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Comparative study of the server-initiated lowest algorithm using a load balancing index based on the process behavior for heterogeneous environment

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2006年第3期9卷 313-319页

作者： de Mello, R. F. Trevelin, L. C. de Paiva, M. S. V. Yang, Laurence T. Univ Sao Paulo Inst Math Sci & Comp BR-13560 Sao Carlos Brazil Univ Fed Sao Carlos Dept Comp Sci BR-13560 Sao Carlos Brazil St Francis Xavier Univ Antigonish NS Canada

The availability of low cost microcomputers and the evolution of computer networks have increased the development of distributed systems. In order to get a better process allocation on distributed environments, several load balancing algorithms have been proposed. Generally, these algorithms consider as the information policy's load index the length of the CPU's process waiting queue. This paper modifies the Server-Initiated Lowest algorithm by using a load index based on the resource occupation. Using this load index the Server-Initiated Lowest algorithm is compared to the Stable symmetrically initiated, which nowadays is defined as the best choice. The comparisons are made by using simulations. The simulations showed that the modified Server-Initiated Lowest algorithm had better results than the Symmetrically Initiated one.

关键词： load balancing algorithm high performance parallel and distributed computing

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Sim-PETEK: A parallel Simulation Execution Framework for Grid Environments

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JOURNAL OF DEFENSE MODELING AND SIMULATION-APPLICATIONS METHODOLOGY TECHNOLOGY-JDMS 2012年第4期9卷 303-319页

作者： Bozagac, Doruk Karaduman, Gulsah Kara, Ahmet Alpdemir, M. Nedim TUBITAK UEKAE LTAREN Sehit Yzblhan Tan Kislasi 8Cad 417 Sok Ankara Turkey

In this paper we introduce a framework for parallel and distributed execution of simulations (Sim-PETEK), a middleware for minimizing the total run time of batch runs and Monte Carlo trials. Sim-PETEK proposes a generic solution for applications in the simulation domain, which improves on our previous work done to parallelize simulation runs in a single node, multiple central processing unit (CPU) setting. Our new framework aims at managing a heterogeneous computational resource pool consisting of multiple CPU nodes distributed on a potentially geographically dispersed network, through a service-oriented middleware layer that is compliant to Web Services Resource Framework standard, thereby providing a scalable and flexible architecture for simulation software developers. What differentiates Sim-PETEK from a general-purpose, Grid-based job-distribution middleware is a number of simulation-specific aspects regarding the specification, distribution, monitoring, result collection and aggregation of simulation runs. These aspects are prevalent in the structure of the messages and in the protocol of interaction both among the constituent services of the framework and within the interfaces exposed to the external clients.

关键词： parallel and distributed computing simulation simulation output analysis software framework stochastic simulation

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GPGPU-Aided Ensemble Empirical-Mode Decomposition for EEG Analysis During Anesthesia

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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE 2010年第6期14卷 1417-1427页

作者： Chen, Dan Li, Duan Xiong, Muzhou Bao, Hong Li, Xiaoli Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Peoples R China Yanshan Univ Inst Informat Sci & Engn Qinhuangdao 066004 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Univ Sci & Technol Beijing Sch Informat Engn Beijing 100083 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

Ensemble empirical-mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy non-linear or nonstationary data. Unfortunately, since the EEMD is highly compute-intensive, the method does not apply in real-time applications on top of commercial-off-the-shelf computers. Aiming at this problem, a parallelized EEMD method has been developed using general-purpose computing on the graphics processing unit (GPGPU), namely, G-EEMD. A spectral entropy facilitated by G-EEMD was, therefore, proposed to analyze the EEG data for estimating the depth of anesthesia (DoA) in a real-time manner. In terms of EEG data analysis, G-EEMD has dramatically improved the run-time performance by more than 140 times compared to the original serial EEMD implementation. G-EEMD also performs far better than another parallelized implementation of EEMD bases on conventional CPU-based distributed computing technology despite the latter utilizes 16 high-end computing nodes for the same computing task. Furthermore, the results obtained from a pharmacokinetics/pharmacodynamic (PK/PD) model analysis indicate that the EEMD method is slightly more effective than its precedent alternative method (EMD) in estimating DoA, the coefficient of determination R(2) by EEMD is significantly higher than that by EMD(p < 0.05, paired t-test) and the prediction probability P(k) by EEMD is also slighter higher than that by EMD (p < 0.2, paired t-test).

