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19th IASTED international conference on parallel and distributed computing and systems

作者： Trefftz, Christian Tao, Yonglei Jorgensen, Paul School of Computing Grand Valley State University Allendale MI 49401-9403 United States

ISBN: (纸本)9780889867048

OpenMP is a popular programming environment for high-level shared-memory parallel processing in C, C++ and Fortran on multiple platforms. Using OpenMP, application developers can focus on the code they want to parallelize for better performance without having to do all the work in code parallelization. While hiding much of the complexity in parallel processing, it imposes a great challenge for the developers to make sure the run-time behavior of the code is as expected. In this paper, we explore potential risks with using OpenMP through reduction, a frequently performed operation in parallel processing.

关键词： Application programming interfaces (API)

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Full parallel decomposition of computational algorithm with discrete state estimator

Full parallel decomposition of computational algorithm with ...

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19th IASTED international conference on parallel and distributed computing and systems

作者： Pelc, Mariusz Byrski, Witold School of Computing and Mathematical Sciences University of Greenwich Park Row Greenwich SE10 9LS London United Kingdom Faculty of Electrical Engineering Automatics Computer Science and Electronics AGH University of Science and Technology Al. Mickiewicza 30 Krakow Poland

ISBN: (纸本)9780889867048

In this paper parallel approach to the algorithm of discrete exact state estimation is presented. the novel idea of double-layer parallel decomposition of such computational algorithm is explained and results of its full implementation are showed. Numerical tests of the computational algorithm with discrete exact state estimator in the context of its scalability are included. All of presented results were obtained by the use of MPI library in the Linux cluster.

关键词： State estimation

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Towards Scalable Optimal Sequence Homology Detection

Towards Scalable Optimal Sequence Homology Detection

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19th international conference on High Performance computing (HiPC)

作者： Daily, Jeff Krishnamoorthy, Sriram Kalyanaraman, Ananth Pacific NW Natl Lab Richland WA 99352 USA

ISBN: (纸本)9781467323703;9781467323727

the field of bioinformatics and computational biology is experiencing a data revolution - experimental techniques to procure data have increased in throughput, improved in accuracy and reduced in costs. this has spurred an array of high profile sequencing and data generation projects. While the data repositories represent untapped reservoirs of rich information critical for scientific breakthroughs, the analytical software tools that are needed to analyze large volumes of such sequence data have significantly lagged behind in their capacity to scale. In this paper, we address homology detection, which is a fundamental problem in large-scale sequence analysis with numerous applications. We present a scalable framework to conduct large-scale optimal homology detection on massively parallel super-computing platforms. Our approach employs distributed memory work stealing to effectively parallelize optimal pairwise alignment computation tasks. Results on 120,000 cores of the Hopper Cray XE6 supercomputer demonstrate strong scaling and up to 2.42 x 10(7) optimal pairwise sequence alignments computed per second (PSAPS), the highest reported in the literature.

关键词： Cray computers DNA bioinformatics distributed memory systems mainframes parallel machines pattern recognition

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Data redistribution and remote method invocation for coupled components

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JOURNAL OF parallel AND distributed computing 2006年第7期66卷 931-946页

作者： Bertrand, Felipe Bramley, Randall Bernholdt, David E. Kohl, James A. Sussman, Alan Larson, Jay W. Damevski, Kostadin B. Indiana Univ Dept Comp Sci Bloomington IN 47405 USA Oak Ridge Natl Lab Div Math & Comp Sci Oak Ridge TN USA Univ Maryland UMIACS College Pk MD 20742 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA Argonne Natl Lab Div Math & Comp Sci Argonne IL 60439 USA Univ Utah Sci Comp & Imaging Inst Salt Lake City UT 84112 USA

With the increasing availability of high-performance massively parallel computer systems, the prevalence of sophisticated scientific simulation has grown rapidly. the complexity of the scientific models being simulated has also evolved, leading to a variety of coupled multi-physics simulation codes. Such cooperating parallel programs require fundamentally new interaction capabilities, to efficiently exchange parallel data structures and collectively invoke methods across programs. So-called "M x N" research, as part of the common component architecture (CCA) effort, addresses these special and challenging needs, to provide generalized interfaces and tools that support flexible parallel data redistribution and parallel remote method invocation. Using this technology, distinct simulation codes with disparate distributed data decompositions can work together to achieve greater scientific discoveries. (C) 2006 Elsevier Inc. All rights reserved.

