• Geophysics and Geophysical Exploration
• /
• v.14 no.1
• /
• pp.98-104
• /
• 2011

Numerical simulation in exploration geophysics provides important insights into subsurface wave propagation phenomena. Although elastic wave simulations take longer to compute than acoustic simulations, an elastic simulator can construct more realistic wavefields including shear components. Therefore, it is suitable for exploration of the responses of elastic bodies. To overcome the long duration of the calculations, we use a Graphic Processing Unit (GPU) to accelerate the elastic wave simulation. Because a GPU has many processors and a wide memory bandwidth, we can use it in a parallelised computing architecture. The GPU board used in this study is an NVIDIA Tesla C1060, which has 240 processors and a 102 GB/s memory bandwidth. Despite the availability of a parallel computing architecture (CUDA), developed by NVIDIA, we must optimise the usage of the different types of memory on the GPU device, and the sequence of calculations, to obtain a significant speedup of the computation. In this study, we simulate two- (2D) and threedimensional (3D) elastic wave propagation using the Finite-Difference Time-Domain (FDTD) method on GPUs. In the wave propagation simulation, we adopt the staggered-grid method, which is one of the conventional FD schemes, since this method can achieve sufficient accuracy for use in numerical modelling in geophysics. Our simulator optimises the usage of memory on the GPU device to reduce data access times, and uses faster memory as much as possible. This is a key factor in GPU computing. By using one GPU device and optimising its memory usage, we improved the computation time by more than 14 times in the 2D simulation, and over six times in the 3D simulation, compared with one CPU. Furthermore, by using three GPUs, we succeeded in accelerating the 3D simulation 10 times.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.04a
• /
• pp.778-780
• /
• 2004

This paper presents a computing system, called MEDICOS. that enables Multidisciplinary Design Optimization (MDO) technology for engineering design on distributed environments. In MDO, various legacy softwares have to be Integrated, so dynamic configuration and seamless coordination between these legacy softwares must be supported. MEDICOS is designed to address these issues by the Linda shared memory model-based design and the agent-based wrapper technology. A prototype system for engineering designs is developed and tested with designing a super high temperature vacuum furnace.

• The Journal of the Korea Contents Association
• /
• v.12 no.2
• /
• pp.30-39
• /
• 2012

In these day, some of IT venders already were going to cloud computing market, as well they are going to expand their territory for the cloud computing market through that based on their hardware and software technology, making collaboration between hardware and software vender. Distributed file system is very mainly technology for the cloud computing that must be protect performance and safety for high levels service requests as well data store. This paper introduced distributed file system for cloud computing and how to use this theory such as memory database, Hadoop file system, high availability database system. now In the market, this paper define a very large distributed processing architect as a reference by kind of distributed file systems through using technology in cloud computing market.

• Journal of Korea Spatial Information System Society
• /
• v.7 no.1 s.13
• /
• pp.25-37
• /
• 2005

Recently, with the development of wireless communications and mobile computing, interest about mobile computing is rising. Mobile computing can be regarded as an environment where a user carries mobile devices, such as a PDA or a notebook, and shares resources with a server computer via wireless communications. A mobile database refers to a database which is used in these mobile devices. The mobile database can be used in the fields of insurance business, banking business, medical treatment, and so on. Especially, LBS(Location Based Service) which utilizes location information of users becomes an essential field of mobile computing. In order to support LBS in the mobile environment, there must be an Embedded Spatial MMDBMS(Main-Memory Database Management System) that can efficiently manage large spatial data in spatial mobile devices. Therefore, in this paper, we designed and implemented the Embedded Spatial MMDBMS, extended from the HSQLDB which is an existing MMDBMS for PC, to manage spatial data efficiently in spatial mobile devices. The Embedded Spatial MMDBMS adopted the spatial data model proposed by ISO(International Organization for Standardization), provided the arithmetic coding method that is suitable for spatial data, and supported the efficient spatial index which uses the MBR compression and hashing method suitable for spatial mobile devices. In addition, the system offered the spatial data display capability in low-performance processors of spatial mobile devices and supported the data caching and synchronization capability for performance improvement of spatial data import/export between the Embedded Spatial MMDBMS and the GIS server.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.99-102
• /
• 2003

