Parallel language constructs for tensor product computations on loosely coupled architectures

Authors:
P. Mehrotra;J. van Rosendale
Affiliations:
Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, Hampton, VA;Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, Hampton, VA
Venue:
Proceedings of the 1989 ACM/IEEE conference on Supercomputing
Year:
1989

Citing 4
Cited 3

Semi-automatic process partitioning for parallel computation

International Journal of Parallel Programming
Compiling programs for nonshared memory machines

Compiling programs for nonshared memory machines
Communicating sequential processes

Communications of the ACM
An Overview of Dino - A New Language for Numerical Computation on Distributed Memory Multiprocessors

Proceedings of the Third SIAM Conference on Parallel Processing for Scientific Computing

Supporting shared data structures on distributed memory architectures

PPOPP '90 Proceedings of the second ACM SIGPLAN symposium on Principles & practice of parallel programming
Programming data parallel algorithms on distributed memory using Kali

ICS '91 Proceedings of the 5th international conference on Supercomputing
Parallelization of FORTRAN code on distributed-memory parallel processors

ICS '90 Proceedings of the 4th international conference on Supercomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Distributed memory architectures offer high levels of performance and flexibility, but have proven awkward to program. Current languages for nonshared memory architectures provide a relatively low-level programming environment, and are poorly suited to modular programming, and to the construction of libraries. This paper describes a set of language primitives designed to allow the specification of parallel numerical algorithms at a higher level. We focus here on tensor product array computations, a simple but important class of numerical algorithms. We consider first the problem of programming one dimensional “kernel” routines, such as parallel tridiagonal solvers, and after that look at how such parallel kernels can be combined to form parallel tensor product algorithms.