• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.1-8
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• 2022

In this paper, we design and implement a GPU-based parallel algorithm to effectively solve the traveling salesman problem through an ant color system. The repetition process of generating hundreds or thousands of tours simultaneously in TSP utilizes GPU's task-level parallelism, and the update process of pheromone trails data actively exploits data parallelism by 32x32 thread blocks. In particular, through simultaneous memory access of multiple threads, the coalesced accesses on continuous memory addresses and concurrent accesses on shared memory are supported. This experiment used 127 to 1002 city data provided by TSPLIB, and compared the performance of sequential and parallel algorithms by using Intel Core i9-9900K CPU and Nvidia Titan RTX system. Performance improvement by GPU parallelization shows speedup of about 10.13 to 11.37 times.

• The Transactions of the Korea Information Processing Society
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• v.5 no.6
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• pp.1620-1632
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• 1998

This paper presents a fast parallel algorithm for volume rendering and its implementation using C language and MPI MasPar Programming Language) on the 4,096 processor MasPar MP-2 machine. This parallel algorithm is a parallelization hased on the Lacroute' s sequential shear - warp algorithm currently acknowledged to be the fastest sequential volume rendering algorithm. This algorithm reduces communication overheads by using the sheared space partition scheme and the load balancing technique using load estimates from the previous iteration, and the number of voxels to be processed by using the run-length encoded volume data structure.Actual performance is 3 to 4 frames/second on the human hrain scan dataset of $128\times128\times128$ voxels. Because of the scalability of this algorithm, performance of ]2-16 frames/sc.'cond is expected on the 16,384 processor MasPar MP-2 machine. It is expected that implementation on more current SIMD or MIMD architectures would provide 3O~60 frames/second on large volumes.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.10
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• pp.407-414
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• 2017

Pig monitoring applications consisting of many tasks can take advantage of inherent data parallelism and enable parallel processing using performance accelerators. In this paper, we propose a task distribution method for pig monitoring applications into a heterogenous computing platform consisting of a multicore-CPU and a manycore-GPU. That is, a parallel program written in OpenCL is developed, and then the most suitable processor is determined based on the measured execution time of each task. The proposed method is simple but very effective, and can be applied to parallelize other applications consisting of many tasks on a heterogeneous computing platform consisting of a CPU and a GPU. Experimental results show that the performance of the proposed task distribution method on three different heterogeneous computing platforms can improve the performance of the typical GPU-only method where every tasks are executed on a deviceGPU by a factor of 1.5, 8.7 and 2.7, respectively.

• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.9-16
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• 2014

This paper proposes a linear scratch detection and restoration algorithm using pixel data comparison in a single frame or consecutive frames. There exists a high parallelism in that a scratch detection and restoration algorithm needs a large amount of comparison operations. The proposed scratch detection and restoration algorithm is designed with a GPU for fast computation. We test the proposed algorithm in sequential and parallel processing with the set of digital videos in National Archive of Korea. In the experiments, the scratch detection rate of consecutive frames is as fast as about 20% for that of a single frame. The detection and restoration rates of a GPU-based algorithm are similar to those of a CPU-based algorithm, but the parallel implementation speeds up to about 50 times.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.141-149
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• 2013

This paper presents a high-speed intrusion detection process for multi-core video surveillance systems. The high-speed intrusion detection was designed to a parallel process. Based on the analysis of the conventional process, a parallel intrusion detection process was proposed so as to be accelerated by utilizing multiple processing cores in contemporary computing systems. The proposed process performs the intrusion detection in a per-frame parallel manner, considering the data dependency between frames. The proposed process was validated by implementing a multi-threaded intrusion detection program. For the system having eight processing cores, the detection speed of the proposed program is higher than that of the conventional one by up to 353.76% in terms of the frame rate.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.479-486
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• 2017

