Proceedings of the 1991 ACM/IEEE Conference on Supercomputing - Supercomputing '91 1991
DOI: 10.1145/125826.126059
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Graphical development tools for network-based concurrent supercomputing

Abstract: This paper describes an X-window based sofiware environment called HeNCE (Heterogeneous Network Computing Environment) designed to assist scientists in developing parallel programs that run on a network of computers. HeNCE is built on top of a software package called P VM which supports process management and communication between a network of heterogeneous computers. HeNCE is based on a parallel programming paradigm where an application program can be described by a graph. Nodes of the graph represent subrout… Show more

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Cited by 89 publications
(35 citation statements)
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“…Further, incremental checkpoints would consist of a single addition or deletion to the bag. HeNCE [BDG+91] admits similar drastic optimizations. We are attempting to develop a toolkit that will hopefully be somewhat general and yet allow programmers of environments to quickly add efficient fail-safety features to their tools (e.g, to HeNCE, distributed shared objects.…”
Section: Future Workmentioning
confidence: 90%
“…Further, incremental checkpoints would consist of a single addition or deletion to the bag. HeNCE [BDG+91] admits similar drastic optimizations. We are attempting to develop a toolkit that will hopefully be somewhat general and yet allow programmers of environments to quickly add efficient fail-safety features to their tools (e.g, to HeNCE, distributed shared objects.…”
Section: Future Workmentioning
confidence: 90%
“…Phred is similar to several other visual parallel programming environments, namely Code [9,20], HeNCE [4,5], Paralex [1. 2], and Schedule [12].…”
Section: Related Systemsmentioning
confidence: 99%
“…(6) Search for methods, based on CPU and network load measures, to automatically locate the limiting machine in a PVM configuration, so that it may be removed during future application runs. (7) Determine ways in which the information learned in (3), (4), (5), and (6) may be used durmg the implementation of a scheduler for compute-intensive programs on workstation networks; specifically, examine the role of run time prediction and load monitoring in common scheduling algorithms.…”
Section: Problem Statementmentioning
confidence: 99%