Data-parallel programming on a network of heterogeneous workstations

2 January 2003

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

p. 28-36
https://doi.org/10.1109/hpdc.1992.246489

Abstract

The authors describe a compiler and run-time system that allows data-parallel programs to execute on a network of heterogeneous UNIX workstations. The programming language supported is Dataparallel C, a SIMD language with virtual processors and a global name space. This parallel programming environment allows the user to take advantage of the power of multiple workstations without adding any message-passing calls to the source program. Because the performance of individual workstations in a multi-user environment may change during the execution of a Dataparallel C program, the run-time system automatically performs dynamic load balancing. The authors present experimental results that demonstrate the usefulness of dynamic load balancing in a multi-user environment.

Keywords

This publication has 10 references indexed in Scilit:

Implementation and performance of Munin
Published by Association for Computing Machinery (ACM) ,1991
Data-parallel programming on MIMD computers
IEEE Transactions on Parallel and Distributed Systems, 1991
Data-Parallel Programming on MIMD Computers
Published by MIT Press ,1991
The Amber system: parallel programming on a network of multiprocessors
Published by Association for Computing Machinery (ACM) ,1989
A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering, 1988
What have we learnt from using real parallel machines to solve real problems?
Published by Association for Computing Machinery (ACM) ,1988
Distributed operating systems
ACM Computing Surveys, 1985
The distributed V kernel and its performance for diskless workstations
Published by Association for Computing Machinery (ACM) ,1983
The architecture of the Eden system
Published by Association for Computing Machinery (ACM) ,1981
Accent
Published by Association for Computing Machinery (ACM) ,1981