• Journal of Information Processing Systems
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• v.7 no.2
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• pp.271-286
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• 2011

In this paper, we propose efficient algorithms for parallel prefix computation and sorting on a recursive dual-net. The recursive dual-net $RDN^k$(B) for k > 0 has $(2n_o)^{2K}/2$ nodes and $d_0$ + k links per node, where $n_0$ and $d_0$ are the number of nod es and the node-degree of the base-network B, respectively. Assume that each node holds one data item, the communication and computation time complexities of the algorithm for parallel prefix computation on $RDN^k$(B), k > 0, are $2^{k+1}-2+2^kT_{comm}(0)$ and $2^{k+1}-2+2^kT_{comp}(0)$, respectively, where $T_{comm}(0)$ and $T_{comp}(0)$ are the communication and computation time complexities of the algorithm for parallel prefix computation on the base-network B, respectively. The algorithm for parallel sorting on $RDN^k$(B) is restricted on B = $Q_m$ where $Q_m$ is an m-cube. Assume that each node holds a single data item, the sorting algorithm runs in $O((m2^k)^2)$ computation steps and $O((km2^k)^2)$ communication steps, respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.3
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• pp.236-242
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• 2004

In this paper, we propose an approach to parallelize a load balancing algorithm that was shown to be very effective in distributing workload for parallel computations. Load balancing algorithms are required in executing parallel program efficiently As a parallel computation model, we used dynamically growing tree structure that can be found in many application problems. The load balancing algorithm tries to balance the workload among processors while keeping the communication cost under certain limit. We show how the load balancing algorithm is effectively parallelized on mesh and hypercube interconnection networks, and analyzed the time complexity for each case to show that parallel algorithm actually reduced the various overhead.

• Journal of the Korean GEO-environmental Society
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• v.25 no.4
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• pp.13-20
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• 2024

In this paper, a parallel analysis algorithm for Smoothed Particle Hydrodynamics (SPH), one of the numerical methods for fluidic materials, is introduced. SPH, which is a meshless method, can represent the behavior of a continuum using a particle-based approach, but it demands substantial computational resources. Therefore, parallel analysis algorithms are essential for SPH simulations. The domain decomposition algorithm, which divides the computational domain into partitions to be independently analyzed, is the most representative method among parallel analysis algorithms. In Discrete Element Method (DEM) and Molecular Dynamics (MD), the Cartesian coordinate-based domain decomposition method is popularly used because it offers advantages in quickly and conveniently accessing particle positions. However, in SPH, it is important to share particle information among partitioned domains because SPH particles are defined based on information from nearby particles within the smoothing length. Additionally, maintaining CPU load balance is crucial. In this study, a highly parallel efficient algorithm is proposed to dynamically minimize the size of orthogonal domain partitions to prevent excess CPU utilization. The efficiency of the proposed method was validated through numerical analysis models. The parallel efficiency of the proposed method is evaluated for up to 30 CPUs for fluidic models, achieving 90% parallel efficiency for up to 28 physical cores.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.44-55
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• 2009

Diagnosis of diseases using gene expression data obtained from microarray chip is an active research area recently. It has been done by general machine learning algorithms, because it is difficult to analyze directly. However, recent research results about the analysis based on the interaction between genes is essential for the gene expression analysis, which means the analysis using the traditional machine learning algorithms has limitations. In this paper, we classify the gene expression data using the hyper-network model that considers the higher-order correlations between the features, and then compares the classification accuracies. And also, we present the new hypo-network model that improve the disadvantage of existing model, and compare the processing performances of the existing hypo-network model based on general sequential processor and the improved hypo-network model implemented on parallel processors. In the experimental results, we show that the performance of our model shows improved and competitive classification performance than traditional machine learning methods, as well as, the existing hypo-network model. We show that the performance is maximized when the hypernetwork model is implemented on our parallel processors.

