From Sparse Matrix to Optimal GPU CUDA Sparse Matrix Vector Product Implementation

El Zein, Ahmed; Rendell, Alistair

Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

From Sparse Matrix to Optimal GPU CUDA Sparse Matrix Vector Product Implementation

Date

2010

Authors

El Zein, Ahmed

Rendell, Alistair

Publisher

IEEE Computer Society

Abstract

The CUDA model for GPUs presents the programmer with a plethora of different programming options. These includes different memory types, different memory access methods, and different data types. Identifying which options to use and when is a non-trivial

URI

http://hdl.handle.net/1885/55420

Collections

Open Access Research

Source

Proceedings 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGRID 2010)

Type

Conference paper

DOI

10.1109/CCGRID.2010.81

Restricted until

2037-12-31

Downloads

File

Description

01_El Zein_From_Sparse_Matrix_to_Optimal_2010.pdf (202.14 KB)

Full item page

Cultural advice

From Sparse Matrix to Optimal GPU CUDA Sparse Matrix Vector Product Implementation

Date

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

Keywords

Citation

URI

Collections

Source

Type

Book Title

Entity type

Access Statement

License Rights

DOI

Restricted until

Downloads