• Journal of Multimedia Information System
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• v.5 no.3
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• pp.201-208
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• 2018

High Efficiency Video Coding range extensions (HEVC RExt) is a kind of extension model of HEVC. HEVC RExt was specially designed for dealing the high quality images. HEVC RExt is very essential for studio editing which handle the very high quality and various type of images. There are some problems to dealing these massive data in studio editing. One of the most important procedure is re-encoding and decoding procedure during the editing. Various codecs are widely used for studio data editing. But most of the codecs have common problems to dealing the massive data in studio editing. First, the re-encoding and decoding processes are frequently occurred during the studio data editing and it brings enormous time-consuming and video quality loss. This paper, we suggest new video coding structure for the efficient studio video editing. The coding structure which is called "ultra-low delay (ULD)". It has the very simple and low-delayed referencing structure. To simplify the referencing structure, we can minimize the number of the frames which need decoding and re-encoding process. It also prevents the quality degradation caused by the frequent re-encoding. Various fast coding algorithms are also proposed for efficient editing such as tool-level optimization, multi-serve based distributed coding and SIMD (Single instruction, multiple data) based parallel processing. It can reduce the enormous computational complexity during the editing procedure. The proposed method shows 9500 times faster coding speed with negligible loss of quality. The proposed method also shows better coding gain compare to "intra only" structure. We can confirm that the proposed method can solve the existing problems of the studio video editing efficiently.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.89-100
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• 2006

The introduction of GSMP open interface to OBS network can materialize to separate the transport plane and the control plane in OBS network. This makes the implementation of OBS switches simple and provides various flexibility. However, the introduction of open interface will cause the connection setup delay because of the additional processing overhead of open interface protocol. Also, in GSMP based network, the location of network management functions are not defined explicitly and the research result about the OBS network management is almost nothing. This paper proposes a parallel connection setup mechanism using centralized connection control server to minimize connection setup delay in OBS network with GSMP open interface and defines managed objects to support connection, configuration, performance, and fault management for the management of OBS network with GSMP open interface. This paper also proposes a distributed network management model, in which the above managed objects are distributed in a controller and an OBS switch according to network management functions. We verify the possibility of OBS control and network management by implementing network management function using proposed parallel connection setup mechanism and distributed network management model.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.12
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• pp.673-681
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• 2004

As the use of digital videos is getting popular, there is an increasing demand for efficient browsing and retrieval of video. To support such operations, effective video indexing should be incorporated. One of the most fundamental steps in video indexing is to parse video stream into shots and scenes. Generally, it takes long time to parse a video due to the huge amount of computation in a traditional single computing environment. Previous studies had widely used Round Robin scheduling which basically allocates tasks to each slave for a time interval of one quantum. This scheduling is difficult to adapt in a heterogeneous environment. In this paper, we propose two different parallel parsing algorithms which are Size-Adaptive Round Robin and Dynamic Size-Adaptive Round Robin for the heterogeneous distributed computing environments. In order to show their performance, we perform several experiments and show some of the results.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.273-282
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• 2016

Visualization tool can be divided by three components - data I/O, visual transformation and interactive rendering. In this paper, we present requirements of three major components on visualization tools for massive scientific dataset and propose strategies to develop the tool which satisfies those requirements. In particular, we present how to utilize open source softwares to efficiently realize our goal. Furthermore, we also study the way to combine several open source softwares which are separately made to produce a single visualization software and optimize it for realtime visualization of massiv espatio-temporal scientific dataset. Finally, we propose a distributed shared memory based scientific visualization tool which is called "GLOVE". We present a performance comparison among GLOVE and well known open source visualization tools such as ParaView and VisIt.

