
- GNU Make
- A C compiler, e.g. GCC or Clang
- A Fortran compiler (optional, for LAPACK)
- IBM MASS (optional, see below)
gmake on BSD) will detect the CPU automatically.
To set a specific target CPU, use
make TARGET=xxx, e.g.
The full target list is in the file
TargetList.txt. For building with
usual conventions apply, i.e. create a build directory either underneath the toplevel
OpenBLAS source directory or separate from it, and invoke
cmake there with the path
to the source tree and any build options you plan to set.
FC to point to the cross toolchains, and set
HOSTCC to your host C compiler.
The target must be specified explicitly when cross compiling.
On an x86 box, compile this library for a loongson3a CPU:
make BINARY=64 CC=mips64el-unknown-linux-gnu-gcc FC=mips64el-unknown-linux-gnu-gfortran HOSTCC=gcc TARGET=LOONGSON3A
or same with the newer mips-crosscompiler put out by Loongson that defaults to the 32bit ABI:
make HOSTCC=gcc CC='/opt/mips-loongson-gcc7.3-linux-gnu/2019.06-29/bin/mips-linux-gnu-gcc -mabi=64' FC='/opt/mips-loongson-gcc7.3-linux-gnu/2019.06-29/bin/mips-linux-gnu-gfortran -mabi=64' TARGET=LOONGSON3A
On an x86 box, compile this library for a loongson3a CPU with loongcc (based on Open64) compiler:
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
A debug version can be built using
Compile with MASS support on Power CPU (optional)
The IBM MASS library consists of a set of mathematical functions for C, C++, and Fortran applications that are tuned for optimum performance on POWER architectures. OpenBLAS with MASS requires a 64-bit, little-endian OS on POWER. The library can be installed as shown:
wget -q http://public.dhe.ibm.com/software/server/POWER/Linux/xl-compiler/eval/ppc64le/ubuntu/public.gpg -O- | sudo apt-key add - echo "deb http://public.dhe.ibm.com/software/server/POWER/Linux/xl-compiler/eval/ppc64le/ubuntu/ trusty main" | sudo tee /etc/apt/sources.list.d/ibm-xl-compiler-eval.list sudo apt-get update sudo apt-get install libxlmass-devel.8.1.5
wget http://public.dhe.ibm.com/software/server/POWER/Linux/xl-compiler/eval/ppc64le/rhel7/repodata/repomd.xml.key sudo rpm --import repomd.xml.key wget http://public.dhe.ibm.com/software/server/POWER/Linux/xl-compiler/eval/ppc64le/rhel7/ibm-xl-compiler-eval.repo sudo cp ibm-xl-compiler-eval.repo /etc/yum.repos.d/ sudo yum install libxlmass-devel.8.1.5
After installing the MASS library, compile OpenBLAS with
For example, to compile on Power8 with MASS support:
make USE_MASS=1 TARGET=POWER8.
Install to a specific directory (optional)
PREFIX= when invoking
make, for example
make install PREFIX=your_installation_directory
The default installation directory is
Supported CPUs and Operating Systems
GotoBLAS_01Readme.txt for older CPU models already supported by the 2010 GotoBLAS.
Additional supported CPUs
- Intel Xeon 56xx (Westmere): Used GotoBLAS2 Nehalem codes.
- Intel Sandy Bridge: Optimized Level-3 and Level-2 BLAS with AVX on x86-64.
- Intel Haswell: Optimized Level-3 and Level-2 BLAS with AVX2 and FMA on x86-64.
- Intel Skylake-X: Optimized Level-3 and Level-2 BLAS with AVX512 and FMA on x86-64.
- Intel Cooper Lake: as Skylake-X with improved BFLOAT16 support.
- AMD Bobcat: Used GotoBLAS2 Barcelona codes.
- AMD Bulldozer: x86-64 ?GEMM FMA4 kernels. (Thanks to Werner Saar)
- AMD PILEDRIVER: Uses Bulldozer codes with some optimizations.
- AMD STEAMROLLER: Uses Bulldozer codes with some optimizations.
- AMD ZEN: Uses Haswell codes with some optimizations.
- MIPS 1004K: uses P5600 codes
- MIPS 24K: uses P5600 codes
- ICT Loongson 3A: Optimized Level-3 BLAS and the part of Level-1,2.
- ICT Loongson 3B: Experimental
- ARMv6: Optimized BLAS for vfpv2 and vfpv3-d16 (e.g. BCM2835, Cortex M0+)
- ARMv7: Optimized BLAS for vfpv3-d32 (e.g. Cortex A8, A9 and A15)
- ARMv8: Basic ARMV8 with small caches, optimized Level-3 and Level-2 BLAS
- Cortex-A53: same as ARMV8 (different cpu specifications)
- Cortex-A55: same as ARMV8 (different cpu specifications)
- Cortex A57: Optimized Level-3 and Level-2 functions
- Cortex A72: same as A57 ( different cpu specifications)
- Cortex A73: same as A57 (different cpu specifications)
- Falkor: same as A57 (different cpu specifications)
- ThunderX: Optimized some Level-1 functions
- ThunderX2T99: Optimized Level-3 BLAS and parts of Levels 1 and 2
- TSV110: Optimized some Level-3 helper functions
- EMAG 8180: preliminary support based on A57
- Neoverse N1: (AWS Graviton2) preliminary support
- Apple Vortex: preliminary support based on ARMV8
- POWER8: Optimized BLAS, only for PPC64LE (Little Endian), only with
- POWER9: Optimized Level-3 BLAS (real) and some Level-1,2. PPC64LE with OpenMP only.