Recently, GPGPU has been able to increase the degradation of computer performance, and it is now possible to run physically based real-time simulations on PCs that require high computational complexity. Physical calculations applied in physics simulation can be performed by parallel processing, and can be efficiently performed using parallel computation using Compute shader recently supported by OpenGL 4.3 and Unity 4.0. In this paper, we measure and compare the number of performance in real - time physics simulation in OpenGL running on various platforms and Unity, a content creation tool supporting various platforms. Particle simulation experiments show that particle simulation using Unity performs faster than 136.04%. It is expected that it will be able to select better development tools for future multi - platform support.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.231-242
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• 2016

In order to meet rapid changes of Web information, it is necessary to extend the current Web technologies to represent both the valid time and location of each fact and knowledge, and reason their relationships. Until recently, many researches on qualitative temporal reasoning have been conducted in laboratory-scale, dealing with small knowledge bases. However, in this paper, we propose the design and implementation of a parallel qualitative temporal reasoner, MRQUTER, which can make reasoning over Web-scale large knowledge bases. This parallel temporal reasoner was built on a Hadoop cluster system using the MapReduce parallel programming framework. It decomposes the entire qualitative temporal reasoning process into several MapReduce jobs such as the encoding and decoding job, the inverse and equal reasoning job, the transitive reasoning job, the refining job, and applies some optimization techniques into each component reasoning job implemented with a pair of Map and Reduce functions. Through experiments using large benchmarking temporal knowledge bases, MRQUTER shows high reasoning performance and scalability.

• Journal of Korea Game Society
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• v.5 no.3
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• pp.39-47
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• 2005

Current rendering processors are organized mainly to process a triangle as fast as possible and recently parallel 3D rendering processors, which can process multiple triangles in parallel with multiple rasterizers, begin to appear. For high performance in processing triangles, it is desirable for each rasterizer have its own local pixel cache. However, the consistency problem may occur in accessing the data at the same address simulaneously by more than one rasterizer. In this paper, we propose a parallel rendering processor architecture resolving such consistency problem effectively. Moreover, the proposed architecture reduces the latency due to a pixel cache miss significantly. The experimental results show that proposed architecture achieves almost linear speedup at best case even in sixteen rasterizer

• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.287-292
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• 2001

Multimedia service systems should have efficient capacity to serve the growing clients and new data. In the general streaming services, users can endure the small amount of time delay at the beginning of service. But they want to have good quality of service. A streaming server tries to transfer video files to clients from a repository of files in real time. The server must guarantee concurrent and uninterrupted delivery of each video stream requested from clients. To achieve its purpose, many stream servers adopt multi-processors, sufficient memory, and RAID or SAN in their systems. In this paper, we propose a Linux-based parallel media streaming server. It is superior to the other systems in the storing structure, fault-tolerance, and service capacity. Since this system supports the web interlace, users can operate easily through the www. This system uses unique striping policy to distribute multimedia files into the parallel storage nodes. If a service request occurs, each storage node transmits striped files concurrently to the client. Its performance is better than the single media streaming service because of the parallel architecture.

• The Journal of Engineering Research
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• v.2 no.1
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• pp.39-47
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• 1997

This paper presents a single board digital signal processing system which can perform two-dimensional (2-D) digital infinite impulse response (IIR) filtering in realtime. We have developed an architecture to provide not only the necessary computational power but also a balance of the system input/output and computational requirements. The architecture achieves large system throughput by using highly parallel processing at both the system and processor levels. It reduces system data communication requirements significantly by taking advantage of a custom-designed processor and by providing each processor with its own input and ouput channel. After system initialization, almost 100 percent of the time is used for data processing. Data transfers occur concurrently with data processing. The functional level simulation reveals that the system throughput can reach as high as one pixel per system cycle. With only 10MHz clock frequency system, it can implement up to fourth order 2-D IIR filters for video-rate data ($512\times512$ pixels per frame at 30 frames per second). If we increase the system frequency, the system can be used for the preprocessing and postprocessing of video signal of HDTV.