OpenBLAS

Travis CI:

AppVeyor:

Drone CI:

Introduction

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.

Binary Packages

We provide official binary packages for the following platform:

• Windows x86/x86_64

You can download them from file hosting on sourceforge.net.

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 (If you want the most up to date version, be sure to use the develop branch - master is several years out of date due to a change of maintainership.)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.

Dependencies

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)

Normal compile

Simply invoking make (or gmake on BSD) will detect the CPU automatically. To set a specific target CPU, use make TARGET=xxx, e.g. make TARGET=NEHALEM. The full target list is in the file TargetList.txt. For building with cmake, the 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.

Cross compile

Set CC and FC to point to the cross toolchains, and set HOSTCC to your host C compiler. The target must be specified explicitly when cross compiling.

Examples:

• 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
  

Debug version

A debug version can be built using make DEBUG=1.

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:

• On Ubuntu:

  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
  

• On RHEL/CentOS:

  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 USE_MASS=1. For example, to compile on Power8 with MASS support: make USE_MASS=1 TARGET=POWER8.

Install to a specific directory (optional)

Use PREFIX= when invoking make, for example

make install PREFIX=your_installation_directory

The default installation directory is /opt/OpenBLAS.

Supported CPUs and Operating Systems

Please read GotoBLAS_01Readme.txt for older CPU models already supported by the 2010 GotoBLAS.

Additional supported CPUs

x86/x86-64

• 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.
• 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.

MIPS32

• MIPS 1004K: uses P5600 codes
• MIPS 24K: uses P5600 codes

MIPS64

• ICT Loongson 3A: Optimized Level-3 BLAS and the part of Level-1,2.
• ICT Loongson 3B: Experimental

ARM

• 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)

ARM64

• ARMv8: Basic ARMV8 with small caches, optimized Level-3 and Level-2 BLAS
• Cortex-A53: 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
• ThunderX3T110
• 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

PPC/PPC64

• POWER8: Optimized BLAS, only for PPC64LE (Little Endian), only with USE_OPENMP=1
• POWER9: Optimized Level-3 BLAS (real) and some Level-1,2. PPC64LE with OpenMP only.
• POWER10:

IBM zEnterprise System

• Z13: Optimized Level-3 BLAS and Level-1,2
• Z14: Optimized Level-3 BLAS and (single precision) Level-1,2

RISC-V

• C910V: Optimized Leve-3 BLAS (real) and Level-1,2 by RISC-V Vector extension 0.7.1.

  make HOSTCC=gcc TARGET=C910V CC=riscv64-unknown-linux-gnu-gcc FC=riscv64-unknown-linux-gnu-gfortran
  

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.

The 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.

Supported OS

• GNU/Linux
• 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:

Usage

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 OPENBLAS_NUM_THREADS > GOTO_NUM_THREADS > OMP_NUM_THREADS.

If you compile this library with USE_OPENMP=1, you should set the OMP_NUM_THREADS environment variable; OpenBLAS ignores OPENBLAS_NUM_THREADS and GOTO_NUM_THREADS when compiled with USE_OPENMP=1.

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.

Reporting bugs

Please submit an issue in https://github.com/xianyi/OpenBLAS/issues.

Contact

• OpenBLAS users mailing list: https://groups.google.com/forum/#!forum/openblas-users
• OpenBLAS developers mailing list: https://groups.google.com/forum/#!forum/openblas-dev

Change log

Please see Changelog.txt to view the differences between OpenBLAS and GotoBLAS2 1.13 BSD version.

Troubleshooting

• 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 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.
• OpenBLAS does not set processor affinity by default. On Linux, you can enable processor affinity by commenting out the line NO_AFFINITY=1 in Makefile.rule. However, note that this may cause a conflict with R parallel.
• On Loongson 3A, make test may fail with a pthread_create error (EAGAIN). However, it will be okay when you run the same test case on the shell.

Contributing

1. Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug.
2. Fork the OpenBLAS repository to start making your changes.
3. Write a test which shows that the bug was fixed or that the feature works as expected.
4. Send a pull request. Make sure to add yourself to CONTRIBUTORS.md.

Donation

Please read this wiki page.