OpenCL offers a portable language for GPU programming that uses CPU’s, GPU’s, Digital Signal Processors and other types of processors. OpenCL an acronym for the Open Computing Language was launched by Apple and the Khronos group as a way to provide a benchmark for heterogeneous computing that was not restricted to only NVIDIA GPU’s.
#OPENCL BENCHMARK FOR 2016 AMD GPUS SERIAL#
The CUDA programming paradigm is a combination of both serial and parallel executions and contains a special C function called the kernel, which is in simple terms a C code that is executed on a graphics card on a fixed number of threads concurrently (learn more about what is CUDA). The graphics cards which support CUDA are the GeForce 8 series, Tesla and Quadro. CUDA while using a language which is similar to the C language is used to develop software for graphic processors and a vast array of general-purpose applications for GPU’s which are highly parallel in nature.ĬUDA is a proprietary API and as such is only supported on NVIDIA’s GPUs that are based on Tesla Architecture. Why CUDA?ĬUDA which stands for Compute Unified Device Architecture, is a parallel programming paradigm which was released in 2007 by NVIDIA. GPU’s make parallel computing possible by use of hundreds of on-chip processor cores which simultaneously communicate and cooperate to solve complex computing problems.ĬUDA vs OpenCL – two interfaces used in GPU computing and while they both present some similar features, they do so using different programming interfaces. GPGPU’s take advantage of software frameworks such as OpenCL and CUDA to accelerate certain functions in a software with the end goal of making your work quicker and easier. GPGPU programming essentially entails dividing multiple processes or a single process among different processors to accelerate the time needed for completion. GPU programming is now included in virtually every industry, from accelerating video, digital image, audio signal processing, and gaming to manufacturing, neural networks and deep learning. This is done by using a GPU together with a Central Processing Unit (CPU) to accelerate the computations in applications that are traditionally handled by just the CPU only. GPGPU Programming is general purpose computing with the use of a Graphic Processing Unit (GPU). Graphic Processing Units or GPUs have become an essential part of providing processing power for high performance computing applications over the recent years.
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