CUDA: Scalable parallel programming for high-performance scientific computing

Author

Luebke, David

Author_Institution

NVIDIA Corp., Santa Clara, CA

fYear

2008

fDate

14-17 May 2008

Firstpage

836

Lastpage

838

Abstract

Graphics processing units (GPUs) originally designed for computer video cards have emerged as the most powerful chip in a high-performance workstation. Unlike multicore CPU architectures, which currently ship with two or four cores, GPU architectures are "manycore" with hundreds of cores capable of running thousands of threads in parallel. NVIDIA\´s CUDA is a co-evolved hardware-software architecture that enables high-performance computing developers to harness the tremendous computational power and memory bandwidth of the GPU in a familiar programming environment - the C programming language. We describe the CUDA programming model and motivate its use in the biomedical imaging community.

Keywords

C language; biomedical imaging; computer graphic equipment; computer graphics; medical computing; parallel programming; C programming language; CUDA; GPU computing; biomedical imaging; co-evolved hardware-software architecture; computational power; graphics processing units; high-performance scientific computing; memory bandwidth; scalable parallel programming; Biomedical computing; Central Processing Unit; Computer architecture; Computer graphics; Marine vehicles; Multicore processing; Parallel programming; Scientific computing; Workstations; Yarn;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on

Conference_Location

Paris

Print_ISBN

978-1-4244-2002-5

Electronic_ISBN

978-1-4244-2003-2

Type

conf

DOI

10.1109/ISBI.2008.4541126

Filename

4541126