A Computation+Communication Load Balanced Loop Partitioning Method for Distributed Memory Systems

Pande, Santosh; Bali, Tareq

doi:10.1006/jpdc.1999.1567

Cited by 4 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An approach for the partitioning of computations and data for distributed address space has also been presented in [13]. A method for partitioning iterations and data in the context of doall loops has been proposed in [12]. Data distribution strategies in the context of the dHPF compiler have been presented in [2].…”

Section: Related Workmentioning

confidence: 99%

A Data Re-Distribution Library for Multi-Processor Task Programming

Rauber

Rünger

2006

Int. J. Found. Comput. Sci.

View full text Add to dashboard Cite

Multiprocessor task (M-task) programming is a suitable parallel programming model for coding application problems with an inherent modular structure. An M-task can be executed on a group of processors of arbitrary size, concurrently to other M-tasks of the same application program. The data of a multiprocessor task program usually include composed data structures, like vectors or arrays. For distributed memory machines or cluster platforms, those composed data structures are distributed within one or more processor groups. Thus, a concise parallel programming model for M-tasks requires a standardized distributed data format for composed data structures. Additionally, functions for data re-distribution with respect to different data distributions and different processor group layouts are needed to glue program parts together. In this paper, we present a data re-distribution library which extends the M-task programming with Tlib, a library providing operations to split processor groups and to map M-tasks to processor groups.

show abstract

Section: Related Workmentioning

confidence: 99%