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PELLPACK: a problemsolving environment for PDEbased applications on multicomputer platforms
 ACM Transactions on Mathematical Software
, 1998
"... This paper presents the software architecture and implementation of the problem solving ..."
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Cited by 24 (4 self)
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This paper presents the software architecture and implementation of the problem solving
On the Future of Problem Solving Environments

, 2000
"... In this paper we review the current state of the problem solving environment (PSE) field and make projections for the future. First we describe the computing context, the definition of a PSE and the goals of a PSE. The stateoftheart is summarized along with sources (books, bibliographics, web sit ..."
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Cited by 19 (2 self)
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In this paper we review the current state of the problem solving environment (PSE) field and make projections for the future. First we describe the computing context, the definition of a PSE and the goals of a PSE. The stateoftheart is summarized along with sources (books, bibliographics, web sites) of more detailed information. The principal components and paradigms for building PSEs are presented. The discussion of the future is given in three parts: future trends, scenarios for 2010/2025, and research
Performance evaluation of MPI implementations and MPI based parallel ELLPACK solvers
 In 2 nd MPI Developers Coneference
, 1996
"... In this study, we are concerned with the parallelizationof finite element mesh generation and its decomposition, and the parallel solution of sparse algebraic equations which are obtained from the parallel discretization of second order elliptic partial differential equations (PDEs) using finite dif ..."
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Cited by 3 (0 self)
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In this study, we are concerned with the parallelizationof finite element mesh generation and its decomposition, and the parallel solution of sparse algebraic equations which are obtained from the parallel discretization of second order elliptic partial differential equations (PDEs) using finite difference and finite element techniques. For this we use the Parallel ELLPACK (//ELLPACK) problem solving environment (PSE) which supports PDE computations on several MIMD platforms. We have considered the ITPACK library of stationary iterative solvers which we have parallelized and integrated into the //ELLPACK PSE. This Parallel ITPACK package has been implemented using the MPI, PVM, PICL, PARMACS, nCUBE Vertex and Intel NX message passing communication libraries. It performs very efficiently on a variety of hardware and communication platforms. To study the efficiency of three MPI library implementations, the performance of the Parallel ITPACK solvers was measured on several distributed memory architectures and on clusters of workstations for a testbed of elliptic boundary value PDE problems. We present a comparison of these MPI library implementationswith PVM and the native communication libraries, based on their performance on these tests. Moreover we have implemented in MPI, a parallel mesh generator that concurrently produces a semiâ€“optimal partitioning of the mesh to support various domain decomposition solution strategies across the above platforms. The results indicate that the MPI overhead varies among the various implementations without significantly affecting the algorithmic speedup even on clusters of workstations.
On the Future of Problem Solving Environments
, 2000
"... In this paper we review the current state of the problem solving ellvironment (PSE) field and make projections for the future. First we describe the computing context, the definition of a PSE and the goals of a PSE. The stateoftheart is summarized along with sources (books, bibliographies, web si ..."
Abstract
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In this paper we review the current state of the problem solving ellvironment (PSE) field and make projections for the future. First we describe the computing context, the definition of a PSE and the goals of a PSE. The stateoftheart is summarized along with sources (books, bibliographies, web site:;) of more detailed information. The principal components and paradigms for building PSEs are presented. The discussion of the future is given in three parts; future trends, scenarioo for 2010/2025, and research issues to be addres:;ed.
Parallel Workload Characterization for Scientific Computing Applications
"... Introduction The methodologies and techniques for characterizing the workload of parallel systems are related to the performance of such systems. Parallel systems are characterized by a large number of cooperating/communicating processors. Their performance is mainly influenced by the structure of ..."
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Introduction The methodologies and techniques for characterizing the workload of parallel systems are related to the performance of such systems. Parallel systems are characterized by a large number of cooperating/communicating processors. Their performance is mainly influenced by the structure of the applications and their ability to exploit the parallel features of the system. In addition, parallel/distributed software communication libraries, such as PVM [1], PARMACS [2], PICL [3], MPI [4], have nearly standarized the communication among parallel processors. Furthermore, new performance measuring and visualization tools have spurred [5], and tools used in sequential machines have been redesigned for parallel machines, like the ARRAYTRACER [6] based on PVM. New standard parallel languages, such as HPF [7] along with the ADAPTOR [8] tool, are used to rewrite sequential application code into parallel code. Thus, workload performance also depends on the efficient implementation