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An Overview of Heterogeneous High Performance and Grid Computing
 In Engineering the Grid
, 2006
"... Abstract. This paper is an overview the ongoing academic research, development, and uses of heterogeneous parallel and distributed computing. This work is placed in the context of scientific computing. The simulation of very large systems often requires computational capabilities which cannot be sat ..."
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Abstract. This paper is an overview the ongoing academic research, development, and uses of heterogeneous parallel and distributed computing. This work is placed in the context of scientific computing. The simulation of very large systems often requires computational capabilities which cannot be satisfied by a single processing system. A possible way to solve this problem is to couple different computational resources, perhaps distributed geographically. Heterogeneous distributed computing is a means to overcome the limitations of single computing systems.
Wrekavoc: a Tool for Emulating Heterogeneity
"... Computer science and especially heterogeneous distributed computing is an experimental science. Simulation, emulation, or insitu implementation are complementary methodologies to conduct experiments in this context. In this paper we address the problem of defining and controlling the heterogeneity ..."
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Computer science and especially heterogeneous distributed computing is an experimental science. Simulation, emulation, or insitu implementation are complementary methodologies to conduct experiments in this context. In this paper we address the problem of defining and controlling the heterogeneity of a platform. We evaluate the proposed solution, called Wrekavoc, with microbenchmark and by implementing algorithms of the literature. 1.
Parallel morphological/neural classification of remote sensing images using fully heterogeneous and homogeneous commodity clusters
 Proc. IEEE International Conference on Cluster Computing
, 2006
"... The wealth spatial and spectral information available from lastgeneration Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel techniques treat remotely sensed data not as images, but as unordered listings of ..."
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The wealth spatial and spectral information available from lastgeneration Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel techniques treat remotely sensed data not as images, but as unordered listings of spectral measurements with no spatial arrangement. In thematic classification applications, however, the integration of spatial and spectral information can be greatly beneficial. Although such integrated approaches can be efficiently mapped in homogeneous commodity clusters, lowcost heterogeneous networks of computers (HNOCs) have soon become a standard tool of choice in Earth and planetary missions. In this paper, we develop a new morphological/neural parallel algorithm for commodity clusterbased analysis of highdimensional remotely sensed image data sets. The algorithms accuracy and parallel performance are tested (in the context of a real precision agriculture application) using two parallel platforms: a fully heterogeneous cluster made up of 16 workstations at University of Maryland, and a massively parallel Beowulf cluster at NASA’s Goddard Space Flight Center. 1.
Experimental Study of Six Different Implementations of Parallel
 Matrix Multiplication on Heterogeneous Computational Clusters of Multicore Processors” in Proceedings of Parallel, Distributed and NetworkBased Processing (PDP
, 2010
"... Abstract—Two strategies of distribution of computations can be used to implement parallel solvers for dense linear algebra problems for Heterogeneous Computational Clusters of Multicore Processors (HCoMs). These strategies are called Heterogeneous Process Distribution Strategy (HPS) and Heterogeneo ..."
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Abstract—Two strategies of distribution of computations can be used to implement parallel solvers for dense linear algebra problems for Heterogeneous Computational Clusters of Multicore Processors (HCoMs). These strategies are called Heterogeneous Process Distribution Strategy (HPS) and Heterogeneous Data Distribution Strategy (HDS). They are not novel and have been researched thoroughly. However, the advent of multicores necessitates enhancements to them. In this paper, we present these enhancements. Our study is based on experiments using six applications to perform Parallel Matrixmatrix Multiplication (PMM) on an HCoM employing the two distribution strategies. Keywords Heterogeneous ScaLAPACK; HeteroMPI; multicore clusters; matrixmatrix multiplication; heterogenous clusters I.
Heterogeneous Parallel Computing in Remote Sensing Applications: Current Trends and Future Perspectives
, 2006
"... Heterogeneous networks of computers have rapidly become a very promising commodity computing solution, expected to play a major role in the design of high performance computing systems for remote sensing missions. Currently, only a few parallel processing strategies are available in this research ..."
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Heterogeneous networks of computers have rapidly become a very promising commodity computing solution, expected to play a major role in the design of high performance computing systems for remote sensing missions. Currently, only a few parallel processing strategies are available in this research area, and most of them assume homogeneity in the underlying computing platform. This paper develops several highly innovative heterogeneous parallel algorithms for information extraction from highdimensional remotely sensed images, with particular emphasis on target detection and landcover mapping applications. Experimental results are presented in the context of a realistic application, using real data collected by NASA’s Jet Propulsion Laboratory over the World Trade Center complex in New York City after September 11th, 2001. Parallel performance of the proposed algorithms is discussed using several (fully and partially) heterogeneous networks at University of Maryland, and a massively parallel Beowulf cluster at NASA’s Goddard Space Flight Center. Combined, these parts deliver a snapshot of the stateoftheart in those areas, and a thoughtful perspective on the potential and challenges of applying heterogeneous computing practices to remote sensing problems. 1.
