Question 1

How do I install OpenBLAS on Ubuntu?

Accepted Answer

OpenBLAS can be installed via package managers like apt, but for optimal performance, build from source with 'make' after installing dependencies like gcc and gfortran. Refer to the installation docs for detailed steps.

Question 2

OpenBLAS vs Intel MKL: which is better?

Accepted Answer

OpenBLAS is free and open-source with broad architecture support, while Intel MKL is proprietary and optimized for Intel CPUs. OpenBLAS often matches or exceeds MKL on non-Intel hardware, but MKL may have better integration with Intel tools.

Question 3

How to set the number of threads in OpenBLAS?

Accepted Answer

Use environment variables like OPENBLAS_NUM_THREADS or runtime functions like openblas_set_num_threads(). If compiled with USE_OPENMP=1, set OMP_NUM_THREADS instead, as noted in the usage section.

Question 4

Does OpenBLAS work on ARM processors like Raspberry Pi?

Accepted Answer

Yes, OpenBLAS supports ARMv6, ARMv7, and ARM64 with optimized kernels for CPUs like Cortex-A53. Build with TARGET specified for your ARM model, and use DYNAMIC_ARCH for runtime detection.

Question 5

How to cross-compile OpenBLAS for Windows on Linux?

Accepted Answer

Set CC and FC to cross toolchains (e.g., mingw32-gcc), specify TARGET like HASWELL, and use CROSS=1. Example commands are provided in the cross-compile section of the README.

Question 6

Is OpenBLAS compatible with NumPy?

Accepted Answer

Yes, NumPy can link against OpenBLAS for accelerated linear algebra. Install OpenBLAS and configure NumPy to use it via site.cfg or build options, often resulting in significant performance improvements.

OpenBLAS

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