• International Journal of Contents
• /
• v.1 no.1
• /
• pp.50-58
• /
• 2005

Generally, multidimensional data such as image and spatial data require large amount of storage space. There is a limit to store and manage those large amounts of data in single workstation. If we manage the data on parallel computing environment which is being actively researched these days, we can get highly improved performance. In this paper, we propose a parallel multidimensional index structure that exploits the parallelism of the parallel computing environment. The proposed index structure is nP(processor)-nxmD(disk) architecture which is the hybrid type of nP-nD and 1P-nD. Its node structure in-creases fan-out and reduces the height of an index. Also, a range search algorithm that maximizes I/O parallelism is devised, and it is applied to k-nearest neighbor queries. Through various experiments, it is shown that the proposed method outperforms other parallel index structures.

• Journal of applied mathematics & informatics
• /
• v.7 no.3
• /
• pp.1045-1058
• /
• 2000

In usual synchronous parallel computing, workload balance is a crucial factor to reduce idle times of some processors that have finished their jobs earlier than others. However, it is difficult to achieve on a heterogeneous workstation clusters where the available computing power of each processor is unpredictable. As a way to overcome such a problem, the idea of asynchronous methods has grown out and is being increasingly used and studied, but there is none for eigenvalue problems yet. In this paper, we suggest a new asynchronous method to solve some singular matrix problems, that can also be used for finding a certain eigenvector of some matrices.

• Structural Engineering and Mechanics
• /
• v.23 no.4
• /
• pp.325-337
• /
• 2006

A hybrid structural design and optimization methodology that combines the strengths of genetic algorithms, local search techniques, and parallel computing is developed to evolve optimal truss systems in this research effort. The primary objective that is met in evolving near-optimal or optimal structural systems using this approach is the capability of satisfying user-defined design criteria while minimizing the computational time required. The application of genetic algorithms to the design and optimization of truss systems supports conceptual design by facilitating the exploration of new design alternatives. In addition, final shape optimization of the evolved designs is supported through the refinement of member sizes using local search techniques for further improvement. The use of the hybrid approach, therefore, enhances the overall process of structural design. Parallel computing is implemented to reduce the total computation time required to obtain near-optimal designs. The support of human-computer interaction during layout optimization and local optimization is also discussed since it assists in evolving optimal truss systems that better satisfy a user's design requirements and design preferences.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.1
• /
• pp.100-106
• /
• 2017

In this paper, we propose a parallel optimization method of Aho-Corasick (AC) algorithm and Parallel Failureless Aho-Corasick (PFAC) algorithm using Open Computing Language (OpenCL) on Field Programmable Gate Array (FPGA). The low throughput of string matching engine causes the performance degradation of network process. Recently, many researchers have studied the string matching engine using parallel computing. FPGA's vendors offer a parallel computing platform using OpenCL. In this paper, we apply the AC and PFAC algorithm on DE1-SoC board with Cyclone V FPGA, where the optimization that considers FPGA architecture is performed. Experiments are performed considering global id, local id, local memory, and loop unrolling optimizations using PFAC algorithm. The performance improvement using loop unrolling is 129 times greater than AC algorithm that not adopt loop unrolling. The performance improvements using loop unrolling are 1.1, 0.2, and 1.5 times greater than those using global id, local id, and local memory optimizations mentioned above.

• The KIPS Transactions:PartA
• /
• v.13A no.2 s.99
• /
• pp.157-162
• /
• 2006

Recent High Performance Computing (HPC) platforms can be classified as Shared-Memory Multiprocessors (SMP), Massively Parallel Processors (MPP), and Clusters of computing nodes. These platforms are deployed in many scientific and engineering applications which require very high demand on computing power. In order to realize an optimal performance for these applications, it is crucial to find and use the suitable computing platforms and programming paradigms. In this paper, we use SPEC HPC 2002 benchmark suite developed in various parallel programming models (MPI, OpenMP, and hybrid of MPI/OpenMP) to find an optimal computing environments and programming paradigms for them through their performance analyses.

• Journal of KIISE:Computer Systems and Theory
• /
• v.31 no.7
• /
• pp.379-386
• /
• 2004

We designed a portable parallel library HPCL(High Performance Computing Library) with following objectives: (1) to provide a close relationship between the parallel code and the original sequential code that will help future versions of the sequential code and (2) to enhance performance of the parallel code. The library is an interface written in C and Fortran programming languages between MPI(Message Passing Interface) and parallel programs in Fortran. Performance results were determined on clusters of PC's and IBM SP4.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.39 no.4
• /
• pp.19-31
• /
• 2014

We study parallel processing techniques for the R programming language of high performance computing technology. In this study, we used massively parallel computing system which has 25,408 cpu cores. We conducted a performance evaluation of a distributed memory system using MPI and of a the shared memory system using OpenMP. Our findings are summarized as follows. First, For some particular algorithms, parallel processing is about 150 times faster than serial processing in R. Second, the distributed memory system gets faster as the number of nodes increases while shared memory system is limited in the improvement of performance, due to the limit of the number of cpus in a single system.

• Proceedings of the KSME Conference
• /
• 2003.11a
• /
• pp.928-933
• /
• 2003

For simulation of three-dimensional turbulent flow with LES and DNS takes much time and expense with current available computing resources. It is nearly impossible to simulate turbulent flow with high Reynolds number. So, the emerging alternative is the Grid computing for needed computation power and working environment. In this study, the CFD code was parallelized to adapt it for the parallel computing under the Grid environment. In the first place, the Grid environment was built to connect the PC-Cluster facilities belong to the different institutions using communication network system. And CFD applications were calculated to check the performance of the parallel code developed for the Grid environment. Although it is a fundamental study, it brings about a important meaning as first step in research of the Grid.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.4 no.4
• /
• pp.1-7
• /
• 2001

Remotely sensed data is the most fundamental data in acquiring the GIS informations, and may be analyzed to extract useful thematic information. Multi-spectral classification is one of the most often used methods of information extraction. The actual multi-spectral classification may be performed using either supervised or unsupervised approaches. This paper analyze the effect of assigning clever initial values to image classes on the performance of parallelepiped classification algorithm, which is one of the supervised classification algorithms. First, we investigate the effect on serial computing model, then expand it on MIMD(Multiple Instruction Multiple Data) parallel computing model. On serial computing model, the performance of the parallel pipe algorithm improved 2.4 times at most and, on MIMD parallel computing model the performance improved about 2.5 times as clever initial values are assigned to image class. Through computer simulation we find that initial values of image class greatly affect the performance of parallelepiped classification algorithms, and it can be improved greatly when classes on both serial computing model and MIMD parallel computation model.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.31 no.4
• /
• pp.16-25
• /
• 2003

A parallel computing environment based on Windows operating system was constructed and a performance test was mode in comparison with Linux based systems. The Windows 2000 cluster was composed with servers and clients connected by Fast-ethernet, within which two sub-clusters may operates together or separately. Compaq Visual Fortran complier and two MPI libraries, MPICH.NT.1.2.2 and NT-MPICHNT.1.2 were installed as computing tools. Parallel computing performance tests were carried out using two-dimensional preconditioned Navier-Stokes code to examine the dependency on the number of processors, problem size and MPI libraries, those were compared with results from Linux clusters. Results shows that a cluster based on the user-friendly Windows operating system is also useful for the parallel computing and has good performance comparable to the previous Linux clusters.