关键词： Depth of anesthesia (DoA) electroencephalogram (EEG) ensemble empirical-mode decomposition (EEMD) general-purpose computing on the graphics processing unit (GPGPU) parallel and distributed computing spectral entropy

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parallel finite element calculation of plastic deformations on Exemplar SPP1000 and on networked workstations

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 1996年第1-4期60卷 409-413页

作者： Bubak, M Chrobak, R Kitowski, J Moscinski, J Pietrzyk, M PEDAGOG UNIV PL-35957 RZESZOW POLAND CYFRONET ACAD COMP CTR PL-30950 KRAKOW POLAND AGH Univ Sci & Technol DEPT MET & SCI MAT PL-30059 KRAKOW POLAND

In this paper we present an approach to parallelization of the program for computation of axisymmetrical forging process. The parallel algorithm we have applied is based on non-overlapping domain decomposition method. A mesh of elements is divided into layers assigned to different processes. The parallel program was written in C using PVM and it was implemented on Convex Exemplar SPP1000 and on networked workstations IBM RS/6000-320. We have investigated dependence of performance of the elaborated parallel program on number of process and on number of nodes in the mesh.

关键词： forging process numerical modelling finite element method parallel and distributed computing PVM network virtual computer

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A practical introduction to butterfly and adaptive resampling in Sequential Monte Carlo

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IFAC-PapersOnLine 2015年第28期48卷 787-792页

作者： Guldas, Hakan Cemgil, A. Taylan Whiteley, Nick Heine, Kari Department of Computer Engineering Bogazici University Istanbul Turkey School of Mathematics University of Bristol United Kingdom Department of Statistics UCL United Kingdom

parallel and distributed computing technologies offer great potential for speed-up of Monte Carlo algorithms. However, in the development of most existing algorithms it has been implicitly assumed that implementation would be on a serial machine, so algorithm structure is often not well-suited to parallel architectures. In recent work the authors have studied the theoretical properties of sequential Monte Carlo algorithms involving a \buttery” resampling method, whose conditional independence structure is intended to better match parallel and distributed architectures, with resampling broken down into stages, allowing sampling tasks for subsets of the particles to be handled concurrently. This paper provides a more practical overview of these methods, including consideration of adaptive resampling schemes, numerical results and an accessible account of theoretical properties. © 2015

关键词： Monte Carlo methods Numerical methods parallel architectures Conditional independences distributed architecture parallel and distributed computing parallelizations Particle filter Practical introduction Sequential Monte Carlo Sequential Monte Carlo algorithms

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GT-WGS: an efficient and economic tool for large-scale WGS analyses based on the AWS cloud service

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BMC GENOMICS 2018年第SUPPL 1期19卷 89-98页

作者： Wang, Yiqi Li, Gen Ma, Mark He, Fazhong Song, Zhuo Zhang, Wei Wu, Chengkun Natl Univ Def Technol Sch Comp Sci Changsha 410000 Hunan Peoples R China Genetalks Biotech Co Ltd Beijing 100000 Peoples R China Cent South Univ Xiangya Hosp Dept Clin Pharmacol Changsha 410000 Hunan Peoples R China

Background: Whole-genome sequencing (WGS) plays an increasingly important role in clinical practice and public health. Due to the big data size, WGS data analysis is usually compute-intensive and IO-intensive. Currently it usually takes 30 to 40 h to finish a 50x WGS analysis task, which is far from the ideal speed required by the industry. Furthermore, the high-end infrastructure required by WGS computing is costly in terms of time and money. In this paper, we aim to improve the time efficiency of WGS analysis and minimize the cost by elastic cloud computing. Results: We developed a distributed system, GT-WGS, for large-scale WGS analyses utilizing the Amazon Web Services (AWS). Our system won the first prize on the Wind and Cloud challenge held by Genomics and Cloud Technology Alliance conference (GCTA) committee. The system makes full use of the dynamic pricing mechanism of AWS. We evaluate the performance of GT-WGS with a 55x WGS dataset (400GB fastq) provided by the GCTA 2017 competition. In the best case, it only took 18.4 min to finish the analysis and the AWS cost of the whole process is only 16.5 US dollars. The accuracy of GT-WGS is 99.9% consistent with that of the Genome Analysis Toolkit (GATK) best practice. We also evaluated the performance of GT-WGS performance on a real-world dataset provided by the XiangYa hospital, which consists of 5x whole-genome dataset with 500 samples, and on average GT-WGS managed to finish one 5x WGS analysis task in 2.4 min at a cost of $3.6. Conclusions: WGS is already playing an important role in guiding therapeutic intervention. However, its application is limited by the time cost and computing cost. GT-WGS excelled as an efficient and affordable WGS analyses tool to address this problem. The demo video and supplementary materials of GT-WGS can be accessed at https://***/Genetalks/wgs_analysis_demo.

