CUDA tutorial: Difference between revisions

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.

|title=Prerequisites for this tutorial

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.

* Understanding the architecture of a GPU.

* Understanding the workflow of a CUDA program  

* Managing GPU memory and understanding the various types of GPU memory

* Writing and compiling a minimal CUDA code and compiling CUDA examples

GPU, or a graphics processing unit, is a single-chip processor that performs rapid mathematical calculations, primarily for the purpose of rendering images.

However, in the recent years, such capability is being harnessed more broadly to accelerate computational workloads of the cutting-edge scientific research areas.

'''CUDA''' = '''C'''ompute '''U'''nified '''D'''evice '''A'''rchitecture

Provides access to instructions and memory of massively parallel elements in a GPU.

Another definition: CUDA is a scalable parallel programming model and software environment for parallel computing.