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The POEMS project is creating an environment for end-to-end performance modeling of complex parallel and distributed systems, spanning the domains of application software, runtime and operating system software, and hardware architecture. To enable end-to-end modeling of large-scale applications and systems, the POEMS framework is designed to compose models(More)
T his issue's theme is the rapidly evolving enabling technology of problem-solving environments, defined as " a computer system that provides all the computational facilities necessary to solve a target class of problems " for scientific computing. 1 The 1991 1 and 1995 2 workshops on PSEs for physical simulation helped to define this research area and(More)
An important class of methodologies for the parallel processing of computational models defined on some discrete geometric data structures (i.e., meshes, grids) is the so called geometry decomposition or splitting approach. Compared to the sequential processing of such models, the geometry splitting parallel methodology requires an additional computational(More)
The increase in the use of mobile & embedded devices, coupled with ad-hoc, short range wireless networking is enabling pervasive computing. This pervasive computing environment and the wired Grid infrastructure can be combined to make the Computation and Information Grid truly pervasive. This paper identifies some of the interesting research issues and(More)
During the early 1960s, scientists began to envision problem-solving computing environments not only powerful enough to solve complex problems but also able to interact with users on human terms. While many tried to create PSEs over the next few years, by the early 1970s they had abandoned almost all of these attempts. Technology could not yet support PSEs(More)
Problem-solving e<?Pub Caret>nvironments (PSEs) interact with theuser in a language &#8220;natural&#8221; to the associated discipline,and they provide a high-level abstraction of the underlying,computationally complex model. The knowledge-based system PYTHIAaddresses the problem of (parameter, algorithm) pair selection within ascientific computing domain(More)
In this paper, we propose two new neuro-fuzzy schemes, one for classification and one for clustering problems. The classification scheme is based on Simpson's fuzzy min-max method (1992, 1993) and relaxes some assumptions he makes. This enables our scheme to handle mutually nonexclusive classes. The neuro-fuzzy clustering scheme is a multiresolution(More)
Neurofuzzy approaches for predicting financial time series are investigated and shown to perform well in the context of various trading strategies involving stocks and options. The horizon of prediction is typically a few days and trading strategies are examined using historical data. Two methodologies are presented wherein neural predictors are used to(More)