• Journal of KIISE:Computer Systems and Theory
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• v.27 no.2
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• pp.188-199
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• 2000

This paper presents an efficient stable logging scheme for the distributed shared memory system based on the lazy release consistent memory model. In the proposed scheme, inter-process dependency is traced and stable logging is performed when the dependency relation between processes actually happens. With the dependency tracking, the proposed scheme requires much less frequency of stable logging, comparing with the previous schemes in which stable logging is performed whenever any information transfer happens between processes. Also, in the proposed scheme, every data item accessed by a process is not logged, but only the access information is logged in the stable storage. For the recovery from a failure, the correct version of the accessed data items can be effectively traced by using the logged access information. As a result, the amount of logged information is also reduced.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.5_6
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• pp.328-334
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• 2004

Recently, Software DSMs continue to improve its performance and scalability As Software DSMs become attractive on larger clusters, the focus of attention is likely to move toward improving the reliability of a system. A popular approach to tolerate failures is message logging with checkpointing, and so many log-based rollback recovery schemes have been proposed. In this work, we propose a remote logging scheme which uses the volatile memory of a remote node assigned to each node. As our remote logging does not incur frequent disk accesses during failure-free execution, its logging overhead is not significant especially over high-speed communication network. The remote logging tolerates multiple failures if the backup nodes of failed nodes are alive. It makes the reliability of DSMs grow much higher. We have designed and implemented the FT-KDSM(Fault Tolerant KAIST DSM) with the remote logging and showed the logging overhead and the recovery time.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2245-2252
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• 1999

Since DSM provides a user a simple shared memory abstraction, the user does not have to be concerned with data movement between hosts. Each node in DSM systems has processor, memory, and connection to a network. Memory is divided into pages, and a page can have multiple copies in different nodes. To maintain data consistency between nodes, two conventional protocols are used : write-update protocol and invalidate protocol. The performance of these protocols depends on the system parameters and the memory access patterns. for adapting to memory access patterns, competitive update protocol updates those copies of a page that are expected to be used in the near future, while selectively invalidating other copies. We present weighted competitive update protocols that consider different communication bandwidth for each connection a of two nodes. Test result by simulation show that the weighted competitive update protocol improves performance.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.9
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• pp.626-633
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• 2006

The wireless sensor networks are sensing, computing and communication infrastructures that allow us to monitor, instrument, observe, and respond to phenomena in the harsh environment. Sensor operating systems that run on tiny sensor nodes are the key to the performance of the distributed computing environment for the wireless sensor networks. Therefore, sensor operating systems should be able to operate efficiently in terms of energy consumption and resource management. In this paper, we present an efficient memory allocation scheme to improve the time and space efficiency of memory management for the sensor operating systems. Our experimental results show that the proposed scheme performs efficiently in both time and space compared with existing memory allocation mechanisms.

• KNOM Review
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• v.22 no.3
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• pp.20-24
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• 2019

Blockchain is a distributed ledger-based technology where all nodes participating in the blockchain network are connected to the P2P network. When a transaction is created in the blockchain network, the transaction is propagated and validated by the blockchain nodes. The verified transaction is sent to peers connected to each node through P2P network, and the peers keep the transaction in the memory pool. Due to the nature of P2P networks, the number and type of transactions delivered by a blockchain node is different for each node. As a result, all nodes do not have the same memory pool. Research is needed to solve problems such as attack detection. In this paper, we analyze transactions in the memory pool before solving problems such as transaction fee manipulation, double payment problem, and DDos attack detection. Therefore, this study collects transactions stored in each node memory pool of Bitcoin and Ethereum, a cryptocurrency system based on blockchain technology, and analyzes how much common transactions they have using jacquard similarity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1490-1501
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• 2016

In this paper, we propose an image-based indoor localization system using parallel distributed computing. In order to reduce computation time for indoor localization, an scale invariant feature transform (SIFT) algorithm is performed in parallel by using Apache Spark. Toward this goal, we propose a novel image processing interface of Apache Spark. The experimental results show that the speed of the proposed system is about 3.6 times better than that of the conventional system.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.73-78
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• 2003