关键词： Whole-genome sequencing AWS parallel and distributed computing

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Engineering high-performance legacy codes as CORBA components for problem-solving environments

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JOURNAL OF parallel and distributed computing 2003年第11期63卷 1152-1163页

作者： Li, M Walker, DW Rana, OF Huang, Y Williams, PT Ward, RC Brunel Univ Dept Elect & Comp Engn Uxbridge UB8 3PH Middx England Cardiff Univ Sch Comp Sci Cardiff CF24 3XF S Glam Wales Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37831 USA

This paper describes techniques used to leverage high-performance legacy codes as CORBA components to a distributed problem-solving environment. It first briefly introduces the software architecture adopted by the environment. Then it presents a CORBA oriented wrapper generator (COWG) which can be used to automatically wrap high-performance legacy codes as CORBA components. Two legacy codes have been wrapped with COWG. One is an MPI-based molecular dynamic simulation (MDS) code, the other is a finite element-based computational fluid dynamics (CFD) code for simulating incompressible Navier-Stokes flows. Performance comparisons between runs of the MDS CORBA component and the original MDS legacy code on a cluster of workstations and on a parallel computer are also presented. Wrapped as CORBA components, these legacy codes can be reused in a distributed computing environment. The first case shows that high-performance can be maintained with the wrapped MDS component. The second case shows that a Web user can submit a task to the wrapped CFD component through a Web page without knowing the exact implementation of the component. In this way, a user's desktop computing environment can be extended to a high-performance computing environment using a cluster of workstations or a parallel computer. (C) 2003 Elsevier Inc. All rights reserved.

关键词： problem-solving environments high-performance legacy codes CORBA components wrapper generator parallel and distributed computing

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NBC: An Efficient Hierarchical Clustering Algorithm for Large Datasets

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INTERNATIONAL JOURNAL OF SEMANTIC computing 2015年第3期9卷 307-331页

作者： Zhang, Wei Zhang, Gongxuan Wang, Yongli Zhu, Zhaomeng Li, Tao Nanjing Univ Sci & Technol Sch Comp Sci & Engn 200 XiaoLingWei Rd Nanjing 210094 Jiangsu Peoples R China

Nearest neighbor search is a key technique used in hierarchical clustering and its computing complexity decides the performance of the hierarchical clustering algorithm. The time complexity of standard agglomerative hierarchical clustering is O(n(3)), while the time complexity of more advanced hierarchical clustering algorithms (such as nearest neighbor chain, SLINK and CLINK) is O(n(2)). This paper presents a new nearest neighbor search method called nearest neighbor boundary (NNB), which first divides a large dataset into independent subset and then finds nearest neighbor of each point in subset. When NNB is used, the time complexity of hierarchical clustering can be reduced to O(n log(2)n). Based on NNB, we propose a fast hierarchical clustering algorithm called nearest-neighbor boundary clustering (NBC), and the proposed algorithm can be adapted to the parallel and distributed computing framework. The experimental results demonstrate that our algorithm is practical for large datasets.

关键词： Hierarchical clustering nearest neighbor boundary parallel and distributed computing MapReduce

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parallel Particle Swarm Optimization for Reactive Power and Voltage Control Verifying Dependability

Parallel Particle Swarm Optimization for Reactive Power and ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Fukuyama, Yoshikazu Meiji Univ Dept Network Design Sch Interdisciplinary Math Sci Tokyo Japan

ISBN: (纸本)9781479974924

This paper presents a parallel particle swarm optimization (PSO) for reactive power and voltage control (Volt/Var Control: VVC) in electric power systems verifying dependability of the control. Considering high penetration of renewable energies and deregulation of power systems, electric power flows can change suddenly and operators in control centers have to control voltage in wider power systems. Therefore, VVC is required to shorten the control interval and handle larger-scale power systems. One of the solutions for this problem is applications of parallel and distributed computing. Since electric power systems is one of the infrastructures of social community, sustainable voltage control is crucial and dependability is strongly required for VVC. This paper investigates not only fast computation by parallel computation but also dependability of parallel PSO for VVC. The results are meaningful for practical parallel computation by PSO in actual VVC operations.

关键词： parallel and distributed computing particle swarm optimization dependability reactive power and voltage control

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LuNA Fragmented Programming System, Main Functions and Peculiarities of Run-Time Subsystem

LuNA Fragmented Programming System, Main Functions and Pecul...

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11th International Conference on parallel computing Technologies

作者： Malyshkin, Victor E. Perepelkin, Vladislav A. Russian Acad Sci Inst Computat Math & Math Geophys Novosibirsk Russia

The peculiarities of the LuNA run-time subsystem implementation are considered. LuNA is the language and system of fragmented programming. The peculiarities are conditioned by the properties of numerical algorithms, t... 详细信息

ISBN: (纸本)9783642231773

关键词： run-time parallel and distributed computing parallel programming language and system model of parallel program

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