• Journal of Korean Society for Geospatial Information Science
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• v.7 no.2 s.14
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• pp.123-132
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• 1999

With the rapid increases of the amount of data and computing, the parallelization of the computing algorithm becomes necessary more than ever. However the parallelization had been conducted mostly in a super-computer until the rod 1990s, it was not for the general users due to the high price, the complexity of usage, and etc. A new concept for the parallel processing has been emerged in the form of K-clustering form the late 1990s, it becomes an excellent alternative for the applications need high computer power with a relative low cost although the installation and the usage are still difficult to the general users. The mapping algorithms (cut, join, resizing, warping, conversion from raster to vector and vice versa, etc) in GIS are well suited for the parallelization due to the characteristics of the data structure. If those algorithms are manipulated using PC-clustering, the result will be satisfiable in terms of cost and performance since they are processed in real flu with a low cos4 In this paper the tools and the libraries for the parallel processing and PC-clustering we introduced and how those tools and libraries are applied to mapping algorithms in GIS are showed. Parallel programs are developed for the mapping algorithms and the result of the experiments shows that the performance in most algorithms increases almost linearly according to the number of node.

• Convergence Security Journal
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• v.21 no.1
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• pp.209-219
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• 2021

The problem of congestion in the licensed radio channels spectrum can be solved by Cognitive Radio Networks (CRN). Several algorithms exist to ensure the rendezvous between Secondary Users (SUs), they are increasingly efficient, allowing faster rendezvous under multiple scenarios. In parallel, several jamming algorithms are developed to counter rendezvous which are also improving. The goal in CRN is to ensure the rendezvous by warding such jammers with robust algorithms. In this paper, we classify various jamming techniques and analyze the performance of various well-known rendezvous algorithms under jamming attacks.

• ETRI Journal
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• v.43 no.3
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• pp.483-510
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• 2021

In computational biology, desired patterns are searched in large text databases, and an exact match is preferable. Classical benchmark algorithms obtain competent solutions for pattern matching in O (N) time, whereas quantum algorithm design is based on Grover's method, which completes the search in $O(\sqrt{N})$ time. This paper briefly explains existing quantum algorithms and defines their processing limitations. Our initial work overcomes existing algorithmic constraints by proposing the quantum-based combined exact (QBCE) algorithm for the pattern-matching problem to process exact patterns. Next, quantum random access memory (QRAM) processing is discussed, and based on it, we propose the QRAM processing-based exact (QPBE) pattern-matching algorithm. We show that to find all t occurrences of a pattern, the best case time complexities of the QBCE and QPBE algorithms are $O(\sqrt{t})$ and $O(\sqrt{N})$, and the exceptional worst case is bounded by O (t) and O (N). Thus, the proposed quantum algorithms achieve computational speedup. Our work is proved mathematically and validated with simulation, and complexity analysis demonstrates that our quantum algorithms are better than existing pattern-matching methods.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2845-2854
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• 1998

Median filter can be implemented in the binary domain based on threshold decomposition, stacking property, and linear separability. In this paper, we develop one-dimensional and two-dimensional parallel algorithms for the median filter on a reconfigurable mesh with buses(RMESH) which is suitable for VLSI implementation. And we evaluate their performance by comparing the time complexities of RMESH algorithms with those of algorithms on mesh-connected computer. When the length of M-valued 1-D signal is N and w is the window width, the RMESH algorithm is done in O(Mw) time and mesh algorithm is done in $O(Mw^2)$ time. Beside, when the size of M-valued 2-D image is $N{\times}N$ and the window size is $w{\times}w$, our algorithm on $N{\times}N$ RMESH can be computed in O(Mw) time which is a significant improvement over the $O(Mw^2)$ complexity on $N{\times}N$ mesh.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.2
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• pp.41-50
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• 2003

In this paper, nonlinear parallel structural analyses are introduced by using the parallel multifrontal solver and damage localization for 2D and 3D crack models is presented as the application of nonlinear parallel computation. The parallel algorithms related with nonliear reduce the amount of memory used is carried out because many variables should be utilized for this highly nonlinear damage analysis. Also, Riks' continuation method is parallelized to search the solution when strain softening occurs due to damage evolution. For damage localization problem, several computational models having up to around 1-million degree of freedoms are used. The parallel performance in this nonlinear parallel algorithm is shown through these examples and the local variation of damage at crack tip is compared among the models with different degree of freedoms.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.6
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• pp.80-87
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• 2016

We propose a parallel mesh smoothing algorithm using graph coloring and OpenMP library for shared memory many core computer architectures. The proposed algorithm partitions a mesh into independent sets and performs a parallel mesh smoothing using OpenMP library. We study the effect of using various graph coloring and color reordering algorithms on the efficiency of performing the proposed parallel mesh smoothing algorithm. We also investigate the influence of using various OpenMP loop scheduling methods on the parallel mesh smoothing efficiency.