In this research, we implement Realtime Air Diffusion Prediction System which is a parallel Fortran model running on distributed-memory parallel computers. The system is designed for air diffusion simulations with four-dimensional data assimilation. For regional air quality forecasting a series of dynamic downscaling technique is adopted using the NCAR/Penn. State MM5 model which is an atmospheric model. The realtime initial data have been provided daily from the KMA (Korean Meteorological Administration) global spectral model output. It takes huge resources of computation to get 24 hour air quality forecast with this four step dynamic downscaling (27km, 9km, 3km, and lkm). Parallel implementation of the realtime system is imperative to achieve increased throughput since the realtime system have to be performed which correct timing behavior and the sequential code requires a large amount of CPU time for typical simulations. The parallel system uses MPI (Message Passing Interface), a standard library to support high-level routines for message passing. We validate the parallel model by comparing it with the sequential model. For realtime running, we implement a cluster computer which is a distributed-memory parallel computer that links high-performance PCs with high-speed interconnection networks. We use 32 2-CPU nodes and a Myrinet network for the cluster. Since cluster computers more cost effective than conventional distributed parallel computers, we can build a dedicated realtime computer. The system also includes web based Gill (Graphic User Interface) for convenient system management and performance monitoring so that end-users can restart the system easily when the system faults. Performance of the parallel model is analyzed by comparing its execution time with the sequential model, and by calculating communication overhead and load imbalance, which are common problems in parallel processing. Performance analysis is carried out on our cluster which has 32 2-CPU nodes.

• Journal of KIISE
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• v.45 no.3
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• pp.280-287
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• 2018

Recently, the use of a recommendation system and tensor data analysis, which has high-dimensional data, is increasing, as they allow us to analyze the tensor and extract potential elements and patterns. However, due to the large size and complexity of the tensor, it needs to be decomposed in order to analyze the tensor data. While several tools are used for tensor decomposition such as rTensor, pyTensor, and MATLAB, since such tools run on a single machine, they are unable to handle large data. Also, while distributed tensor decomposition tools based on Hadoop can handle a scalable tensor, its computing speed is too slow. In this paper, we propose S-PARAFAC, which is a tensor decomposition tool based on Apache Spark, in distributed in-memory environments. We converted the PARAFAC algorithm into an Apache Spark version that enables rapid processing of tensor data. We also compared the performance of the Hadoop based tensor tool and S-PARAFAC. The result showed that S-PARAFAC is approximately 4~25 times faster than the Hadoop based tensor tool.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.6
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• pp.700-707
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• 1999

본 논문에서는 GIS 관련 연산을 실시간에 효율적으로 처리하기 위한 분산공유메모리 기반 병렬처리 시스템을 제안한다. 본 논문의 분산공유메모리 시스템은 메시지전달 방식의 분산메모리 MIMD 컴퓨터 상에 소프트웨어 기반 분산공유메모리 모듈을 탑재함으로써 구현되었다. 또한 GIS 연산의 기본이 되는 공간 객체를 공유의 기본 단위로 설정하고, GIS 데이타의 특성을 반영하여 읽기전용 공유데이타 타입을 추가하였으며, 네트워크 오버헤드를 줄이기 위하여 복수의 객체를 한번에 읽어오는 bulk access가 가능하도록 하였다. 본 시스템에서는 GIS 데이타의 효율적인 분배를 위하여 부하균등화 기법으로 guided self scheduling을 사용하였다. 실험결과 본 시스템은 네트워크 캐쉬의 효율적인 활용을 통하여 소프트웨어 기반 분산메모리 시스템의 오버헤드에도 불구하고 MPI 기반 메시지전달 방식에 비하여 향상된 성능을 얻을 수 있었다.Abstract In this paper, we propose a distributed shared memory(DSM) based parallel processing system to process GIS related computations efficiently in real time. The system is based on a software DSM module implemented on top of a distributed MIMD computer. In the DSM system, spatial object, which is a fundamental structure to represent GIS data, is used as a basic unit for sharing, and a read-only shared data type is added to reflect the characteristics of GIS data. In addition, a bulk access to multiple shared data is made possible to reduce the network overhead. A guided self scheduling method is devised for efficient load balancing in distributing GIS data to parallel processors. The experimental results show that the DSM system performs better than an MPI based message-passing system through the efficient utilization of network cache in spite of the system's software overhead.

• Journal of The Korean Society of Integrative Medicine
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• v.6 no.4
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• pp.29-38
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• 2018

Purpose: The purpose of this study was to investigate the psychological symptoms of insomnia, impulsiveness, and memory impairment according to the drinking frequency of college students. Methods: From May 4 to May 17, 2018, a questionnaire survey was conducted for men and women enrolled in J city S university in the Department of Occupational Therapy, Architecture, Social Welfare, and Digital Content. After visiting the department to explain the purpose of the study, 400 questionnaires were distributed to those who agreed to participate in the study. The SPSS 22.0 program was used to analyze the data collected. All statistical analyses were performed at the significance level of 5 %. Results: There is a correlation between alcohol consumption and psychological symptoms, such as insomnia, impulsivity, and memory impairment. As a result of analyzing all departments, insomnia, impulsivity, and memory impairment were the highest in the addiction level. In post-analysis of psychological symptoms, insomnia and impulsiveness had no significant difference, but there was a significant difference in memory impairment (p=.04). Conclusion: Our hope is that this study will help activate programs like preventative education and counseling on alcohol-related psychological symptoms for college students.