Computing environments of Embedded Systems are different from those of desktop computers so that they have resource constraints such as CPU processing, memory capacity, power, and etc.. Thus, when a desktop S/W is ported into embedded systems, optimization should be seriously considered. In this paper, we investigate several S/W optimization techniques to be considered for porting H.263 encoder into a high performance DSP, TMS320C6711. Through experiments, it is found that optimization techniques employed can make a big performance improvement.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.1
• /
• pp.193-198
• /
• 2020

The various uses of the Convolutional Neural Network technology are accelerating the evolution of the computing area, but the opposite is causing serious hardware performance shortages. Neural network accelerators, next-generation memory device technologies, and high-bandwidth memory architectures were proposed as countermeasures, but they are difficult to actively introduce due to the problems of versatility, technological maturity, and high cost, respectively. This study proposes DRAM-based main memory technology that enables read operations to be completed without waiting until the end of the refresh operation using pre-stored XOR bit values, even when the refresh operation is performed in the main memory. The results showed that the proposed technique improved performance by 5.8%, saved energy by 1.2%, and improved EDP by 10.6%.

• Journal of KIISE:Computing Practices and Letters
• /
• v.13 no.5
• /
• pp.333-337
• /
• 2007

Flash Translation Layer(FTL) which enables NAND Flash memory-based storage system to be used as a block device is designed considering only characteristics of NAND Flash memory. However, since FTL precesses I/O requests which survived against buffer replacement algorithm, FTL algorithm has tight relationship with buffer replacement algorithm. Therefore, if we do not consider both FTL and buffer replacement algorithms, it is difficult to predict the actual I/O performance of the computer systems that have Flash memory-based storage system. The necessity of FTL and buffer replacement algorithm co-design arises here. In this work, we implemented I/O performance evaluation tool, IPSiNS, which simulates both the buffer replacement and FTL algorithms, simultaneously.

• Journal of KIISE:Computing Practices and Letters
• /
• v.13 no.6
• /
• pp.422-426
• /
• 2007

Personal mobile devices equipped with non-volatile storage such as MP3 player, PMP, cellular phone, and USB memory require safety for the stored data on the devices. One of the safety requirements is secure deletion, which is removing stored data completely so that the data can not be restored illegally. In this paper, we study how to design the secure deletion on Flash memory, commonly used as storage media for mobile devices. We consider two possible secure deletion policy, named zero-overwrite and garbage-collection respectively, and analyze how each policy affects the performance of Flash memory file systems. Then, we propose an adaptive file deletion scheme that exploits the merits of the two possible policies. Specifically, the proposed scheme applies the zero-overwrite policy for small files, whereas it employs the garbage-collection policy for large files. Real implementation experiments show that the scheme is not only secure but also efficient.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.15 no.5
• /
• pp.125-133
• /
• 2016

Data processing capacity and speed are rapidly increasing due to the development of hardware and software in recent time. As a result, data usage is geometrically increasing and the amount of data which computers have to process has already exceeded five-thousand transaction per second. That is, the importance of Big Data is due to its 'real-time' and this makes it possible to analyze all the data in order to obtain accurate data at right time under any circumstances. Moreover, there are many researches about this as construction of smart factory with the application of Big Data is expected to have reduction in development, production, and quality management cost. In this paper, system using In-Memory Data Grid for high speed processing is implemented in semiconductor process which numerous data occur and improved performance is proven with experiments. Implemented system is expected to be possible to apply on not only the semiconductor but also any fields using Big Data and further researches will be made for possible application on other fields.

• KIISE Transactions on Computing Practices
• /
• v.24 no.1
• /
• pp.24-32
• /
• 2018

This paper proposes a Big Data system for energy Big Data which is aggregated in real-time from industrial and public sources. The constructed Big Data system is based on Hadoop and the Spark framework is simultaneously applied on Big Data processing, which supports in-memory distributed computing. In the paper, we focus on Big Data, in the form of heat energy for district heating, and deal with methodologies for storing, managing, processing and analyzing aggregated Big Data in real-time while considering properties of energy input and output. At present, the Big Data influx is stored and managed in accordance with the designed relational database schema inside the system and the stored Big Data is processed and analyzed as to set objectives. The paper exemplifies a number of heat demand plants, concerned with district heating, as industrial sources of heat energy Big Data gathered in real-time as well as the proposed system.