• 제목/요약/키워드: Parallel Virtual Machine

검색결과 33건 처리시간 0.028초

초고속 Myrinet 통신망에서의 PVM 성능 개선 (PVM Performance Enhancement over a High-Speed Myrinet)

  • 김인수;심재홍;최경희;정기현;문경덕;김태근
    • 한국정보처리학회논문지
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    • 제7권1호
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    • pp.74-87
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    • 2000
  • 본 논문에서는 초고속 통신망인 Myrinet을 기반으로 하는 워크스테이션 연동(network of workstations) 환경하에서 병렬 프로그래밍 모델을 지원하는 PVM (parallel virtual machine)의 통신 성능 개선 방안을 제시한다. PVM을 위해 새로이 제안하는 Myrinet 기반 통신 모델은 커널을 경유하는 기존의 UDP/IP 프로토콜을 이용하는 통신 모델과 Myrinet API를 직접 이용하는 통신 모델을 혼합한 복합 통신 모델이다. 제안된 복합 통신 모델은 사용자 영역(커널)에서 커널(사용자) 버퍼로의 메시지 복사 부하를 제거하고 커널내의 프로토콜 스택 처리를 위한 통신 지연 요인을 감소시키므로, Myrinet상에서 보다 빠른 PVM 테스크간의 데이터 전송 속도를 보장한다. 또한, UDP/IP를 사용하는 기존 PVM에 본 논문에서 제안된 Myrinet 기반 복합 통신 모델을 적용시킨 EPVM (Enhanced PVM)을 구현하고, 이의 성능을 측정하였다. 실험 결과 EPVM의 통신 성능이 기존 PVM 보다 평균 1.5배 정보 우수하다는 것을 확인하였다.

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클러스터링 컴퓨터 시스템을 이용한 병렬화 유전자 알고리즘의 효율성 증대에 대한 연구 (A Study for Improvement Effect of Paralleled Genetic Algorithm by Using Clustering Computer System)

  • 이원창;성활경;백영종
    • 한국공작기계학회:학술대회논문집
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    • 한국공작기계학회 2004년도 춘계학술대회 논문집
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    • pp.430-438
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    • 2004
  • Among the optimization method, GA (genetic algorithm) is a very powerful searching method enough to compete with design sensitivity analysis method. GA is very easy to apply, since it dose not require any design sensitivity information. However, GA has been computationally not efficient due to huge repetitive computation. In this study, parallel computation is adopted to Improve computational efficiency, Paralleled GA is introduced on a clustered LINUX based personal computer system.

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유한요소망에서의 효율적인 직접해법 병렬계산에 관한 연구 (Study of Efficient Parallel Computation of Cholesky's Method in FE Mesh)

  • 이향범;최경;김형중;정현교;한송엽
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1996년도 하계학술대회 논문집 A
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    • pp.68-70
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    • 1996
  • In this paper, an efficient parallel computation method for solving large sparse systems of linear algebraic equations by using Cholesky's method in the finite element method is studied. The methods of minimizing the number of fill-ins in the factorization process of factorization are investigated for minimizing the amount of memory and computation time. The parallel programming is implemented under the PVM(Parallel Virtual Machine) environment. The method of load-distribution is studied for minimizing the computation time and the communication time.

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A Methodology for Task placement and Scheduling Based on Virtual Machines