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.519-524
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• 2002

In this paper we study the qualify of sequential coverage analysis under a scenario of distributed stochastic simulation known as MRIP(Multiple Replications In Parallel) in terms of the confidence intervals of coverage and the speedup. The estimator based in the F-distribution was applied to the sequential coverage analysis of steady-state means. in simulations of the $M/M/1/{\infty},\;M/D/I/{\infty}\;and\;M/H_{2}/1/{\infty}$ queueing systems on a single processor and multiple processors. By using multiple processors under the MRIP scenario, the time for collecting many replications needed in sequential coverage analysis is reduced. One can also easily collect more replications by executing it in distributed computers or clusters linked by a local area network.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.361-366
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• 2023

Apache Hadoop, a representative framework for distributed processing of big data, has the advantage of increasing cluster size up to thousands of nodes to improve parallel distributed processing performance. However, reducing the size of the cluster is limited to the extent of permanently decommissioning nodes with defects or degraded performance, so there are limitations to operate multiple nodes flexibly in small clusters. In this paper, we discuss the problems that occur when removing nodes from the Hadoop cluster and propose a dynamic down-scaling technique to manage the distributed cluster more flexibly. To do this, we design and implement a modified Hadoop system and interfaces to support dynamic down-scaling of the cluster which supports temporary pause of a node and reconnection of it when necessary, rather than decommissioning the node when removing a node from the Hadoop cluster. We have verified that effective downsizing can be performed without performance degradation based on experimental results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.287-295
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• 2010

In this paper, we introduce a novel approach to optimization algorithm which is a distributed Mean field Genetic algorithm (MGA) implemented in MPI(Message Passing Interface) environments. Distributed MGA is a hybrid algorithm of Mean Field Annealing(MFA) and Simulated annealing-like Genetic Algorithm(SGA). The proposed distributed MGA combines the benefit of rapid convergence property of MFA and the effective genetic operations of SGA. The proposed distributed MGA is applied to the channel routing problem, which is an important issue in the automatic layout design of VLSI circuits. Our experimental results show that the composition of heuristic methods improves the performance over GA alone in terms of mean execution time. It is also proved that the proposed distributed algorithm maintains the convergence properties of sequential algorithm while it achieves almost linear speedup as the problem size increases.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.4
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• pp.289-293
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• 2016

As more mobile devices are equipped with multi-core CPUs and are required to execute many compute-intensive multimedia applications, it is important to optimize the systems, considering the underlying parallel hardware architecture. In this paper, we implement and optimize ray-tracing application tailored to a given mobile computing platform with multiple heterogeneous processing elements. In this paper, a lightweight ray-tracing application is specified and implemented in Kahn process network (KPN) model-of-computation, which is known to be suitable for the description of real-time applications. We take an open-source C/C++ implementation of ray-tracing and adapt it to KPN description in the Distributed Application Layer framework. Then, several possible configurations are evaluated in the target mobile computing platform (Exynos 5422), where eight heterogeneous ARM cores are integrated. We derive the optimal degree of parallelism and a suitable distribution of the replicated tasks tailored to the target architecture.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1141-1149
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• 2000

This paper proposes new cost error measurement method and analyzes the optimistic and pessimistic cost errors statistically which is resulted from an asynchronous parallel Simulated annealing (SA) in distributed memory multicomputers. The traditional cost error measurement scheme has inherent problems which are corrected in the new method. At each temperature the new method predicts the amount of cost error that an algorithm will tolerate and still converge by the hill-climbing nature of SA. This method also explains three interesting phenomenon of he cost error analytically. So the new cost error measurement method provides a single mechanism for the occurrence of cost error and its control.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1201-1210
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• 2000

In order to tolerate faults which may occur during the execution of distributed tasks in high-performance parallel computer systems, tasks are duplicated on different processors. In this paper, by utilizing the task duplication based scheduling algorithm, a new task scheduling algorithm which duplicates each task on more than two different processors with the minimum schedule length is presented, and the number of processors required for the duplication is analyzed with the ratio of communication cost to computation time and the workload of the system. A simulation with various task graphs reveals that the number of processors required for the full-duplex fault-tolerant task scheduling with the obtainable minimum schedule length increases about 30% to 75% when compared with that of the task duplication based scheduling algorithm.