• Journal of Advanced Marine Engineering and Technology
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• v.26 no.3
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• pp.313-319
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• 2002

Since the oil crisis in 1970, a great deal of effort has been made to develop automatic electric load sharing systems as a part of the efforts to save energy. A large scale electric generating system composes more than two generators whose characteristics may be different. When such a system is operated individually or in parallel, the lagrange multiplier's method has difficulty in achieving optimal load distribution because generators usually have the limitations of the operating range with inequality constraints. Therefore, a suitable operating mode of generators has to be decided according to the selection of the generators to meet electric power requirements at the minimum cost. In this study, a method which solves the optimal electric load distribution problem using the dynamic programming technique is proposed. This study also shows that the dynamic programming method has an advantage in dealing with the optimal load distribution problem under the limitations of the operating range with inequality constraints including generator operation mode. In this study, generator operating cost curve of second order equation by shop trial test results of diesel generators are used. The results indicate that the proposed method can be applied to the ship's electric generating system.

• Management Science and Financial Engineering
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• v.11 no.2
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• pp.19-43
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• 2005

This paper considers a variation of the customer order scheduling problem, and the variation is the case where the machine-job assignment is fixed. We examine the parallel machine environment, and the objective is to minimize the sum of the completion times of the batches. While a machine can process only one job at a time, different machines can simultaneously process different jobs in a batch. The recognition version of this problem is known to be NP-complete in the strong sense even if there exist only two parallel machines. When there are an arbitrary number of parallel machines, we establish three lower bounds and develop a dynamic programming (DP) algorithm which runs in exponential time on the number of batches. We present two simple but intuitive heuristics, SB and GR, and find some special cases where SB and GR generate an optimal schedule. We also find worst case upper bounds on the relative error. For the case of the two parallel machines, we show that GR generates an optimal schedule when processing times of all batches are equal. Finally, the heuristics and the lower bounds are empirically evaluated.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.11-17
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• 2021

In this paper, we demonstrate a performance boost by parallelizing a purely functional bisimulation algorithm on a multicore processor machine. The key idea of this parallelization is exploiting the referential transparency of purely functional programs to minimize refactoring of the original implementation without any parallel constructs. Both original and parallel implementations are written in Haskell, a purely functional programming language. The change from the original program to the parallel program is minuscule, maintaining almost original structure of the program. Through benchmark, we show that the proposed parallelization doubles the performance of the bisimulation test compared to the original non-parallel implementation. We also shaw that similar performance boost is also possible for a memoized version of the bisimulation implementation.

• Journal of the Korea Society for Simulation
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• v.7 no.1
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• pp.69-83
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• 1998

In this paper, we present the results of simulation by running parallel VHDL simulation on typical MPP(Massively Parallel Processor) systems such as IBM SP2 and SGI Origin 2000. Parallel simulation uses the synchronous protocol and parallel program is implemented using MPI(Message Passing Interface) based on message passing model, so that it can urn on any parallel programming environment which supports MPI, a standard communication library. And then GVT(Global Virtual Time) computation for parallel simulation is based on the global broadcasting with MPI＿Bcast(), which is a standard function in MPI and piggybacking. Our benchmark exhibits that as size of VHDL grows, the parallel simulation has a better performance compared with the sequential simulation. In addition, we also show the results of comparison between IBM SP2 and SGI Origin 2000 by applying the same application to those indirectly.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2008.09a
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• pp.40-43
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• 2008

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2008.09b
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• pp.40-43
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• 2008

• Journal of applied mathematics & informatics
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• v.8 no.2
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• pp.387-401
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• 2001

If the distance between two vertices becomes longer after the removal of a vertex u, then u is called a hinge vertex. In this paper, a linear time sequential algorithm is presented to find all hinge vertices of an interval graph. Also, a parallel algorithm is presented which takes O(n/P + log n) time using P processors on an EREW PRAM.

• Journal of applied mathematics & informatics
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• v.6 no.2
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• pp.649-659
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• 1999

This paper presents a parallel implementation of stiff-ness matrix calculation based on the processor farm model on a net-work of workstations running PVM programming environment. As the computational characteristics of stiffnes matrix exhibits good po-tentials for effective prallel computation the performance improve-ment is show to be almost linear with the number of sorkstations involved in the computation.

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.77-92
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• 2005

An efficient parallel algorithm is presented to find a maximum weight independent set of a permutation graph which takes O(log n) time using O($n^2$/ log n) processors on an EREW PRAM, provided the graph has at most O(n) maximal independent sets. The best known parallel algorithm takes O($log^2n$) time and O($n^3/log\;n$) processors on a CREW PRAM.

• KIISE Transactions on Computing Practices
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• v.23 no.8
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• pp.459-465
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• 2017

Although the performance of computer hardware is increasing due to the development of manycore technologies, software lacking a proportional increase in throughput. Functional languages can be a viable alternative to improve the performance of parallel programs since such languages have an inherent parallelism in evaluating pure expressions without side-effects. Specifically, Haskell is notably popular for parallel programming because it provides easy-to-use parallel constructs based on monads. However, the scalability of parallel programs in Haskell tends to fluctuate as the number of cores increases, and the garbage collector is suspected to be the source of this fluctuations because it affects both the space and the time needed to execute the programs. This paper uses the tuning tool, GC-Tune, to improve the scalability of the performance. Our experiment was conducted with a parallel plagiarism detection program, and the scalability improved. Specifically, the fluctuation range of the speedup was narrowed down by 39% compared to the original execution of the program without any tuning.