• Title, Summary, Keyword: Parallel Computing

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Parallel damage detection through finite frequency changes on multicore processors

  • Messina, Arcangelo;Cafaro, Massimo
    • Structural Engineering and Mechanics
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    • v.63 no.4
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    • pp.457-469
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    • 2017
  • This manuscript deals with a novel approach aimed at identifying multiple damaged sites in structural components through finite frequency changes. Natural frequencies, meant as a privileged set of modal data, are adopted along with a numerical model of the system. The adoption of finite changes efficiently allows challenging characteristic problems encountered in damage detection techniques such as unexpected comparison of possible shifted modes and the significance of modal data changes very often affected by experimental/environmental noise. The new procedure extends MDLAC and exploits parallel computing on modern multicore processors. Smart filters, aimed at reducing the potential damaged sites, are implemented in order to reduce the computational effort. Several use cases are presented in order to illustrate the potentiality of the new damage detection procedure.

Infrastructure of Grid-based Distributed Remotely Sensed Images Processing Environment and its Parallel Intelligence Algorithms

  • ZHENG, Jiang;LUO, Jian-Cheng;Hu, Cheng;CHEN, Qiu-Xiao
    • Proceedings of the KSRS Conference
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    • pp.1284-1286
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    • 2003
  • There is a growing demand on remotely sensed and GIS data services in modern society. However, conventional WEB applications based on client/server pattern can not meet the criteria in the future . Grid computing provides a promising resolution for establishing spatial information system toward future applications. Here, a new architecture of the distributed environment for remotely sensed data processing based on the middleware technology was proposed. In addition, in order to utilize the new environment, a problem had to be algorithmically expressed as comprising a set of concurrently executing sub-problems or tasks. Experiment of the algorithm was implemented, and the results show that the new environmental can achieve high speedups for applications compared with conventional implementation.

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Thread-Level Parallelism using Java Thread and Network Resources (자바 스레드와 네트워크 자원을 이용한 병렬처리)

  • Kim, Tae-Yong
    • The Journal of Advanced Navigation Technology
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    • v.14 no.6
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    • pp.984-989
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    • 2010
  • In this paper, parallel programming technique by using Java Thread is introduced so as to develop parallel design tool to analyze the small micro flow sensor. To estimate computing time for Thread-level parallelism, the performances of two experimental models for potential problem subject to Thermal transfer equation are examined. As a result, if the number of network PC is increase, computing time for parallelism on network environment is enhanced to be almost n times. The micro sensor design tool based on distributed computing can be utilized to analyze a large scale problem.

Optimal Design of Permanent Magnet Wind Generator for Maximum Annual Energy Production (최대 연간 에너지 생산을 위한 영구자석형 풍력발전기의 최적설계)

  • Jung, Ho-Chang;Jung, Sang-Yong;Hahn, Sung-Chin;Lee, Cheol-Gyun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.56 no.12
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    • pp.2109-2115
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    • 2007
  • The wind generators have been installed with high output power to increase the energy production and efficiency. Hence, Optimal design of the direct-driven PM wind generator, coupled with F.E.M(Finite Element Method) and Genetic Algorithm(GA), has been performed to maximize the Annual Energy Production(AEP) over the whole wind speed characterized by the statistical model of wind speed distribution. Particularly, the parallel computing via internet web service has been applied to loose excessive computing times for optimization. The results of the optimal design of Surface-Mounted Permanent Magnet Synchronous Generator(SPMSG) are compared with each other candidates to verify the usefulness of the maximizing AEP model.

CMOS-Memristor Hybrid 4-bit Multiplier Circuit for Energy-Efficient Computing

  • Vo, Huan Minh;Truong, Son Ngoc;Shin, Sanghak;Min, Kyeong-Sik
    • Journal of IKEEE
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    • v.18 no.2
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    • pp.228-233
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    • 2014
  • In this paper, we propose a CMOS-memristor hybrid circuit that can perform 4-bit multiplication for future energy-efficient computing in nano-scale digital systems. The proposed CMOS-memristor hybrid circuit is based on the parallel architecture with AND and OR planes. This parallel architecture can be very useful in improving the power-delay product of the proposed circuit compared to the conventional CMOS array multiplier. Particularly, from the SPECTRE simulation of the proposed hybrid circuit with 0.13-mm CMOS devices and memristors, this proposed multiplier is estimated to have better power-delay product by 48% compared to the conventional CMOS array multiplier. In addition to this improvement in energy efficiency, this 4-bit multiplier circuit can occupy smaller area than the conventional array multiplier, because each cross-point memristor can be made only as small as $4F^2$.

A PRICING METHOD OF HYBRID DLS WITH GPGPU

  • YOON, YEOCHANG;KIM, YONSIK;BAE, HYEONG-OHK
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.20 no.4
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    • pp.277-293
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    • 2016
  • We develop an efficient numerical method for pricing the Derivative Linked Securities (DLS). The payoff structure of the hybrid DLS consists with a standard 2-Star step-down type ELS and the range accrual product which depends on the number of days in the coupon period that the index stay within the pre-determined range. We assume that the 2-dimensional Geometric Brownian Motion (GBM) as the model of two equities and a no-arbitrage interest model (One-factor Hull and White interest rate model) as a model for the interest rate. In this study, we employ the Monte Carlo simulation method with the Compute Unified Device Architecture (CUDA) parallel computing as the General Purpose computing on Graphic Processing Unit (GPGPU) technology for fast and efficient numerical valuation of DLS. Comparing the Monte Carlo method with single CPU computation or MPI implementation, the result of Monte Carlo simulation with CUDA parallel computing produces higher performance.

