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CUDA tutorial: Difference between revisions
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=Introduction= | |||
This tutorial introduces the Graphics Processing Unit (GPU) as a massively parallel computing device, the CUDA parallel programming language, and some of the CUDA numerical libraries for use in high performance computing. | |||
{{Prerequisites | |||
|title=<translate>Prerequisites for this tutorial</translate> | |||
|content= | |||
<translate>This tutorial uses CUDA to accelerate C or C++ code. A working knowledge of one of these languages is therefore required to gain the most benefit out of it. Even though Fortran is also supported by CUDA, for the purpose of this tutorial we only cover the CUDA C/C++. From here on, we use term CUDA C to refer "CUDA C and C++". CUDA C is essentially a C/C++ that allow one to execute function on both GPU and CPU. </translate> | |||
}} | |||
=What is GPU ?= | =What is GPU ?= | ||
GPU, or a graphics processing unit, is a single-chip processor that performs rapid mathematical calculations, primarily for the purpose of rendering images. | GPU, or a graphics processing unit, is a single-chip processor that performs rapid mathematical calculations, primarily for the purpose of rendering images. | ||
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* Execute GPU functions (kernels) | * Execute GPU functions (kernels) | ||
* Transfer data back to the Host memory | * Transfer data back to the Host memory | ||
=CUDA Execution Model= | |||