• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.197-199
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• 2012

Recently, a lot of general-purpose application programs in addition to graphic applications have been parallelized for boosting their performance using Graphic Processing Unit (GPU)'s excellent floating-point performance. In order to maximize the application performance on GPUs, optimizing the memory hierarchy and the on-chip caches such as the shared memory is essential. In this paper, we propose techniques to optimize the shared memory, and verify its effectiveness using a pattern matching application program.

• Psychiatry investigation
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• v.15 no.12
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• pp.1154-1161
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• 2018

Objective The present study aimed to investigate relationship among cognitive factors (working memory and processing speed) and fluid reasoning (Gf) in psychiatric patients using a standardized clinical tool. Methods We included the responses of 115 heterogeneous patients who were diagnosed with the MINI-Plus 5.0 and WAIS-IV/WMS-IV was administered. For our analysis, structured equation modeling (SEM) was conducted to evaluate which cognitive variables are closely related to the Gf. Results The results showed that the visual working memory was the strongest predictor of the Gf compared to other cognitive factors. Conclusion Processing speed was capable of predicting the Gf, when visual working memory was controlled. The inter-relationship among the Gf and other cognitive factors and its clinical implications were further discussed.

• Journal of the Semiconductor & Display Technology
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• v.11 no.2
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• pp.85-91
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• 2012

In this paper, the large data processing and suitable FAT32 file system for industrial system using a PLC and CF memory was implemented. Most of PLC can't save the large data in user data memory. So it's required to the external devices of CF memory or NAND flash memory. The CF memory is used in order to save the large data of PLC system. The file system using the CF memory is NTFS, FAT, and FAT32 system to configure in various ways. Typically, the file system which is widely used in industrial data storage has been implemented as modified FAT32. The conventional FAT 32 file system was not possible for multiple writing and high speed data accessing. The proposed file system was implemented by the large data processing module can be handled that the files are copied at the 40 bytes for 1msec speed logging and creating 8 files at the same time. In a sudden power failure, high reliability was obtained that the problem was solved using a power fail monitor and the non-volatile random-access memory (NVSRAM). The implemented large data processing system was applied the modified file system as FAT32 and the good performance and high reliability was showed.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.1471-1480
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• 2006

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.606-613
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• 2018

This study is concerned with implementing an external memory expansion device for large-scale image processing. It consists of an external memory adapter card with a PCI(Peripheral Component Interconnect) Express Gen3 x8 interface mounted on a graphics workstation for image processing and an external memory board with external DDR(Dual Data Rate) memory. The connection between the memory adapter card and the external memory board is made through the optical interface. In order to access the external memory, both Programmable I/O and DMA(Direct Memory Access) methods can be used to efficiently transmit and receive image data. We implemented the result of this study using the boards equipped with Altera Stratix V FPGA(Field Programmable Gate Array) and 40G optical transceiver and the test result shows 1.6GB/s bandwidth performance.. It can handle one channel of 4K UHD(Ultra High Density) image. We will continue our study in the future for showing bandwidth of 3GB/s or more.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.3
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• pp.13-19
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• 2016

Spark, an in-memory big-data processing framework is popular to use for real-time processing workload. Spark can store all intermediate data in the cluster memory so that Spark can minimize I/O access. However, when the resident memory of workload is larger that the physical memory amount of the cluster, the total performance can drop dramatically. In this paper, we analyse the factors of bottleneck on PageRank Application that needs many memory through experiment, and cluster the Spark with Tachyon File System for using memory to solve the factor of bottleneck and then we improve the performance about 18%.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.151-158
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• 2019

With the recent advances of memory technologies, high-performance non-volatile memories such as non-volatile dual in-line memory module (NVDIMM) have begun to be used as an addition or an alternative to server-side storages. When these memory bus-connected storages (MBSs) are installed over non-uniform memory access (NUMA) servers, the distance between NUMA nodes and MBSs is one of the crucial factors that influence file processing performance, because the access latency of a NUMA system varies depending on its distance from the NUMA nodes. This paper presents the design and implementation of a high-performance logical volume manager for MBSs, called MBS-LVM, when multiple MBSs are scattered over a NUMA server. The MBS-LVM consolidates the address space of each MBS into a single global address space and dynamically utilizes storage spaces such that each thread can access an MBS with the lowest latency possible. We implemented the MBS-LVM in the Linux kernel and evaluated its performance by porting it over the tmpfs, a memory-based file system widely used in Linux. The results of the benchmarking show that the write performance of the tmpfs using MBS-LVM has been improved by up to twenty times against the original tmpfs over a NUMA server with four nodes.

• International Journal of Contents
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• v.12 no.1
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• pp.60-64
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• 2016

As new applications of cloud data management system (CDMS) such as online games, cooperation edit, social network, and so on, are increasing, transaction processing capabilities for CDMS are required. Several transaction processing methods for cloud data management system (CDMS) have been proposed. However, existing transaction processing methods have some problems. Some of them provide limited transaction processing capabilities. Some of them are hard to be integrated with existing CDMSs. In this paper, we proposed a new concurrency control method to support transaction processing capability for CDMS to solve these problems. The proposed method was designed and implemented based on Spark, an in-memory distributed processing framework. It uses RDD (Resilient Distributed Dataset) model to provide fault tolerant to data in the main memory. In our proposed method, database stored in CDMS is loaded to main memory managed by Spark. The loaded data set is then transformed to RDD. In addition, we proposed a multi-version concurrency control method through immutable characteristics of RDD. Finally, we performed experiments to show the feasibility of the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.567-571
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• 2009

This study was conducted in order to investigate the role of working memory in word recognition. As a preliminary step in tackling this topic, word frequency and working memory load were manipulated in a naming task. The results showed that word frequency is significantly involved with the working memory load. The effects of working memory load were greater in low-frequency word processing than in high-frequency word processing. These results indicat that working memory is involved more in the processing of low-frequency words. The implications for the teaching of children at the early reading acquisition stage are discussed in this paper.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.2
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• pp.127-134
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• 2019

With the advancement of IT technology, the amount of data generated has been growing exponentially every year. As an alternative to this, research on distributed systems and in-memory based big data processing schemes has been actively underway. The processing power of traditional big data processing schemes enables big data to be processed as fast as the number of nodes and memory capacity increases. However, the increase in the number of nodes inevitably raises the frequency of failures in a big data infrastructure environment, and infrastructure management points and infrastructure operating costs also increase accordingly. In addition, the increase in memory capacity raises infrastructure costs for a node configuration. Therefore, this paper proposes an in-memory-based hybrid big data processing scheme for improve the big data processing rate. The proposed scheme reduces the number of nodes compared to traditional big data processing schemes based on distributed systems by adding a combiner step to a distributed system processing scheme and applying an in-memory based processing technology at that step. It decreases the big data processing time by approximately 22%. In the future, realistic performance evaluation in a big data infrastructure environment consisting of more nodes will be required for practical verification of the proposed scheme.