coding beacon

[programming & visualization]

High Performance Computing Libraries (to be updated)

1. IRC channel #opencl at freenode network

2. https://blog.ajguillon.com/

3. Reference on installing pre-requisites (hardware drivers)

http://cran.r-project.org/web/packages/OpenCL/INSTALL

CUDA vs OpenCL

https://www.wikivs.com/wiki/CUDA_vs_OpenCL

http://streamcomputing.eu/blog/2010-01-28/opencl-the-battle-part-i/

http://programmers.stackexchange.com/questions/53410/cuda-vs-opencl-opinions#53699

http://blog.accelereyes.com/blog/2012/02/17/opencl_vs_cuda_webinar_recap/

ArrayFire (open source) http://arrayfire.com/

“ArrayFire supports both CUDA-capable NVIDIA GPUs and most OpenCL devices, including AMD GPUs/APUs and Intel Xeon Phi co-processors. It also supports mobile OpenCL devices from ARM, Qualcomm, and others. We want your code to run as fast as possible, regardless of the hardware.”

“ArrayFire is a blazing fast software library for GPU computing. Its easy-to-use API and array-based function set make GPU programming simple. A few lines of code in ArrayFire can replace dozens of lines of raw GPU code, saving you valuable time and lowering development costs.”

Getting Started

(written prior to being open-source): http://blog.accelereyes.com/blog/2013/03/04/arrayfire-examples-part-1-of-8-getting-started/

Overview by NVidia: http://devblogs.nvidia.com/parallelforall/arrayfire-portable-open-source-accelerated-computing-library/

* Download here

http://arrayfire.com/download/ & install, and append “%AF_PATH%/lib;” to your PATH env.variable

* Sources

https://github.com/arrayfire/arrayfire

* Files required to use ArrayFire from R (prerequisites: source files above)

https://github.com/arrayfire/arrayfire_r

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