Analytical fragility curves of a structure subject to tsunami waves using smooth particle hydrodynamics

  • Sihombing, Fritz;Torbol, Marco
    • Smart Structures and Systems
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    • v.18 no.6
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    • pp.1145-1167
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    • 2016
  • This study presents a new method to computes analytical fragility curves of a structure subject to tsunami waves. The method uses dynamic analysis at each stage of the computation. First, the smooth particle hydrodynamics (SPH) model simulates the propagation of the tsunami waves from shallow water to their impact on the target structure. The advantage of SPH over mesh based methods is its capability to model wave surface interaction when large deformations are involved, such as the impact of water on a structure. Although SPH is computationally more expensive than mesh based method, nowadays the advent of parallel computing on general purpose graphic processing unit overcome this limitation. Then, the impact force is applied to a finite element model of the structure and its dynamic non-linear response is computed. When a data-set of tsunami waves is used analytical fragility curves can be computed. This study proves it is possible to obtain the response of a structure to a tsunami wave using state of the art dynamic models in every stage of the computation at an affordable cost.

A Study on Distributed System Construction and Numerical Calculation Using Raspberry Pi

  • Ko, Young-ho;Heo, Gyu-Seong;Lee, Sang-Hyun
    • International journal of advanced smart convergence
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    • v.8 no.4
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    • pp.194-199
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    • 2019
  • As the performance of the system increases, more parallelized data is being processed than single processing of data. Today's cpu structure has been developed to leverage multicore, and hence data processing methods are being developed to enable parallel processing. In recent years desktop cpu has increased multicore, data is growing exponentially, and there is also a growing need for data processing as artificial intelligence develops. This neural network of artificial intelligence consists of a matrix, making it advantageous for parallel processing. This paper aims to speed up the processing of the system by using raspberrypi to implement the cluster building and parallel processing system against the backdrop of the foregoing discussion. Raspberrypi is a credit card-sized single computer made by the raspberrypi Foundation in England, developed for education in schools and developing countries. It is cheap and easy to get the information you need because many people use it. Distributed processing systems should be supported by programs that connected multiple computers in parallel and operate on a built-in system. RaspberryPi is connected to switchhub, each connected raspberrypi communicates using the internal network, and internally implements parallel processing using the Message Passing Interface (MPI). Parallel processing programs can be programmed in python and can also use C or Fortran. The system was tested for parallel processing as a result of multiplying the two-dimensional arrangement of 10000 size by 0.1. Tests have shown a reduction in computational time and that parallelism can be reduced to the maximum number of cores in the system. The systems in this paper are manufactured on a Linux-based single computer and are thought to require testing on systems in different environments.

Real-Time IoT Big-data Processing for Stream Reasoning (스트림-리즈닝을 위한 실시간 사물인터넷 빅-데이터 처리)

  • Yun, Chang Ho;Park, Jong Won;Jung, Hae Sun;Lee, Yong Woo
    • Journal of Internet Computing and Services
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    • v.18 no.3
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    • pp.1-9
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    • 2017
  • Smart Cities intelligently manage numerous infrastructures, including Smart-City IoT devices, and provide a variety of smart-city applications to citizen. In order to provide various information needed for smart-city applications, Smart Cities require a function to intelligently process large-scale streamed big data that are constantly generated from a large number of IoT devices. To provide smart services in Smart-City, the Smart-City Consortium uses stream reasoning. Our stream reasoning requires real-time processing of big data. However, there are limitations associated with real-time processing of large-scale streamed big data in Smart Cities. In this paper, we introduce one of our researches on cloud computing based real-time distributed-parallel-processing to be used in stream-reasoning of IoT big data in Smart Cities. The Smart-City Consortium introduced its previously developed smart-city middleware. In the research for this paper, we made cloud computing based real-time distributed-parallel-processing available in the cloud computing platform of the smart-city middleware developed in the previous research, so that we can perform real-time distributed-parallel-processing with them. This paper introduces a real-time distributed-parallel-processing method and system for stream reasoning with IoT big data transmitted from various sensors of Smart Cities and evaluate the performance of real-time distributed-parallel-processing of the system where the method is implemented.

Evaluating Computational Efficiency of Spatial Analysis in Cloud Computing Platforms (클라우드 컴퓨팅 기반 공간분석의 연산 효율성 분석)

  • CHOI, Changlock;KIM, Yelin;HONG, Seong-Yun
    • Journal of the Korean Association of Geographic Information Studies
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    • v.21 no.4
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    • pp.119-131
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    • 2018
  • The increase of high-resolution spatial data and methodological developments in recent years has enabled a detailed analysis of individual experiences in space and over time. However, despite the increasing availability of data and technological advances, such individual-level analysis is not always possible in practice because of its computing requirements. To overcome this limitation, there has been a considerable amount of research on the use of high-performance, public cloud computing platforms for spatial analysis and simulation. The purpose of this paper is to empirically evaluate the efficiency and effectiveness of spatial analysis in cloud computing platforms. We compare the computing speed for calculating the measure of spatial autocorrelation and performing geographically weighted regression analysis between a local machine and spot instances on clouds. The results indicate that there could be significant improvements in terms of computing time when the analysis is performed parallel on clouds.