关键词： common component architecture model coupling parallel data redistribution parallel remote method invocation

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Job Scheduling with License Reservation: A Semantic Approach

Job Scheduling with License Reservation: A Semantic Approach

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19th international Euromicro conference on parallel, distributed and Network-Based Processing (PDP)

作者： Ejarque, Jorge Micsik, Andras Sirvent, Raul Pallinger, Peter Kovacs, Laszlo Badia, Rosa M. Barcelona Supercomp Ctr Grid Comp & Clusters Grp Barcelona Spain Spanish Natl Res Council IIIA CSIC Arti Cial Intelligence Res Inst Barcelona Spain Univ Politecn Cataluna Comp Architecture Dept Barcelona Spain Hungarian Acad Sci MTA SZTAKI Comp & Automat Res Inst Distributed Syst Dept Budapest Hungary

ISBN: (纸本)9780769543284

the license management is one of the main concerns when Independent Software Vendors (ISV) try to distribute their software in computing platforms such as Clouds. they want to be sure that customers use their software according to their license terms. the work presented in this paper tries to solve part of this problem extending a semantic resource allocation approach for supporting the scheduling of job taking into account software licenses. this approach defines the licenses as another type of computational resource which is available in the system and must be allocated to the different jobs requested by the users. License terms are modeled as resource properties, which describe the license constraints. A resource ontology has been extended in order to model the relations between customers, providers, jobs, resources and licenses in detail and make them machine processable. the license scheduling has been introduced in a semantic resource allocation process by providing a set of rules, which evaluate the semantic license terms during the job scheduling.

关键词： multi-agent semantics scheduling resource allocation software licenses grid computing cloud computing distributed systems

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Performance problems of using system-predicted runtimes for parallel job scheduling

Performance problems of using system-predicted runtimes for ...

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19th IASTED international conference on parallel and distributed computing and systems

作者： Vasupongayya, Sangsuree Chiang, Su-Hui Computer Sciences Department Portland State University United States

ISBN: (纸本)9780889867048

Many prediction techniques based on historical data have been proposed to reduce over-estimations of job runtimes provided by users. they were shown to improve the accuracy of runtime estimates and scheduling performance of backfill policies, according to particular error metrics and average performance measures. However, using a more complete set of performancemeasures and a new error metric, we show potential performance problems of using previous prediction techniques for job scheduling. Furthermore, we show simply adding half of the requested runtime to each initial prediction greatly reduces the problems.

关键词： Forecasting

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Further Improvement on GPU-Based parallel Implementation of WRF 5-Layer thermal Diffusion Scheme

Further Improvement on GPU-Based Parallel Implementation of ...

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19th IEEE international conference on parallel and distributed systems (ICPADS)

作者： Huang, Melin Huang, Bormin Mielikainen, Jarno Huang, H. -L Allen Goldberg, Mitchell D. Mehta, Ajay Univ Wisconsin Madison Space Sci & Engn Ctr Madison WI 53706 USA Natl Ocean & Atmospher Adm Joint Polar Satellite Syst Off Silver Spring MD USA

ISBN: (纸本)9781479920815

the Weather Research and Forecasting (WRF) model has been widely employed for weather prediction and atmospheric simulation with dual purposes in forecasting and research. Land-surface models (LSMs) are parts of the WRF model, which is used to provide information of heat and moisture fluxes over land and sea-ice points. the 5-layer thermal diffusion simulation is an LSM based on the MM5 soil temperature model with an energy budget made up of sensible, latent, and radiative heat fluxes. Owing to the feature of no interactions among horizontal grid points, the LSMs are very favorable for massively parallel processing. the study presented in this article demonstrates the parallel computing efforts on the WRF 5-layer thermal diffusion scheme using Graphics Processing Unit (GPU). Since this scheme is only one intermediate module of the entire WRF model, the involvement of the I/O transfer does not occur in the intermediate process. By employing one NVIDIA GTX 680 GPU in the case without I/O transfer, our optimization efforts on the GPU-based 5-layer thermal diffusion scheme can reach a speedup as high as 247.5x with respect to one CPU core, whereas the speedup for one CPU socket with respect to one CPU core is only 3.1x. We can even boost the speedup to 332x with respect to one CPU core when three GPUs are applied.

