• Journal of the Korean Society for Precision Engineering
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• v.20 no.4
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• pp.189-196
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• 2003

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.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.7
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• pp.901-910
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• 1999

Investigated In this paper is the shock reflection from a blunt body, In particular, a circular cylinder of 20 mm diameter, for the weak shock impinging in the range 1<$M_s$ <2. Pressure and shock speed are measured for various shock strengths. Double-pulse holographic interferograms are taken to study the unsteady flow field at $M_s=1.34$. These experimental results are, in overall, well compared with the unstructured adaptive finite volume computation of the Euler equations performed in this study. Correlation of incident and reflected shocks and shock-shock locus obtained by experiment, computation, and theory are presented in parallel.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.323-328
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• 2008

This paper presents a Digital Signal Processor achieving high through-put for both decision intensive and computation intensive tasks. The proposed processor employees a multiplier, two ALU and load/store. Unit as operational units. Those four units are controlled and works parallel by superscalar control scheme, which is different from prior DSP architecture. The performance evaluation was done by implementing AC-3 decoding algorithm and 37.8% improvement was achieved. This study is valuable especially for the consumer electronics applications, which require very low cost.

• Journal of Radiation Industry
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• v.4 no.1
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• pp.33-38
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• 2010

The MCNP5 neutron, electron, photon Monte Carlo transport program was installed on the KISTI's SUN Tachyon computer using the parallel programming. Electron beam accelerators were modeled and shielding calculations were performed in order to investigate the reduction of computation time in the supercomputer environment. It was observed that a speedup of 40 to 80 of computation time can be obtained using 64 CPUs compared to an IBM PC.

• ETRI Journal
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• v.40 no.6
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• pp.788-793
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• 2018

3D reconstruction of urban architecture, land, and roads is an important part of building a "digital city." Unmanned aerial vehicles (UAVs) are gradually replacing other platforms, such as satellites and aircraft, in geographical image collection; the reason for this is not only lower cost and higher efficiency, but also higher data accuracy and a larger amount of obtained information. Recent 3D reconstruction algorithms have a high degree of automation, but their computation time is long and the reconstruction models may have many voids. This paper decomposes the object into multiple regional parallel reconstructions using the clustering principle, to reduce the computation time and improve the model quality. It is proposed to detect the planar area under low resolution, and then reduce the number of point clouds in the complex area.

• Journal of Institute of Convergence Technology
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• v.8 no.1
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• pp.15-20
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• 2018

Recently a GPU has acquired programmability to perform general purpose computation fast by running thousands of threads concurrently. This paper presents a parallel GPU computation algorithm for dense matrix-matrix addition and scalar multiplication using OpenGL compute shader. It can play a very important role as a fundamental building block for many high-performance computing applications. Experimental results on NVIDIA Quad 4000 show that the proposed algorithm runs 21 times faster than CPU algorithm and achieves performance of 16 GFLOPS in single precision for dense matrices with size 4,096. Such performance proves that our algorithm is practical for real applications.

• Coupled systems mechanics
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• v.12 no.3
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• pp.221-239
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• 2023

In our research, we have proposed a solid solution for aviation analysis which can ensure the asymptotic stability of coupled nonlinear plants, according to the theoretical solutions and demonstrated method. Because this solution employed the scheme of specific novel theorem of control, the controllers are artificially combined by the parallel distribution computation to have a feasible solution given the random coupled systems with aviation stability analysis. Therefore, we empathize and manually derive the results which shows the utilized lemma and criterion are believed effective and efficient for aircraft structural analysis of composite and nonlinear scenarios. To be fair, the experiment by numerical computation and calculations were explained the perfectness of the methodology we provided in the research.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.36D no.3
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• pp.48-58
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• 1999

In this paper, a new parallel computation method, which fully utilize the parallel processors both in mesh generation and FEM calculation for 2D/3D process simulation, is presented. High performance parallel FEM and parallel linear algebra solving technique was showed that excessive computational requirement of memory size and CPU time for the three-dimensional simulation could be treated successively. Our parallelized numerical solver successfully interpreted the transient enhanced diffusion (TED) phenomena of dopant diffusion and irregular shape of R-LOCOS within 15 minutes. Monte Carlo technique requires excessive computational requirement of CPU time. Therefore high performance parallel solving technique were employed to our cascade sputter simulation. The simulation results of Our sputter simulator allowed the calculation time of 520 sec and speedup of 25 using 30 processors. We found the optimized number of ion injection of our MC sputter simulation is 30,000.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.8
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• pp.375-387
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• 2005

This paper presents an application of parallel Genetic Algorithm-Tabu Search (GA-TS) algorithm to search an optimal solution of a service restoration in distribution systems. The main objective of service restoration of distribution systems is, when a fault or overload occurs, to restore as much load as possible by transferring the do-energized load in the out of service area via network reconfiguration to the appropriate adjacent feeders at minimum operational cost without violating operating constraints, which is a combinatorial optimization problem. This problem has many constraints with many local minima to solve the optimal switch position. This paper develops parallel GA-TS algorithm for service restoration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solutions of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper $10\%$ of the population to enhance the local searching capabilities. With migration operation, best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC cluster system consists of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through ethernet switch based fast ethernet. To show the validity of the proposed method, proposed algorithm has been tested with a practical distribution system in Korea. From the simulation results, we can find that the proposed algorithm is efficient for the distribution system service restoration in terms of the solution quality, speedup, efficiency and computation time.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.959-968
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• 1999

several papers have reported that parallel heuristics or hybrid approaches combining several heuristics can get better results. However, the parallelization and hybridization of any search methods on the single CPU type computer need enormous computation time. that case, we need more elegant combination method. For this purpose, we propose Fast Model of Parallel Heuristics(FMPH). FMPH is based on the island model of parallel genetic algorithms and takes local search to the elite solution obtained form each island(sub group). In this paper we introduce how can we adapt FMPH to the job-shop scheduling problem notorious as the most difficult NP-hard problem and report the excellent results of several famous benchmark problems.