Neptune: The application of coarse-grain data flow methods to scientific parallel programming

The dissertation investigates a data flow programming style for developing efficient and machine-independent scientific programs on multiprocessor computers. I have designed and implemented a programming system called Neptune. Both functional and data parallelism are incorporated into Neptune. A coa...

Full description

Bibliographic Details
Other Authors: Traversat, Bernard.
Format: Others
Language:English
Subjects:
Online Access: http://purl.flvc.org/fsu/lib/digcoll/etd/3162207
id ndltd-fsu.edu-oai-fsu.digital.flvc.org-fsu_78405
record_format oai_dc
spelling ndltd-fsu.edu-oai-fsu.digital.flvc.org-fsu_784052019-07-01T04:20:30Z Neptune: The application of coarse-grain data flow methods to scientific parallel programming Traversat, Bernard. Florida State University Text eng 162 p. The dissertation investigates a data flow programming style for developing efficient and machine-independent scientific programs on multiprocessor computers. I have designed and implemented a programming system called Neptune. Both functional and data parallelism are incorporated into Neptune. A coarse-grain data flow model is used to explicitly specify functional parallelism. Data parallelism is represented within the data flow model by an activity decomposition model which ensures efficient execution of data parallel computation. Four scientific applications have been implemented in this data flow style to evaluate execution performance. The machine-independence of the data flow model is demonstrated by obtaining speedup performance on three different parallel architectures (Sun network, Sequent Balance 12000 and a Cray Y-MP/464). The dissertation provides a detailed description of the implementation of the runtime environment on three main classes of parallel architectures (network, shared memory and distributed memory). Each implementation takes advantage of an architecture and provides a suitable data mapping strategy for minimizing overhead and optimizing resource utilization. Developing parallel programs presents a major difficulty. In addition to the sequential problems, the programmer has to design and debug parallel constructs which express concurrency. The dissertation describes the Neptune programming system, which is specially designed for supporting a data flow methodology. Neptune provides an effective visual environment for designing and debugging data flow programs. Applications developed and debugged on a network of workstations can be scaled up and run on multiprocessor computers without requiring any software modifications. On campus use only. Source: Dissertation Abstracts International, Volume: 51-12, Section: B, page: 5982. Major Professor: Gregory A. Riccardi. Thesis (Ph.D.)--The Florida State University, 1990. Computer Science http://purl.flvc.org/fsu/lib/digcoll/etd/3162207 Dissertation Abstracts International AAI9113954 3162207 FSDT3162207 fsu:78405 http://diginole.lib.fsu.edu/islandora/object/fsu%3A78405/datastream/TN/view/Neptune%3A%20The%20application%20of%20coarse-grain%20data%20flow%20methods%20to%20scientific%20parallel%20programming.jpg
collection NDLTD
language English
format Others
sources NDLTD
topic Computer Science
spellingShingle Computer Science
Neptune: The application of coarse-grain data flow methods to scientific parallel programming
description The dissertation investigates a data flow programming style for developing efficient and machine-independent scientific programs on multiprocessor computers. I have designed and implemented a programming system called Neptune. Both functional and data parallelism are incorporated into Neptune. A coarse-grain data flow model is used to explicitly specify functional parallelism. Data parallelism is represented within the data flow model by an activity decomposition model which ensures efficient execution of data parallel computation. Four scientific applications have been implemented in this data flow style to evaluate execution performance. === The machine-independence of the data flow model is demonstrated by obtaining speedup performance on three different parallel architectures (Sun network, Sequent Balance 12000 and a Cray Y-MP/464). The dissertation provides a detailed description of the implementation of the runtime environment on three main classes of parallel architectures (network, shared memory and distributed memory). Each implementation takes advantage of an architecture and provides a suitable data mapping strategy for minimizing overhead and optimizing resource utilization. === Developing parallel programs presents a major difficulty. In addition to the sequential problems, the programmer has to design and debug parallel constructs which express concurrency. The dissertation describes the Neptune programming system, which is specially designed for supporting a data flow methodology. Neptune provides an effective visual environment for designing and debugging data flow programs. Applications developed and debugged on a network of workstations can be scaled up and run on multiprocessor computers without requiring any software modifications. === Source: Dissertation Abstracts International, Volume: 51-12, Section: B, page: 5982. === Major Professor: Gregory A. Riccardi. === Thesis (Ph.D.)--The Florida State University, 1990.
author2 Traversat, Bernard.
author_facet Traversat, Bernard.
title Neptune: The application of coarse-grain data flow methods to scientific parallel programming
title_short Neptune: The application of coarse-grain data flow methods to scientific parallel programming
title_full Neptune: The application of coarse-grain data flow methods to scientific parallel programming
title_fullStr Neptune: The application of coarse-grain data flow methods to scientific parallel programming
title_full_unstemmed Neptune: The application of coarse-grain data flow methods to scientific parallel programming
title_sort neptune: the application of coarse-grain data flow methods to scientific parallel programming
url http://purl.flvc.org/fsu/lib/digcoll/etd/3162207
_version_ 1719216397564772352