It’s a GPGPU/GPU Acceleration real-world face-off we’ve got on our hands here! If you’re looking for more information on CUDA and OpenCL, this is the article for you. We’ll give you a brief overview of what GPGPU is and look at how AMD, Nvidia, OpenCL & CUDA fit into the mix. Finally, we will explain which applications work best with which brand of graphics cards, providing a list that gives a brief overview of CUDA/OpenCL support in a wide variety of professional apps.
Introduction to GPGPU (General Purpose Computing on Graphics Processing Units)
If you’ve never heard of GPGPU or GPU acceleration, don’t worry, most people haven’t, but custom Apple computer experts like ourselves do, and we can explain! OpenCL and CUDA, however, are terms that are starting to become more and more prevalent in the professional computing sector. OpenCL and CUDA are software frameworks that allow GPGPU to accelerate processing in applications where they are respectively supported.
So what exactly is GPGPU, or general purpose computing on graphics processing units?
GPGPU is the utilisation of a GPU (graphics processing unit), which would typically only handle computer graphics, to assist in performing tasks that are traditionally handled solely by the CPU (central processing unit).
In traditional computing, data can be passed from the CPU to the GPU, the GPU then renders the data, but the GPU cannot pass information back. GPGPU allows information to be transferred in both directions, from CPU to GPU and GPU to CPU. Such bidirectional processing can hugely improve efficiency in a wide variety of tasks related to images and video. If the application you use supports OpenCL or CUDA, you will normally see huge performance boosts when using hardware that supports the relevant GPGPU framework.
So now you know what GPGPU is, how do OpenCL and CUDA fit into the equation? OpenCL is currently the leading open source GPGPU framework. CUDA, on the other hand, is the leading proprietary GPGPU framework.