IBM zEnterprise System
- Z13: Optimized Level-3 BLAS and Level-1,2
- Z14: Optimized Level-3 BLAS and (single precision) Level-1,2
- C910V: Optimized Level-3 BLAS (real) and Level-1,2 by RISC-V Vector extension 0.7.1.
sh make HOSTCC=gcc TARGET=C910V CC=riscv64-unknown-linux-gnu-gcc FC=riscv64-unknown-linux-gnu-gfortran(also known to work on C906)
Support for multiple targets in a single library
OpenBLAS can be built for multiple targets with runtime detection of the target cpu by specifiying
DYNAMIC_ARCH=1 in Makefile.rule, on the gmake command line or as
-DDYNAMIC_ARCH=TRUE in cmake.
For x86_64, the list of targets this activates contains Prescott, Core2, Nehalem, Barcelona, Sandybridge, Bulldozer, Piledriver, Steamroller, Excavator, Haswell, Zen, SkylakeX. For cpu generations not included in this list, the corresponding older model is used. If you also specify
DYNAMIC_OLDER=1, specific support for Penryn, Dunnington, Opteron, Opteron/SSE3, Bobcat, Atom and Nano is added. Finally there is an option
DYNAMIC_LIST that allows to specify an individual list of targets to include instead of the default.
DYNAMIC_ARCH is also supported on x86, where it translates to Katmai, Coppermine, Northwood, Prescott, Banias,
Core2, Penryn, Dunnington, Nehalem, Athlon, Opteron, Opteron_SSE3, Barcelona, Bobcat, Atom and Nano.
On ARMV8, it enables support for CortexA53, CortexA57, CortexA72, CortexA73, Falkor, ThunderX, ThunderX2T99, TSV110 as well as generic ARMV8 cpus.
For POWER, the list encompasses POWER6, POWER8 and POWER9, on ZARCH it comprises Z13 and Z14.
TARGET option can be used in conjunction with
DYNAMIC_ARCH=1 to specify which cpu model should be assumed for all the
common code in the library, usually you will want to set this to the oldest model you expect to encounter.
Please note that it is not possible to combine support for different architectures, so no combined 32 and 64 bit or x86_64 and arm64 in the same library.
- MinGW or Visual Studio (CMake)/Windows: Please read https://github.com/xianyi/OpenBLAS/wiki/How-to-use-OpenBLAS-in-Microsoft-Visual-Studio.
- Darwin/macOS/OSX/iOS: Experimental. Although GotoBLAS2 already supports Darwin, we are not OSX/iOS experts.
- FreeBSD: Supported by the community. We don't actively test the library on this OS.
- OpenBSD: Supported by the community. We don't actively test the library on this OS.
- NetBSD: Supported by the community. We don't actively test the library on this OS.
- DragonFly BSD: Supported by the community. We don't actively test the library on this OS.
- Android: Supported by the community. Please read https://github.com/xianyi/OpenBLAS/wiki/How-to-build-OpenBLAS-for-Android.
- AIX: Supported on PPC up to POWER8
- Haiku: Supported by the community. We don't actively test the library on this OS.
- SunOS: Supported by the community. We don't actively test the library on this OS.
- Cortex-M: Supported by the community. Please read https://github.com/xianyi/OpenBLAS/wiki/How-to-use-OpenBLAS-on-Cortex-M.
Statically link with
libopenblas.a or dynamically link with
-lopenblas if OpenBLAS was
compiled as a shared library.
Setting the number of threads using environment variables
Environment variables are used to specify a maximum number of threads. For example,
export OPENBLAS_NUM_THREADS=4 export GOTO_NUM_THREADS=4 export OMP_NUM_THREADS=4
The priorities are
If you compile this library with
USE_OPENMP=1, you should set the
environment variable; OpenBLAS ignores
Setting the number of threads at runtime
We provide the following functions to control the number of threads at runtime:
void goto_set_num_threads(int num_threads); void openblas_set_num_threads(int num_threads);
Note that these are only used once at library initialization, and are not available for
fine-tuning thread numbers in individual BLAS calls.
If you compile this library with
USE_OPENMP=1, you should use the above functions too.
Please submit an issue in https://github.com/xianyi/OpenBLAS/issues.
- OpenBLAS users mailing list: https://groups.google.com/forum/#!forum/openblas-users
- OpenBLAS developers mailing list: https://groups.google.com/forum/#!forum/openblas-dev
Please see Changelog.txt to view the differences between OpenBLAS and GotoBLAS2 1.13 BSD version.
- Please read the FAQ first.
- Please use GCC version 4.6 and above to compile Sandy Bridge AVX kernels on Linux/MinGW/BSD.
- Please use Clang version 3.1 and above to compile the library on Sandy Bridge microarchitecture. Clang 3.0 will generate the wrong AVX binary code.
- Please use GCC version 6 or LLVM version 6 and above to compile Skylake AVX512 kernels.
- The number of CPUs/cores should be less than or equal to 256. On Linux
amd64), there is experimental support for up to 1024 CPUs/cores and 128 numa nodes if you build the library with
- OpenBLAS does not set processor affinity by default.
On Linux, you can enable processor affinity by commenting out the line
NO_AFFINITY=1in Makefile.rule. However, note that this may cause a conflict with R parallel.
- On Loongson 3A,
make testmay fail with a
EAGAIN). However, it will be okay when you run the same test case on the shell.
- Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug.
- Fork the OpenBLAS repository to start making your changes.
- Write a test which shows that the bug was fixed or that the feature works as expected.
- Send a pull request. Make sure to add yourself to
Please read this wiki page.