TwoDimensional Matrix Partitioning for Parallel Computing on Heterogeneous Processors Based on Their Functional Performance Models
"... Abstract. The functional performance model (FPM) of heterogeneous processors has proven to be more realistic than the traditional models because it integrates many important features of heterogeneous processors such as the processor heterogeneity, the heterogeneity of memory structure, and the effe ..."
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Abstract. The functional performance model (FPM) of heterogeneous processors has proven to be more realistic than the traditional models because it integrates many important features of heterogeneous processors such as the processor heterogeneity, the heterogeneity of memory structure, and the effects of paging. Optimal 1D matrix partitioning algorithms employing FPMs of heterogeneous processors are already being used in solving complicated linear algebra kernel such as dense factorizations. However, 2D matrix partitioning algorithms for parallel computing on heterogeneous processors based on their FPMs are unavailable. In this paper, we address this deficiency by presenting a novel iterative algorithm for partitioning a dense matrix over a 2D grid of heterogeneous processors and employing their 2D FPMs. Experiments with a parallel matrix multiplication application on a local heterogeneous computational cluster demonstrate the efficiency of this algorithm.
Experimental Study of Six Different Parallel MatrixMatrix Multiplication Applications for Heterogeneous Computational Clusters of Multicore Processors
, 2009
"... In this document, we describe two strategies of distribution of computations that can be used to implement parallel solvers for dense linear algebra problems for Heterogeneous Computational Clusters of Multicore Processors (HCoMs). These strategies are called Heterogeneous Process Distribution Strat ..."
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In this document, we describe two strategies of distribution of computations that can be used to implement parallel solvers for dense linear algebra problems for Heterogeneous Computational Clusters of Multicore Processors (HCoMs). These strategies are called Heterogeneous Process Distribution Strategy (HPS) and Heterogeneous Data Distribution Strategy (HDS). They are not novel and have already been researched thoroughly. However, the advent of multicores necessitates enhancements to them. We conduct experiments using six applications utilizing the various distribution strategies to perform parallel matrixmatrix multiplication (PMM) on a local HCoM. The first application calls ScaLAPACK PBLAS routine PDGEMM, which uses the traditional homogeneous strategy of distribution of computations. The second application is an MPI application, which utilizes HDS to perform the PMM. The application requires an input, which is the twodimensional processor grid arrangement to use during the execution of the PMM. The third application is also an MPI application but that uses HPS to perform the PMM. The application requires two inputs, which are the number of threads to run per process and the twodimensional process grid arrangement to use during the execution of the PMM. The fourth
Systems and Computation,
"... This paper discusses the design and the implementation of the LU factorization routines included in the Heterogeneous ScaLAPACK library, which is built on top of ScaLAPACK. These routines are used in the factorization and solution of a dense system of linear equations. They are implemented using opt ..."
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This paper discusses the design and the implementation of the LU factorization routines included in the Heterogeneous ScaLAPACK library, which is built on top of ScaLAPACK. These routines are used in the factorization and solution of a dense system of linear equations. They are implemented using optimized PBLAS, BLACS and BLAS libraries for heterogeneous computational clusters. We present the details of the implementation as well as performance results on a heterogeneous computing cluster. 1.
Highperformance computing in remotely sensed hyperspectral imaging: The purity index algorithm as a case study
 In: Proceedings of the 7th Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC). Rhodes Island
"... The incorporation of lastgeneration sensors to airborne and satellite platforms is currently producing a nearly continual stream of highdimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral imaging is ..."
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The incorporation of lastgeneration sensors to airborne and satellite platforms is currently producing a nearly continual stream of highdimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral imaging is a new technique in remote sensing that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. The price paid for such a wealth of spectral information available from latestgeneration sensors is the enormous amounts of data that they generate. In recent years, several efforts have been directed towards the incorporation of highperformance computing (HPC) models in remote sensing missions. This paper explores three HPCbased paradigms for efficient information extraction from remote sensing data using the Pixel Purity Index (PPI) algorithm (available from the popular Kodak’s Research Systems ENVI software) as a case study for algorithm optimization. The three considered approaches are: 1) Commodity clusterbased parallel computing; 2) Distributed computing using heterogeneous networks of workstations; and 3) FPGAbased hardware implementations. Combined, these parts deliver an excellent snapshot of the stateoftheart in those areas, and offer a thoughtful perspective on the potential and emerging challenges of adapting HPC models to remote sensing problems. 1.
unknown title
, 2005
"... www.elsevier.com/locate/jpdc HeteroMPI: Towards a messagepassing library for heterogeneous networks of computers ..."
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www.elsevier.com/locate/jpdc HeteroMPI: Towards a messagepassing library for heterogeneous networks of computers