Abstract
Data parallelism is a model of parallel computing in which the same set of instructions is applied to all the elements in a data set. A sampling of data parallel algorithms is presented. The examples are certainly not exhaustive, but address many issues involved in designing data parallel algorithms. Case studies are used to illustrate some algorithm design techniques; and to highlight some implementation decisions that influence the overall performance of a parallel algorithm. It is shown that the characteristics of a particular parallel machine to be used need to be considered in transforming a given task into a parallel algorithm that executes effectively.
Date of this Version
December 1994
COinS
Comments
"to appear as a chapter in: Handbook of Parallel and Distributed Computing edited by Albert Y. Zomaya, McGraw-Hill, 1995"