• Title, Summary, Keyword: Parallel Computing

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Design and optimization of steel trusses using genetic algorithms, parallel computing, and human-computer interaction

  • Agarwal, Pranab;Raich, Anne M.
    • Structural Engineering and Mechanics
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    • v.23 no.4
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    • pp.325-337
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    • 2006
  • A hybrid structural design and optimization methodology that combines the strengths of genetic algorithms, local search techniques, and parallel computing is developed to evolve optimal truss systems in this research effort. The primary objective that is met in evolving near-optimal or optimal structural systems using this approach is the capability of satisfying user-defined design criteria while minimizing the computational time required. The application of genetic algorithms to the design and optimization of truss systems supports conceptual design by facilitating the exploration of new design alternatives. In addition, final shape optimization of the evolved designs is supported through the refinement of member sizes using local search techniques for further improvement. The use of the hybrid approach, therefore, enhances the overall process of structural design. Parallel computing is implemented to reduce the total computation time required to obtain near-optimal designs. The support of human-computer interaction during layout optimization and local optimization is also discussed since it assists in evolving optimal truss systems that better satisfy a user's design requirements and design preferences.

Comparison of Distributed and Parallel NGS Data Analysis Methods based on Cloud Computing

  • Kang, Hyungil;Kim, Sangsoo
    • International Journal of Contents
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    • v.14 no.1
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    • pp.34-38
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    • 2018
  • With the rapid growth of genomic data, new requirements have emerged that are difficult to handle with big data storage and analysis techniques. Regardless of the size of an organization performing genomic data analysis, it is becoming increasingly difficult for an institution to build a computing environment for storing and analyzing genomic data. Recently, cloud computing has emerged as a computing environment that meets these new requirements. In this paper, we analyze and compare existing distributed and parallel NGS (Next Generation Sequencing) analysis based on cloud computing environment for future research.

Parallel String Matching and Optimization Using OpenCL on FPGA (FPGA 상에서 OpenCL을 이용한 병렬 문자열 매칭 구현과 최적화 방향)

  • Yoon, Jin Myung;Choi, Kang-Il;Kim, Hyun Jin
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.66 no.1
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    • pp.100-106
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    • 2017
  • In this paper, we propose a parallel optimization method of Aho-Corasick (AC) algorithm and Parallel Failureless Aho-Corasick (PFAC) algorithm using Open Computing Language (OpenCL) on Field Programmable Gate Array (FPGA). The low throughput of string matching engine causes the performance degradation of network process. Recently, many researchers have studied the string matching engine using parallel computing. FPGA's vendors offer a parallel computing platform using OpenCL. In this paper, we apply the AC and PFAC algorithm on DE1-SoC board with Cyclone V FPGA, where the optimization that considers FPGA architecture is performed. Experiments are performed considering global id, local id, local memory, and loop unrolling optimizations using PFAC algorithm. The performance improvement using loop unrolling is 129 times greater than AC algorithm that not adopt loop unrolling. The performance improvements using loop unrolling are 1.1, 0.2, and 1.5 times greater than those using global id, local id, and local memory optimizations mentioned above.

Comparative and Combined Performance Studies of OpenMP and MPI Codes (OpenMP와 MPI 코드의 상대적, 혼합적 성능 고찰)

  • Lee Myung-Ho
    • The KIPS Transactions:PartA
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    • v.13A no.2
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    • pp.157-162
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    • 2006
  • Recent High Performance Computing (HPC) platforms can be classified as Shared-Memory Multiprocessors (SMP), Massively Parallel Processors (MPP), and Clusters of computing nodes. These platforms are deployed in many scientific and engineering applications which require very high demand on computing power. In order to realize an optimal performance for these applications, it is crucial to find and use the suitable computing platforms and programming paradigms. In this paper, we use SPEC HPC 2002 benchmark suite developed in various parallel programming models (MPI, OpenMP, and hybrid of MPI/OpenMP) to find an optimal computing environments and programming paradigms for them through their performance analyses.

Efficient Checkpoint Algorithm for Message-Passing Parallel Applications on Cloud Computing (클라우드컴퓨팅에서 메시지패싱방식 응용프로그램의 효율적인 체크포인트 알고리즘)

  • Le, Duc Tai;Dao, Manh Thuong Quan;Ahn, Min-Joon;Choo, Hyun-Seung
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.156-157
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    • 2011
  • In this work, we study the checkpoint/restart problem for message-passing parallel applications running on cloud computing environment. This is a new direction which arises from the trend of enabling the applications to run on the cloud computing environment. The main objective is to propose an efficient checkpoint algorithm for message-passing parallel applications considering communications with external systems. We further implement the novel algorithm by modifying gSOAP and OpenMPI (the open source libraries) which support service calls and checkpoint message-passing parallel programs, especially. The simulation showed that additional costs to the executing and checkpointing application of the algorithm are negligible. Ultimately, the algorithm supports efficiently the checkpoint/restart service for message-passing parallel applications, that send requests to external services.

