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High Performance computing for Eigenvalue Solver in Density-Matrix Renormalization Group Method: parallelization of the Hamiltonian Matrix-Vector Multiplication

High Performance Computing for Eigenvalue Solver in Density-...

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8th International Conference on High Performance computing for Computational Science (VECPAR 2008)

作者： Yamada, Susumu Okumura, Masahiko Machida, Masahiko Japan Atom Energy Agcy CCSE Taito Ku 6-9-3 Higashi Ueno Tokyo 1100015 Japan JST CREST Kawaguchi Saitama 3300012 Japan

ISBN: (纸本)9783540928584

The Density Matrix Renormalization Group (DMRG) method is widely used by computational physicists as a high accuracy tool to obtain the ground state of large quantum lattice models. Since the DMRG method has been originally developed for 1-D models, many extended method to a 2-D model have been proposed. However, some of them have issues in term of their accuracy. It is expected that the accuracy of the DMRG method extended directly to 2-D models is excellent. The direct extension DMRG method demands an enormous memory space. Therefore, we parallelize the matrix-vector multiplication irk iterative methods for solving the eigenvalue problem, which is the most time-and memory-consuming operation. We find that the parallel efficiency of the direct extension DMRG method shows a good one as the number of states kept increases.

关键词： parallel and distributed computing DMRG method matrix-vector multiplication eigenvalue problem quantum lattice systems

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The engineering handbook of smart technology for aging, disability, and independence /

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2008年

作者： edited by Abdelsalam (Sumi) Helal Mounir Mokhtari Bessam Abdulrazak.

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parallel history matching and associated forecast at the center for interactive smart oilfield technologies

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JOURNAL OF SUPERcomputing 2007年第2期41卷 109-117页

作者： Nomura, Ken-Ichi Kalia, Rajiv K. Nakano, Aiichiro Vashishta, Priya Landa, Jorge L. Univ So Calif CiSoft CACS Los Angeles CA 90089 USA Univ So Calif Dept Comp Sci Los Angeles CA 90089 USA Univ So Calif Dept Phys & Astron Los Angeles CA 90089 USA Univ So Calif Dept Chem Engn & Mat Sci Los Angeles CA 90089 USA ChevronTexaco San Ramon CA 94583 USA

We have developed a parallel and distributed computing framework to solve an inverse problem, which involves massive data sets and is of great importance to petroleum industry. A Monte Carlo method, combined with proxies to avoid excessive data processing, is employed to identify reservoir simulation models that best match the oilfield production history. Subsequently, the selected models are used to forecast future productions with uncertainty estimates. The parallelization framework combines: (1) message passing for tightly coupled intra-simulation decomposition;and (2) scheduler/Grid remote procedure calls for model parameter sweeps. A preliminary numerical test has included 3,159 simulations on a 256-processor Intel Xeon cluster at the USC-CACS. The results provide uncertainty estimates of unprecedented precision.

关键词： smart oilfield history matching and forecast inverse problem Monte Carlo method parallel and distributed computing massive data sets

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A model for obstacles to be used in simulations of wireless sensor networks and its application in studying routing protocol performance

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2007年第8期83卷 587-608页

作者： Chatzigiannakis, Ioannis Mylonas, Georgios Nikoletseas, Sotiris Univ Patras Dept Comp Engn & Informat Comp Technol Inst Patras 26500 Greece

We propose a simple obstacle model to be used while simulating wireless sensor networks. To the best of our, knowledge, this is the first time such an integrated and systematic obstacle model for these networks has been proposed. We define several types of obstacles that can be found inside the deployment area of a wireless sensor network and provide a categorization of these obstacles based on their nature (physical and communication obstacles, i.e. obstacles that are formed out of node distribution patterns or have physical presence, respectively), their shape and their change of nature over time. We make an extension to a custom-made sensor network simulator (simDust) and conduct a number of simulations in order to study the effect of obstacles on the performance of some representative (in terms of their logic) data propagation protocols for wireless sensor networks. Our findings confirm that obstacle presence has a significant impact on protocol performance, and also that different obstacle shapes and sizes may affect each protocol in different ways. This provides an insight into how a routing protocol will perform in the presence of obstacles and highlights possible protocol shortcomings. Moreover, our results show that the effect of obstacles is not directly related to the density of a sensor network, and cannot be emulated only by changing the network density.

