Simultaneous exploitation of task and data parallelism in regular scientific applications
Ramaswamy, Shankar
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https://hdl.handle.net/2142/19839
Description
Title
Simultaneous exploitation of task and data parallelism in regular scientific applications
Author(s)
Ramaswamy, Shankar
Issue Date
1996
Doctoral Committee Chair(s)
Banerjee, Prithviraj
Department of Study
Electrical and Computer Engineering
Discipline
Electrical and Computer Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Computer Science
Language
eng
Abstract
Distributed Memory Multicomputers (DMMs) such as the IBM SP-2, the Intel Paragon and the Thinking Machines CM-5 offer significant advantages over shared memory multiprocessors in terms of cost and scalability. Unfortunately, the utilization of all the available computational power in these machines involves a tremendous programming effort on the part of users, which creates a need for sophisticated compiler and run-time support for distributed memory machines. In this thesis we explore a new compiler optimization for regular scientific applications--the simultaneous exploitation of task and data parallelism. Our optimization is implemented as part of the PARADIGM HPF compiler framework and as part of a MATLAB compiler framework we have developed. The intuitive idea behind the optimization is the use of task parallelism to control the degree of data parallelism of individual tasks. The reason this provides increased performance is that data parallelism provides diminishing returns as the number of processors used is increased. By controlling the number of processors used for each data parallel task in an application and by concurrently executing these tasks, we make program execution more efficient and therefore faster. A practical implementation of a task and data parallel scheme of execution for an application on a distributed memory multicomputer also involves data redistribution. This data redistribution causes an overhead. However, as our experimental results show, this overhead is not a problem; execution of a program using task and data parallelism together can be significantly faster that its execution using data parallelism alone. This makes our proposed optimization practical and extremely useful.
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