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Everyone has this huge massively parallelized supercomputer on their desktop in the form of a graphics card GPU.
-Adam
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Learning the syntax of programming for GPU is easy. The problem is porting algorithms to utilize the GPU most efficiently. This means taking into account SIMD architecture, warps, different kinds of memory, ...
For example, GPU programming has been used to accelerate video, digital image, and audio signal processing, statistical physics, scientific computing, medical imaging, computer vision, neural networks and deep learning, cryptography, and even intrusion detection, among many other areas.
GPU Programming is a method of running highly parallel general-purpose computations on GPU accelerators. While the past GPUs were designed exclusively for computer graphics, today they are being used extensively for general-purpose computing (GPGPU computing) as well.
As of 2016, OpenCL is the dominant open general-purpose GPU computing language, and is an open standard defined by the Khronos Group. OpenCL provides a cross-platform GPGPU platform that additionally supports data parallel compute on CPUs. OpenCL is actively supported on Intel, AMD, Nvidia, and ARM platforms.
Check out CUDA by NVidia, IMO it's the easiest platform to do GPU programming. There are tons of cool materials to read. http://www.nvidia.com/object/cuda_home.html
Hello world would be to do any kind of calculation using GPU.
Hope that helps.
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