关键词： 5-layer thermal diffusion Compute Unified Device Architecture (CUDA) Graphics Processing Unit (GPU) speedup Weather Research and Forecasting (WRF)

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Analyzing the Effects of Reordering Work List Items for Selected Control Flow Patterns 19

Analyzing the Effects of Reordering Work List Items for Sele...

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IEEE 19th international Enterprise distributed Object computing (EDOCW)

作者： Pflug, Johannes Rinderle-Ma, Stefanie Univ Vienna Res Grp Workflow Syst & Technol Vienna Austria

ISBN: (纸本)9781467393317

Efficient resource management is an important requirement for many process-oriented applications. Typically, work items are assigned to resources through their work lists. there are many reasons for reordering work items in a resource's work list. For process scheduling, for example, swapping process instances constitutes a mean to keep due times. At the same time, reducing the throughput time of the global process is typically not the primary goal. For process optimization, in turn, the implications of reordering work items on the overall temporal performance of the process might be crucial. In this paper, we investigate how reordering work items affects performance parameters that are typically associated with a first-in-first-out processing mechanism at resources. the analysis is conducted for single process tasks and for typical control flow patterns such as sequence as well as parallel and alternative branchings. It is shown that the implications on the global throughput time are less than expected, while the effects on instance-based parameters strongly depend on the control-flow pattern in which the reordering mechanism is implemented. the results are supported by means of a simulation.

关键词： information systems resource allocation scheduling alternative branchings control flow patterns first-in-first-out processing mechanism instance based parameters parallel branchings process optimization process scheduling resource management work list items reordering mechanism Delays Electronic mail Engines Optimization Process control throughput Yttrium Business Process Analysis Business Process Management Business Process Optimization Work List Management management of resources FLOW PATTERNS Process optimization Business process management electronic mail throughput time Engines Process control Rare earths yttrium throughput Computer based information systems Resource utilization delay

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parallel double Divide and Conquer and its evaluation on a super computer

Parallel double Divide and Conquer and its evaluation on a s...

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19th IASTED international conference on parallel and distributed computing and systems

作者： Konda, Taro Nakamura, Yoshimasa Department of Applied Mathematics and Physics Graduate School of Informatics Kyoto University Yoshida-Honmachi Sakyo-ku Kyoto Japan SORST JST Japan

ISBN: (纸本)9780889867048

this paper presents comprehensive evaluations of parallel double Divide and Conquer for singular value decomposition on a super computer, HPC2500. For bidiagonal SVD, double Divide and Conquer was proposed. It first computes singular values by a compact version of Divide and Conquer. the corresponding singular vectors are then computed by twisted factorization. the speed and accuracy of double Divide and Conquer are as good or even better than standard algorithms such as QR and the original Divide and Conquer. Moreover, it shows high scalability even on a PC cluster, distributed memory architecture. parallel algorithm of dDC and numerical results in some architectural options, matrix sizes and types on HPC2500, SMP cluster is shown.

关键词： Singular value decomposition

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A new scalable and efficient parallel algorithm (PRACAL) for clustering large datasets

A new scalable and efficient parallel algorithm (PRACAL) for...

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19th IASTED international conference on parallel and distributed computing and systems

作者： Salem, Sameh A. Nandi, Asoke K. Signal Processing and Communications Group Department of Electrical Engineering and Electronics University of Liverpool Brownlow Hill Liverpool L69 3GJ United Kingdom

ISBN: (纸本)9780889867048

Data clustering is a common technique for data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Due to the continuous increase of datasets size and the intensive computation of clustering algorithms when used for analyzing large datasets, developing of efficient clustering algorithms is needed for processing time reduction. this paper describes the design and implementation of a recently developed clustering algorithm RACAL [1], which is a RAdius based Clustering ALgorithm. the proposed parallel algorithm (PRACAL) has the ability to cluster large datasets of high dimensions in a reasonable time, which leads to a higher performance computing.

关键词： Clustering algorithms

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