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Implementation and Performance Analysis of High Performance Computing Library for Parallel Processing (병렬처리를 위한 고성능 라이브러리의 구현과 성능 평가)

  • 김영태;이용권
    • Journal of KIISE:Computer Systems and Theory
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    • v.31 no.7
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    • pp.379-386
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    • 2004
  • We designed a portable parallel library HPCL(High Performance Computing Library) with following objectives: (1) to provide a close relationship between the parallel code and the original sequential code that will help future versions of the sequential code and (2) to enhance performance of the parallel code. The library is an interface written in C and Fortran programming languages between MPI(Message Passing Interface) and parallel programs in Fortran. Performance results were determined on clusters of PC's and IBM SP4.

Parallel Computing Environment for R with on Supercomputer Systems (빅데이터 분석을 위한 슈퍼컴퓨터 환경에서 R의 병렬처리)

  • Lee, Sang Yeol;Won, Joong Ho
    • Journal of the Korean Operations Research and Management Science Society
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    • v.39 no.4
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    • pp.19-31
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    • 2014
  • We study parallel processing techniques for the R programming language of high performance computing technology. In this study, we used massively parallel computing system which has 25,408 cpu cores. We conducted a performance evaluation of a distributed memory system using MPI and of a the shared memory system using OpenMP. Our findings are summarized as follows. First, For some particular algorithms, parallel processing is about 150 times faster than serial processing in R. Second, the distributed memory system gets faster as the number of nodes increases while shared memory system is limited in the improvement of performance, due to the limit of the number of cpus in a single system.

Stale Synchronous Parallel Model in Edge Computing Environment (Edge Computing 환경에서의 Stale Synchronous Parallel Model 연구)

  • Kim, Dong-Hyun;Lee, Byung-Jun;Kim, Kyung-Tae;Youn, Hee-Yong
    • Proceedings of the Korean Society of Computer Information Conference
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    • pp.89-92
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    • 2018
  • 본 논문에서는 Edge computing 환경에서 다수의 노드들로 구성된 네트워크의 디바이스를 효율적으로 관리하기 위한 방법을 제안한다. 기존의 클라이언트-서버 모델은 모든 데이터와 그에 대한 요청을 중심 서버에서 처리하기 때문에, 다수의 노드로부터 생성된 많은 양의 데이터를 처리하는 데 빠른 응답속도를 보장하지 못한다. Edge computing은 분담을 통해 네트워크의 부담을 줄일 수 있는 IoT 네트워크에 적합한 방법으로, 데이터를 전송하고 받는 과정에서 네트워크의 대역폭을 사용하는 대신 서로 연결된 노드들이 협력해서 데이터를 처리하고, 또한 네트워크 말단에서의 데이터 처리가 허용되어 데이터 센터의 부담을 줄일 수 있다. 여러병렬 기계학습 모델 중 본 연구에서는 Stale Synchronous Parallel(SSP) 모델을 이용하여 Edge 노드에서 분산기계 학습에 적용하였다.

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Parallel Computing on Intensity Offset Tracking Using Synthetic Aperture Radar for Retrieval of Glacier Velocity

  • Hong, Sang-Hoon
    • Korean Journal of Remote Sensing
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    • v.35 no.1
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    • pp.29-37
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    • 2019
  • Synthetic Aperture Radar (SAR) observations are powerful tools to monitor surface's displacement very accurately, induced by earthquake, volcano, ground subsidence, glacier movement, etc. Especially, radar interferometry (InSAR) which utilizes phase information related to distance from sensor to target, can generate displacement map in line-of-sight direction with accuracy of a few cm or mm. Due to decorrelation effect, however, degradation of coherence in the InSAR application often prohibit from construction of differential interferogram. Offset tracking method is an alternative approach to make a two-dimensional displacement map using intensity information instead of the phase. However, there is limitation in that the offset tracking requires very intensive computation power and time. In this paper, efficiency of parallel computing has been investigated using high performance computer for estimation of glacier velocity. Two TanDEM-X SAR observations which were acquired on September 15, 2013 and September 26, 2013 over the Narsap Sermia in Southwestern Greenland were collected. Atotal of 56 of 2.4 GHz Intel Xeon processors(28 physical processors with hyperthreading) by operating with linux environment were utilized. The Gamma software was used for application of offset tracking by adjustment of the number of processors for the OpenMP parallel computing. The processing times of the offset tracking at the 256 by 256 pixels of window patch size at single and 56 cores are; 26,344 sec and 2,055 sec, respectively. It is impressive that the processing time could be reduced significantly about thirteen times (12.81) at the 56 cores usage. However, the parallel computing using all the processors prevent other background operations or functions. Except the offset tracking processing, optimum number of processors need to be evaluated for computing efficiency.