Context
In July 2014, GPGPU (General-Purpose computing on GPU) was gaining popularity thanks to CUDA and OpenCL. Harnessing the computing power of graphics cards for general tasks opened new perspectives in scientific computing.
What is this article about?
This issue contains two articles on parallel programming with the GPU: the fundamental concepts of GPU computing and their practical application to parallelize workloads.
Summary
Parallelize your workloads by offloading them to your GPU