关键词： applications in computer networks sensor networks modeling and simulation environments parallel and distributed computing performance evaluation obstacle avoidance routing

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Load balancing approach parallel algorithm for frequent pattern mining

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9th International Conference on parallel computing Technologies

作者： Yu, Kun-Ming Zhou, Jiayi Hsia, Wei Chen Chung Hua Univ Inst Engn Sci Hsinchu Taiwan Chung Hua Univ Dept Comp Sci & Informat Engn Hsinchu Taiwan Chung Hua Univ Dept Informat Management Hsinchu Taiwan

ISBN: (纸本)9783540739395

Association rules mining from transaction-oriented databases is an important issue in data mining. Frequent pattern is crucial for association rules generation, time series analysis, classification, etc. There are two categories of algorithms that had been proposed, candidate set generate-and-test approach (Apriori-like) and Pattern growth approach. Many methods had been proposed to solve the association rules mining problem based on FP-tree instead of Apriori-like, since apriori-like algorithm scans the database many times. However, the computation time is costly when the database size is large with FP-tree data structure. parallel and distributed computing is a good strategy to solve this circumstance. Some parallel algorithms had been proposed, however, most of them did not consider the load balancing issue. In this paper, we proposed a parallel and distributed mining algorithm based on FP-tree structure, Load Balancing FP-Tree (LFP-tree). The algorithm divides the item set for mining by evaluating the tree's width and depth. Moreover, a simple and trusty calculate formulation for loading degree is proposed. The experimental results show that LFP-tree can reduce the computation time and has less idle time compared with parallel FP-Tree (PFP-tree). In addition, it has better speed-up ratio than PFP-tree when number of processors grow. The communication time can be reduced by preserving the heavy loading items in their local computing node.

关键词： FP-tree data mining association rules parallel and distributed computing load-balancing

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A service oriented system for on demand dynamic structural analysis over computational Grids

A service oriented system for on demand dynamic structural a...

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7th International Conference on High Performance computing for Computational Science

作者： Alonso, J. M. Hernandez, V. Lopez, R. Molto, G. Univ Politecn Valencia Dept Sist Informat & Computac Camino Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783540713500

In this paper we describe the implementation of a service oriented environment that enables to couple a parallel application, which performs the 3D linear dynamic structural analysis of high-rise buildings, to a Grid computing infrastructure. The Grid service, developed under Globus Toolkit 4, exposes the dynamic simulation as a service to the structural scientific community. It employs the GMarte middleware, a metascheduler that enables to perform the computationally intensive simulations on the distributed resources of a Grid-based infrastructure.

关键词： parallel and distributed computing cluster and grid computing large scale simulations in all areas of engineering and science

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Evaluation of several variants of explicitly restarted Lanczos eigensolvers and their parallel implementations

Evaluation of several variants of explicitly restarted Lancz...

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7th International Conference on High Performance computing for Computational Science

作者： Hernandez, V. Roman, J. E. Tomas, A. Univ Politecn Valencia D Sist Informat & Computac Camino Vera S-N E-46022 Valencia Spain

ISBN: (纸本)9783540713500

It is well known that the Lanczos process suffers from loss of orthogonality in the case of finite-precision arithmetic. Several approaches have been proposed in order to address this issue, thus enabling the successful computation of approximate eigensolutions. However, these techniques have been studied mainly in the context of long Lanczos runs, but not for restarted Lanczos eigensolvers. Several variants of the explicitly restarted Lanczos algorithm employing different reorthogonalization strategies have been implemented in SLEPc, the Scalable Library for Eigenvalue Computations. The aim of this work is to assess the numerical robustness of the proposed implementations as well as to study the impact of reorthogonalization in parallel efficiency.

