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-[Build from source](https://github.com/rindow/rindow-openblas)
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### Acceleration with GPU
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You can use GPU acceleration on OpenCL.
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-[rindow-math-matrix-matlibffi](https://github.com/rindow/rindow-math-matrix-matlibffi): plug-in drivers for OpenBLAS,Rindow-Matlib,OpenCL,CLBlast for FFI
Please see the [rindow-math-matrix-matlibffi](https://github.com/rindow/rindow-math-matrix-matlibffi) to setup plug-in and pre-build binaries.
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This OpenCL support extension works better in your environment and helps speed up your laptop environment without n-NVIDIA.
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Tested on AMD's Bobcat architecture APU.
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In the Windows environment, Integrated GPU usage was more effective than CPU, and it worked comfortably.
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However, OLD AMD APU on Linux, libclc used in linux standard mesa-opencl-icd is very buggy and slow. I made a temporary fix to make it look like it would work, but gave up on careful testing.
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If you have testable hardware, please test using the proprietary driver.
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On the other hand, I tested with Ivy-bridge of Intel CPU and Integrated GPU.
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Windows 10 standard OpenCL driver worked fine, but it was very slow and occasionally crashed.
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And it worked fine and fast in Ubuntu 20.04 + beignet-opencl-icd environment.
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How to Setup
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============
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Set it up using composer.
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```shell
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$ composer require rindow-math-matrix
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```
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You can use it as is, but you will need to speed it up to process at a practical speed.
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And then, Set up pre-build binaries for the required high-speed calculation libraries. Click [here](https://github.com/rindow/rindow-math-matrix-matlibffi) for details.
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