• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.270-276
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• 2018

Apache Spark is one of the high performance in-memory computing frameworks for big-data processing. Recently, to improve the performance, general-purpose computing on graphics processing unit(GPGPU) is adapted to Apache Spark framework. Previous Spark-GPGPU frameworks focus on overcoming the difficulty of an implementation resulting from the difference between the computation environment of GPGPU and Spark framework. In this paper, we propose a Spark framework based on a heterogenous pipeline computing with OpenCL to further improve the performance. The proposed framework overlaps the Java-to-Native memory copies of CPU with CPU-GPU communications(DMA) and GPU kernel computations to hide the CPU idle time. Also, CPU-GPU communication buffers are implemented with switching dual buffers, which reduce the mapped memory region resulting in decreasing memory mapping overhead. Experimental results showed that the proposed Spark framework based on a heterogenous pipeline computing with OpenCL had up to 2.13 times faster than the previous Spark framework using OpenCL.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.731-763
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• 2017

In Underwater Linear Sensor Networks (UW-LSN) routing process, nodes without proper address make it difficult to determine relative sensor details specially the position of the node. In addition, it effects to determine the exact leakage position with minimized delay for long range underwater pipeline monitoring. Several studies have been made to overcome the mentioned issues. However, little attention has been given to minimize communication delay using dynamic addressing schemes. This paper presents the novel solution called Hop-by-Hop Dynamic Addressing based Routing Protocol for Pipeline Monitoring (H2-DARP-PM) to deal with nodes addressing and communication delay. H2-DARP-PM assigns a dynamic hop address to every participating node in an efficient manner. Dynamic addressing mechanism employed by H2-DARP-PM differentiates the heterogeneous types of sensor nodes thereby helping to control the traffic flows between the nodes. The proposed dynamic addressing mechanism provides support in the selection of an appropriate next hop neighbour. Simulation results and analytical model illustrate that H2-DARP-PM addressing support distribution of topology into different ranges of heterogeneous sensors and sinks to mitigate the higher delay issue. One of the distinguishing characteristics of H2-DARP-PM has the capability to operate with a fewer number of sensor nodes deployed for long-range underwater pipeline monitoring.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.379-382
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• 2002

GA(Genetic Algorithm)는 자연계 진화를 모방한 계산 알고리즘으로서 단순하고 응용이 쉽기 때문에 여러 분야에 사용되고 있다. 하지만 GA의 단점은 일반적인 소프트웨어로 동작시켰을 때는 실행속도가 느리다는 것이다. 특히 chromosome이 길 경우 연속적인 교차, 돌연변이를 수행해야한다. GA Processor(GAP)는 GA를 수행하기위한 전용 Processor로서 GA의 동작을 빨리 수행할 수 있게 한다. 본 논문에서는 pipeline 구조의 GAP를 설계하여 GA를 수행함에 있어 소프트웨어와 하드웨어의 성능을 비교한다.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.107-112
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• 2013

With the progress of NGS technologies, large genome data have been exploded recently. To analyze such data effectively, the assistance of HPC technique is necessary. In this paper, we organized a genome analysis pipeline to call SNP from NGS data. To organize the pipeline efficiently under HPC environment, we analyzed the CPU utilization pattern of each pipeline steps. We found that sequence alignment is computing centric and suitable for parallelization. We also analyzed the performance of parallel open source alignment tools and found that alignment method utilizing many-core processor can improve the performance of genome analysis pipeline.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.278-288
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• 2021

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.79-86
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• 2008

H.264/AVC is the newest international video coding standard developed by the joint ITU-T and ISO/IEC standards organizations. This newest video coding standard offers much higher coding efficiency than the H.261, H.263 and MPEG-4. But it has high computing complexity and high H/W resources wasting problem. This paper described the two unit parallel pipeline structure. This new structure comparing with standard model decreased the computing complexity of 67% and the H/W resources waste of 3%.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.161-166
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• 2014

An accurate gain-of-two amplifier, which successfully reduces the capacitor mismatch error is proposed. This amplifier has similar circuit complexity and linearity improvement to the capacitor error-averaging technique, but operates with two clock phases just like the conventional pipeline stage. This makes it suitable for high-speed, high-resolution analog-to-digital converters (ADCs). Two ADC architectures employing the proposed accurate gain-of-two amplifier are also presented. The simulation results show that the proposed ADCs can achieve 15-bit linearity with 8-bit capacitor matching.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1146-1165
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• 2022

A huge amount of data in the form of videos and images is being produced owning to advancements in sensor technology. Use of low performance commodity hardware coupled with resource heavy image processing and analyzing approaches to infer and extract actionable insights from this data poses a bottleneck for timely decision making. Current approach of GPU assisted and cloud-based architecture video analysis techniques give significant performance gain, but its usage is constrained by financial considerations and extremely complex architecture level details. In this paper we propose a data pipeline system that uses open-source tools such as Apache Spark, Kafka and OpenCV running over commodity hardware for video stream processing and image processing in a distributed environment. Experimental results show that our proposed approach eliminates the need of GPU based hardware and cloud computing infrastructure to achieve efficient video steam processing for face detection with increased throughput, scalability and better performance.

• KIISE Transactions on Computing Practices
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• v.23 no.12
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• pp.691-697
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• 2017

Volcanic ash has been predicted to be the main source of damage caused by a potential volcanic disaster around Mount Baekdu and the regions of the Korean peninsula. Computer simulations to predict the diffusion of volcanic ash should be performed according to prevalent meteorological situations within a predetermined time. Therefore, a workflow using pipelining is proposed to parallelize the software used for this computation. Due to the nature of volcanic calamities, the simulations need to be carried out for various plausible conditions given that the parameters cannot be precisely determined during the simulations, even at the time of a volcanic eruption. Among the given conditions, computations need to be first performed for the condition with the highest probability so that a response to the volcanic disaster can be provided using these results. Further action can then be performed later based on subsequent results. The computations need to be performed using a volcanic disaster damage prediction system on a computing server with limited computing performance. Hence, an optimal distribution of the computing resources is required. We propose a method through which specific data can be provided first to the proposed pipeline-based workflow.

• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.81-87
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• 2010

This paper proposes a G-BLAST(BLAST using Grid Computing) system, an integrated software package for BLAST searches operated in heterogeneous distributed environment. G-BLAST employed 'database splicing' method to improve the performance of BLAST searches using exists computing resources. G-BLAST is a basic local alignment search tool of DNA Sequence using grid computing in heterogeneous distributed environment. The G-BLAST improved the existing BLAST search performance in gene sequence analysis. Also G-BLAST implemented the pipeline and data management method for users to easily manage and analyze the BLAST search results. The proposed G-BLAST system has been confirmed the speed and efficiency of BLAST search performance in heterogeneous distributed computing.