The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Mapping of portable parallel programs
Author:	Chen, Song
View Online:	njit-etd1995-046 (xiv, 114 pages ~ 4.5 MB pdf)
Department:	Department of Computer and Information Science
Degree:	Doctor of Philosophy
Program:	Computer Science
Document Type:	Dissertation
Advisory Committee:	Eshaghian, Mary M. (Committee chair) Carpinelli, John D. (Committee member) McHugh, James A. (Committee member) Ng, Peter A. (Committee member) Ziavras, Sotirios (Committee member)
Date:	1995-05
Keywords:	Parallel processing (Electronic computers) Parallel programming (Computer science)
Availability:	Unrestricted
Abstract:	An efficient parallel program designed for a parallel architecture includes a detailed outline of accurate assignments of concurrent computations onto processors, and data transfers onto communication links, such that the overall execution time is minimized. This process may be complex depending on the application task and the target multiprocessor architecture. Furthermore, this process is to be repeated for every different architecture even though the application task may be the same. Consequently, this has a major impact on the ever increasing cost of software development for multiprocessor systems. A remedy for this problem would be to design portable parallel programs which can be mapped efficiently onto any computer system. In this dissertation, we present a portable programming tool called Cluster-M. The three components of Cluster-M are the Specification Module, the Representation Module, and the Mapping Module. In the Specification Module, for a given problem, a machine-independent program is generated and represented in the form of a clustered task graph called Spec graph. Similarly, in the Representation Module, for a given architecture or heterogeneous suite of computers, a clustered system graph called Rep graph is generated. The Mapping Module is responsible for efficient mapping of Spec graphs onto Rep graphs. As part of this module, we present the first algorithm which produces a near-optimal mapping of an arbitrary non-uniform machine-independent task graph with M modules, onto an arbitrary non-uniform task-independent system graph having N processors, in 0(M P) time, where P = max(M, N). Our experimental results indicate that Cluster-M produces better or similar mapping results compared to other leading techniques which work only for restricted task or system graphs.