• Journal of KIISE:Computer Systems and Theory
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• v.27 no.5
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• pp.450-463
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• 2000

Array redistribution is usually required to enhance algorithm performance in many parallel programs on distributed memory multicomputers. GEN_BLOCK redistribution, which is redistribution between different GEN_BLOCKs, is essential for load balancing. However, prior research on redistribution has been focused on regular redistribution, such as redistribution between different CYCLIC(N)s. GEN_BLOCK redistribution is very different from regular redistribution. Message passing in regular redistribution involves repetitions of basic message passing patterns, while message passing for GEN_BLOCK redistribution shows locality. This paper proves that two optimal condition, reducing the number of communication steps and minimizing redistribution size, are essential in GEN_BLOCK redistribution. Additionally, by adding a relocation phase to list scheduling, we make an optimal scheduling algorithm for GEN_BLOCK redistribution. To evaluate the performance of the algorithm, we have performed experiments on a CRAY T3E. According to the experiments, it was proven that the scheduling algorithm shows better performance and that the conditions are critical in enhancing the communication speed of GEN_BLOCK redistribution.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.5
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• pp.361-367
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• 2014

In this paper, task scheduling and load balancing methods of multifrontal solution methods of finite element structural analysis in a modern multicore machine are introduced. Many structural analysis problems have generally irregular grid and many kinds of properties and materials. These irregularities and heterogeneities lead to bottleneck of parallelization and cause idle time to analysis. Therefore, task scheduling and load balancing are desired to reduce inefficiency. Several kinds of multithreaded parallelization methods are presented and comparison between static and dynamic task scheduling are shown. To reduce the idle time caused by irregular partitioned subdomains, computational load balancing methods, Balancing all tasks and minmax task pairing balancing, are invented. Theoretical and actual elapsed time are shown and the reason of their performance gap are discussed.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.673-674
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• 2011

정부의 녹색성장 정책에 의해 배전계통에 태양광, 풍력발전 등 분산전원의 설치가 급증함에 따라 기존의 배전계통의 조류의 방향 및 고장전류, 부하전류가 변화되어 배전계통의 보호협조 운용상에 여러 가지 문제점들이 발생할 가능성이 커지고 있다. 특히 분산전원연계시 임피던스 병렬화로 보호기기에 흐르는 사고전류가 감소하는 분류효과가 발생할 가능성이 있다. 따라서 본 논문에서는 분산전원이 연계된 경우 보호협조를 검토하기 위한 양방향 보호협조 평가프로그램을 이용하여 분산전원이 연계된 경우 계통에서 발생할 수 있는 문제점 중 분류 효과로 인한 보호기기의 부동작 사례를 시뮬레이션하고, 그 결과를 분석하여 본 프로그램의 유용성을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.55-58
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• 2016

스카이라인 질의란 다수의 선택지 중 '선호될 만한(preferable)' 선택지를 요청하는 질의이다. 사용자가 검토해야하는 선택지의 수를 대폭 감소시키는 스카이라인 질의는 데이터가 폭증하는 빅데이터 환경에서 매우 유용하게 활용된다. 이러한 배경에서 대용량 데이터에 대한 스카이라인 질의를 분산 병렬 처리하는 기법이 각광을 받고 있으며, 특히 맵리듀스(MapReduce) 기반의 분산 병렬 처리 기법 연구가 활발히 진행 중이다. 맵리듀스 기반 알고리즘의 병렬성 제고를 위해서는 부하 불균등 문제 중복 계산 문제 과다한 네트워크 비용 발생 문제를 해소해야 한다. 최근 각 기반 공간분할 기법을 사용하여 부하 불균등 문제와 중복 계산 문제를 해소하는 맵리듀스 기반 스카이라인 질의 처리 기법이 제안되었으나 해당 기법은 네트워크 비용 관점에서 최적화되어있지 않다. 본 논문에서는 부하 불균등 문제와 중복 계산 문제를 해소하면서도 프루닝을 통해 네트워크 비용 절감 시킬 수 있는 새로운 맵리듀스 기반 병렬 스카이라인 질의 처리 기법인 MR-SEAP(MapReduce sample Skyline object Equality Angular Partitioning)을 제안한다. MR-SEAP에서는 데이터를 샘플링하여 샘플 스카이라인 객체를 추출한 뒤 해당 객체들을 균등 분배하는 각도를 기준으로 공간을 분할하여 스카이라인 질의를 병렬 계산하되, 샘플 스카이라인을 이용하여 다수의 객체를 사전에 프루닝함으로써 네트워크 비용을 절감한다. 본 논문에서는 다양한 데이터 수량(cardinality) 및 분포(distribution)에 따른 제안 기법의 성능을 실험 평가함으로써 제안 기법의 우수성을 검증한다.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2836-2838
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• 1999