关键词： numerical methods parallel and distributed computing

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distributed generative data mining

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7th Industrial Conference on Data Mining

作者： Ramos, Ruy Camacho, Rui LIACC Rua Ceuta 118-6 P-4050190 Oporto Portugal EEUP P-4200465 Oporto Portugal

ISBN: (纸本)9783540734345

A process of Knowledge Discovery in Databases (KDD) involving large amounts of data requires a considerable amount of computational power. The process may be done on a dedicated and expensive machinery or, for some tasks, one can use distributed computing techniques on a network of affordable machines. In either approach it is usual the user to specify the workflow of the sub-tasks composing the whole KDD process before execution starts. In this paper we propose a technique that we call distributed Generative Data Mining. The generative feature of the technique is due to its capability of generating new sub-tasks of the Data Mining analysis process at execution time. The workflow of sub-tasks of the DM is, therefore, dynamic. To deploy the proposed technique we extended the distributed Data Mining system HARVARD and adapted an Inductive Logic Programming system (IndLog) used in a Relational Data Ming task. As a proof-of-concept, the extended system was used to analyse an artificial dataset of a credit scoring problem with eighty million records.

关键词： data mining parallel and distributed computing inductive logic programming

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Alternative model representations and computing capacity: Implications for model management

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DECISION SUPPORT SYSTEMS 2006年第3期42卷 1413-1430页

作者： Gnanendran, Kingsley Sundarraj, R. P. Univ Waterloo Dept Management Sci Waterloo ON N2L 3G1 Canada Univ Scranton Dept Operat & Informat Mangement Scranton PA 18510 USA

Recent research on model management systems (MMS) recognizes the importance of considering potential algorithmic performance in the selection of an appropriate model to solve a real-world problem. Model selection, as typically viewed in the literature however, is the process of selecting from among alternative model classes, rather than from alternative mathematical representations of the same model class. In this paper, we take up this subtler aspect of model selection, and provide tangible evidence that shows how just changing the representation of a model can have a dramatic impact on algorithmic performance. Using problem decomposition and distributed processing, we conduct a series of computational experiments to study the interrelationships between model representation, computing capacity, and algorithmic performance. We discuss potential implications of our results for improving MMS design and address a key prerequisite for the enhanced design, by proposing and validating an approach for solution time prediction. (c) 2005 Elsevier B.V. All rights reserved.

关键词： model management systems distributed model management model selection parallel and distributed computing grid computing metacomputing decomposition

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A robust framework for real-time distributed processing of satellite data

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JOURNAL OF parallel and distributed computing 2006年第3期66卷 403-418页

作者： Tehranian, S Zhao, YS Harvey, T Swaroop, A Mckenzie, K AC Technol Inc Adv Computat & Informat Serv Lanham MD 20706 USA Natl Environm Satellite Data & Informat Serv NOAA US Dept Commerce Suitland MD 20746 USA

It is estimated that future satellite instruments such as the Advanced Baseline Imager (ABI) and the Hyperspectral Environmental Suite (HES) on the GOES-R series of satellites will provide raw data volume of about 1.5 Terabyte per day. Due to the high data rate, satellite ground data processing will require considerable computing power to process data in real-time. Cluster technologies employing a multi-processor system present the only current economically viable option. To sustain high levels of system reliability and operability in a cluster-oriented operational environment, a fault-tolerant data processing framework is proposed to provide a platform for encapsulating science algorithms for satellite data processing. The science algorithms together with the framework are hosted on a Linux cluster. In this paper we present an architectural model and a system prototype for providing performance, reliability, and scalability of candidate hardware and software for a satellite data processing system. Furthermore, benchmarking results are presented for a selected number of science algorithms for the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) instrument showing that considerable performance can be gained without sacrificing the reliability and high availability constraints imposed on the operational cluster system. (C) 2006 Elsevier Inc. All rights reserved.

关键词： real-time systems parallel and distributed computing task scheduling reliability

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