  • Chen, Xiaojun;Zhang, Jing;Li, Junhuai
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제5권9호
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    • pp.1544-1572
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    • 2011
  • Task placement and scheduling are traditionally studied in following aspects: resource utilization, application throughput, application execution latency and starvation, and recently, the studies are more on application scalability and application performance. A methodology for task placement and scheduling centered on tasks based on virtual machines is studied in this paper to improve the performances of systems and dynamic adaptability in applications development and deployment oriented parallel computing. For parallel applications with no real-time constraints, we describe a thought of feature model and make a formal description for four layers of task placement and scheduling. To place the tasks to different layers of virtual computing systems, we take the performances of four layers as the goal function in the model of task placement and scheduling. Furthermore, we take the personal preference, the application scalability for a designer in his (her) development and deployment, as the constraint of this model. The workflow of task placement and scheduling based on virtual machines has been discussed. Then, an algorithm TPVM is designed to work out the optimal scheme of the model, and an algorithm TEVM completes the execution of tasks in four layers. The experiments have been performed to validate the effectiveness of time estimated method and the feasibility and rationality of algorithms. It is seen from the experiments that our algorithms are better than other four algorithms in performance. The results show that the methodology presented in this paper has guiding significance to improve the efficiency of virtual computing systems.

Zero-copy 기술을 이용한 PVM의 성능 개선 (Performance Improvement for PVM by Zero-copy Mechanism)

  • 임성택;심재홍;최경희;정기현;김재훈;문성근
    • 한국통신학회논문지
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    • 제25권5B호
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    • pp.899-912
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    • 2000
  • PVM(Parallel Virtual Machine)은 네트워크 상에 분산되어 있는 여러 시스템들을 투명하게 활용하여 사용자에 고성능 병렬 컴퓨팅을 지원하는 단일 가상 시스템(single virtual system)으로 인식되게 하는 middle-ware 소프트웨어이다. 초고속 통신망을 기반으로 하는 PVM에서는 임의의 태스크로부터 하나의 메시지를 전송하기 위해 세 번의 메시지 복사가 필요하며, 이는 PVM의 성능 저하를 가져오는 주요 원인이 된다. 이러한 문제점을 개선하기 위해 이 논문에서는 zero-copy PVM 통신 모델을 제안한다. 제안된 모델은 PVM 태스크, PVM 데몬, 네트워크 인터페이스 보드 등에 의해 동시 접근이 가능한 전역 공유 메모리(global shared memory)를 이용하며, 초고속 통신망을 기반으로 하고 있다. 이 모델에서 PVM 태스크는 전송하고자 하는 메시지를 전역 공유 메모리에 저장하고, 메시지를 보낼 준비가 되었음을 PVM 데몬에게 알리며, 데몬은 해당 메시지를 커널을 통하지 않고 바로 초고속 통신망으로 전송함으로써, 메시지의 메모리 복사 횟수를 감소 시킨다. 실험 결과 두 시스템간의 메시지 왕복 시간은 제안된 모델을 사용함으로써 현저히 줄어 들었음을 확인하였다.

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부구조법에 의한 영역 분할 및 강소성 유한요소해석의 병렬 계산 (Domain Decomposition using Substructuring Method and Parallel Computation of the Rigid-Plastic Finite Element Analysis)

  • 박근;양동열
    • 소성∙가공
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    • 제7권5호
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    • pp.474-480
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    • 1998
  • In the present study a domain decomposition scheme using the substructuring method is developed for the computational efficiency of the finite element analysis of metal forming processes. in order to avoid calculation of an inverse matrix during the substructuring procedure, the modified Cholesky decomposition method is implemented. As obtaining the data independence by the substructuring method the program is easily paralleized using the Parallel Virtual machine(PVM) library on a work-station cluster connected on networks. A numerical example for a simple upsetting is calculated and the speed-up ratio with respect to various number of subdomains and number of processors. The efficiency of the parallel computation is discussed by comparing the results.

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다중블록 유동해석에서 병렬처리를 위한 시스템의 구조 (A framework for parallel processing in multiblock flow computations)

  • 박상근;이건우
    • 대한기계학회논문집B
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    • 제21권8호
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    • pp.1024-1033
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    • 1997
  • The past several years have witnessed an ever-increasing acceptance and adoption of parallel processing, both for high performance scientific computing as well as for more general purpose applications. Furthermore with increasing needs to perform the complex flow calculations in an efficient manner, the use of the message passing model on distributed networks has emerged as an important alternative to the expensive supercomputers. This work attempts to provide a generic framework to enable the parallelization of all CFD-related works using the master-slave model. This framework consists of (1) input geometry, (2) domain decomposition, (3) grid generation, (4) flow computations, (5) flow visualization, and (6) output display as the sequential components, but performs computations for (2) to (5) in parallel on the workstation clustering. The flow computations are parallized by having multiple copies of the flow-code to solve a PDE on different spatial regions on different processors, while their flow data are exchanged across the region boundaries, and the solution is time-stepped. The Parallel Virtual Machine (PVM) is used for distributed communication in this work.

