CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on proprietary GPUs (graphics processing units). CUDA allows developers to accelerate compute-intensive applications by leveraging the power of GPUs for the parallelizable portion of the computation.

Although there are other proposed GPU APIs such as OpenCLand there are competing GPUs from other companies such as AMDthe combination of CUDA and NVIDIA GPUs dominates several application areas, including deep learning, and is the basis for some of the world’s fastest computers.

Graphics cards are probably as old as the computer—that is, if you consider the 1981 IBM Monochrome Display Adapter to be a graphics card. By 1988, you could get a 16-bit 2D VGA Wonder card from ATI (the company was eventually acquired by AMD). By 1996, you could buy a 3D graphics accelerator from 3dfx to be able to run the first-person shooter Quake at full speed.

Also in 1996, NVIDIA began trying to compete in the 3D accelerator market with weak products, but learned in the process and in 1999 introduced the successful GeForce 256, the first graphics card called a GPU. At the time, the main reason for having a GPU was for gaming. It wasn’t until later that people used GPUs for math, science, and engineering.

The origins of CUDA

In 2003, a team of researchers led by Ian Buck unveiled Brook, the first widely adopted programming model for extending C with data-parallel constructs. Buck later joined NVIDIA and led the launch of CUDA in 2006, the first commercial GPU-based general-purpose computing solution.

OpenCL vs. CUDA

CUDA competitor OpenCL started in 2009 in an effort to provide a standard for heterogeneous computing that is not limited to Intel/AMD processors with NVIDIA GPUs. Although OpenCL sounds attractive because of its mainstream, it doesn’t perform as well as CUDA on NVIDIA GPUs, and many deep learning frameworks either don’t support OpenCL or only support it as an afterthought after their CUDA support is released.

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