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20
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 in-situ 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 in-situ 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 micro-benchmark 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 last-generation Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel tech-niques treat remotely sensed data not as images, but as unordered listings of ..."
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The wealth spatial and spectral information available from last-generation Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel tech-niques treat remotely sensed data not as images, but as unordered listings of spectral measurements with no spa-tial arrangement. In thematic classification applications, however, the integration of spatial and spectral informa-tion can be greatly beneficial. Although such integrated ap-proaches can be efficiently mapped in homogeneous com-modity clusters, low-cost heterogeneous networks of com-puters (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 cluster-based analysis of high-dimensional re-motely sensed image data sets. The algorithms accuracy and parallel performance are tested (in the context of a real precision agriculture application) using two parallel plat-forms: a fully heterogeneous cluster made up of 16 work-stations 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 Multi-core 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 prob-lems 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 prob-lems 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 neces-sitates enhancements to them. In this paper, we present these enhancements. Our study is based on experiments using six ap-plications to perform Parallel Matrix-matrix Multiplication (PMM) on an HCoM employing the two distribution strategies. Keywords- Heterogeneous ScaLAPACK; HeteroMPI; multicore clusters; matrix-matrix multiplication; heterogenous clusters I.
Heterogeneous Parallel Computing in Remote Sensing Applications: Current Trends and Future Perspectives
, 2006
"... Heterogeneous networks of computers have rapidly be-come a very promising commodity computing solution, ex-pected to play a major role in the design of high per-formance 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 be-come a very promising commodity computing solution, ex-pected to play a major role in the design of high per-formance 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 high-dimensional remotely sensed images, with particular em-phasis on target detection and land-cover mapping appli-cations. Experimental results are presented in the con-text 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 paral-lel Beowulf cluster at NASA’s Goddard Space Flight Center. Combined, these parts deliver a snapshot of the state-of-the-art in those areas, and a thoughtful perspective on the potential and challenges of applying heterogeneous com-puting practices to remote sensing problems. 1.
Two-Dimensional Matrix Partitioning for Parallel Computing on Heterogeneous Processors Based on Their Functional Performance Models
"... Abstract. The functional performance model (FPM) of heterogeneous proces-sors 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 proces-sors 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 het-erogeneous processors are already being used in solving complicated linear al-gebra 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 het-erogeneous processors and employing their 2D FPMs. Experiments with a par-allel matrix multiplication application on a local heterogeneous computational cluster demonstrate the efficiency of this algorithm.
Experimental Study of Six Different Parallel Matrix-Matrix 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 matrix-matrix 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 two-dimensional 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 two-dimensional 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.
High-performance 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 last-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional 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 last-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional 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 latest-generation sensors is the enormous amounts of data that they generate. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models in remote sensing missions. This paper explores three HPC-based 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 cluster-based parallel computing; 2) Distributed computing using heterogeneous networks of workstations; and 3) FPGA-based hardware implementations. Combined, these parts deliver an excellent snapshot of the state-of-the-art 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 message-passing library for heterogeneous networks of computers ..."
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www.elsevier.com/locate/jpdc HeteroMPI: Towards a message-passing library for heterogeneous networks of computers