분기 함수를 적용한 분산 최근접 휴리스틱 (A Distributed Nearest Neighbor Heuristic with Bounding Function)

  • 김정숙
    • 한국정보과학회논문지:시스템및이론
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    • 제29권7호
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    • pp.377-383
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    • 2002
  • 외판원 문제는 잘 알려진 NP-완전 문제로, 최적해(optimal value)를 구하는 다양한 알고리즘들이 개발되었다. 그러나 최악의 경우 지수 시간이 걸리므로 수행시간을 줄이는 다양한 방법들이 제안되고 있다. 최근접 휴리스틱 알고리즘은 최적해를 구하는 다른 알고리즘들에 비해 구조가 비교적 간단하다. 따라서 본 논문에서는 외판원 문제(Traveling Salesman Problem, TSP)의 최적해를 구할 수 있는 분기 함수(bounding function)를 적용한 분산 최근접 휴리스틱(nearest neighbor heuristic) 알고리즘을 PVM(Parallel Virtual Machine)에서 제공하는 마스터/슬래이브(master/slave) 모델을 사용하여 설계하고 구현하였다. 먼저 최적해를 찾는 수행 시간을 줄이기 위해 최적화 문제에서 좋은 성능을 보이는 분산 유전 알고리즘(distributed genetic algorithm)을 수행해 얻은 근사해(near optimal)를 초기 분기 함수로 사용한다. 특히 더욱 좋은 근사해를 구하고자 유전 연산자인 돌연변이를 새롭게 변형하여 적용하였다.

부구조법에 의한 영역 분할 및 강소성 유한요소해석의 병렬 계산 (Domain Decomposition using Substructuring Method and Parallel Comptation of the Rigid-Plastic Finite Element Analysis)

  • 박근;양동열
    • 한국소성가공학회:학술대회논문집
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    • 한국소성가공학회 1998년도 춘계학술대회논문집
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    • pp.246-249
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    • 1998
  • In the present study, domain decomposition using the substructuring method is developed for the computational efficiency of the finite element analysis of metal forming processes. In order to avoid calculation of an inverse matrix during the substructuring procedure, the modified Cholesky decomposition method is implemented. As obtaining the data independence by the substructuring method, the program is easily parallelized using the Parallel Virtual Machine(PVM) library on a workstation cluster connected on networks. A numerical example for a simple upsetting is calculated and the speed-up ratio with respect to various domain decompositions and number of processors. Comparing the results, it is concluded that the improvement of performance is obtained through the proposed method.

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Integration Architecture for Virtualized Naval Shipboard Computing Systems

  • Kim, Hongjae;Oh, Sangyoon
    • 정보화연구
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    • 제10권1호
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    • pp.1-11
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    • 2013
  • Various computing systems are used in naval ships. Since each system has a single purpose and its applications are tightly coupled with the physical machine, applications cannot share physical resources with each other. It is hard to utilize resources efficiently in conventional naval shipboard computing environment. In this paper, we present an integration architecture for virtualized naval shipboard computing systems based on open architecture. Our proposed architecture integrates individual computing resources into one single integrated hardware pool so that the OS and applications are encapsulated as a VM. We consider the issue of varying needs of all applications in a naval ship that have different purposes, priorities and requirements. We also present parallel VM migration algorithm that improves the process time of resource reallocation of given architecture. The evaluation results with the prototype system show that our algorithm performs better than conventional resource reallocation algorithm in process time.