고신뢰도의 요구를 보장하는 병렬 구조의 분산시스템의 사용이 증가함에 따라 네트워크 상에서 메시지전달을 방해하는 요소고장의 영향을 최소화시킬 수 있는 고장포용 라우팅에 대한 중요성이 부각되고 있다. 그러나, 네트워크의 복잡한 환경 때문에 요소고장을 극복하기 위한 고장포용 라우팅 알고리즘의 설계는 쉬운 일이 아니다. 본 논문에서는 2차원 메쉬 네트워크에 적용되는 최적의 고장포용 라우팅 알고리즘을 설계하기 위하여 관련 응용분야에서 그 유용성이 검증된 유전 알고리즘을 이용한다. 제안된 알고리즘은 wormhole 라우팅 방식을 사용하며, 교착상태를 없애기 위하여 하나의 물리적 채널을 공유하는 4개의 가상채널을 사용한다. 마지막으로, 시뮬레이션을 통하여 제안된 알고리즘이 기존의 다른 고장포용 라우팅 알고리즘보다 우수함을 증명한다.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.6
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• pp.555-568
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• 2000

The World Wide Web has become the largest virtual system that is almost universal in scope. In recent research, it has become effective to utilize idle hosts existing in the World Wide Web for running applications that require a substantial amount of computation. This novel computing paradigm has been referred to as the advent of global computing. In this paper, we implement and propose a mobile object-based global computing framework called Tiger, whose primary goal is to present novel object-oriented programming libraries that support distribution, dispatching, migration of objects and concurrency among computational activities. The programming libraries provide programmers with access, location and migration transparency for distributed and mobile objects. Tiger's second goal is to provide a system supporting requisites for a global computing environment - scalability, resource and location management. The Tiger system and the programming libraries provided allow a programmer to easily develop an objectoriented parallel and distributed application using globally extended computing resources. We also present the improvement in performance gained by conducting the experiment with highly intensive computations such as parallel fractal image processing and genetic-neuro-fuzzy algorithms.

• The KIPS Transactions:PartD
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• v.18D no.2
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• pp.81-88
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• 2011

Sequential pattern mining that determines frequent patterns appearing in a given set of sequences is an important data mining problem with broad applications. For example, sequential pattern mining can find the web access patterns, customer's purchase patterns and DNA sequences related with specific disease. In this paper, we develop the sequential pattern mining algorithms using MapReduce framework. Our algorithms distribute input data to several machines and find frequent sequential patterns in parallel. With synthetic data sets, we did a comprehensive performance study with varying various parameters. Our experimental results show that linear speed up can be achieved through our algorithms with increasing the number of used machines.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.196-204
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• 2012

Recently, many applications perform OLAP(On-Line Analytical Processing) over a very large volume of data. Multidimensional data cube is regarded as a core tool in OLAP analysis. This paper focuses on the method how to efficiently compute data cubes in parallel by using a popular parallel processing tool, MapReduce. We investigate efficient ways to implement PipeSort algorithm, a well-known data cube computation method, on the MapReduce framework. The PipeSort executes several (descendant) cuboids at the same time as a pipeline by scanning one (ancestor) cuboid once, which have the same sorting order. This paper proposed four ways implementing the pipeline of the PipeSort on the MapReduce framework which runs across 20 servers. Our experiments show that PipeMap-NoReduce algorithm outperforms the rest algorithms for high-dimensional data. On the contrary, Post-Pipe stands out above the others for low-dimensional data.

• The Journal of Bigdata
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• v.7 no.1
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• pp.1-14
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• 2022

The proof-of-work consensus algorithm used by most blockchains is causing a massive waste of computing resources in the form of mining. A useful proof-of-work consensus algorithm has been studied to reduce the waste of computing resources in proof-of-work, but there are still resource waste and mining centralization problems when creating blocks. In this paper, the problem of resource waste in block generation was solved by replacing the relatively inefficient computation process for block generation with distributed artificial intelligence model learning. In addition, by providing fair rewards to nodes participating in the learning process, nodes with weak computing power were motivated to participate, and performance similar to the existing centralized AI learning method was maintained. To show the validity of the proposed methodology, we implemented a blockchain network capable of distributed AI learning and experimented with reward distribution through resource verification, and compared the results of the existing centralized learning method and the blockchain distributed AI learning method. In addition, as a future study, the thesis was concluded by suggesting problems and development directions that may occur when expanding the blockchain main network and artificial intelligence model.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.370-371
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• 2019

태양광 및 풍력과 같은 신 재생에너지원을 바탕으로 하는 분산 발전이 증가함에 따라 대용량의 PCS가 요구된다. 대용량의 PCS를 제어하기 위해서는 병렬운전이 바탕이 되어야 한다. 본 논문에서는 통신 없이 제어가 가능한 기존의 드룹 제어의 장점을 살리면서 전력 편차가 적은 마스터 슬레이브 제어의 장점을 결합한 병렬운전 제어 방식을 제안하고 시뮬레이션을 통해 제어 알고리즘의 타당성을 검증하고자 한다.