• Journal of Digital Contents Society
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• v.15 no.5
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• pp.631-642
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• 2014

The main memory DBMS is operated which the contents of the table that resides on a disk at the same time as the drive is in the memory. However, because the main memory DBMS stores the data and transaction log file using the disk file system, there are a limit to the speed at which the CPU accesses the memory. In this paper, I evaluated the performance through analysis of the application side difference the technology that has been implemented in Altibase system of main memory DBMS and Sybase of disk-based DBMS. When the application performance of main memory DBMS is in comparison with the disk-based DBMS, the performance of main memory DBMS was outperformed 1.24~3.36 times in the single soccer game, and was outperformed 1.29~7.9 times in the soccer game / special soccer. The result of sale transaction response time showed a fast response time of 1.78 ~ 6.09 times.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1448-1455
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• 2008

This paper designs Shared Memory on the Dual Core system so that it operates a general System V IPC on the Linux O.S. Shared Memory is the technique that many processes can access to identical memory area. We treat Shared Memory in this paper among big two branches of Shared Memory which are SVR in a kernel step format. We design a share memory facility of Linux operating system on the Dual Core System. In this paper the suggesting design plan of share memory facility in Dual Core system is enhancing the performance in existing unity processor system as a dual core practical use. We attempt a performance enhance in each CPU for each process which uses a share memory.

• KIISE Transactions on Computing Practices
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• v.20 no.9
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• pp.483-491
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• 2014

Contemporary computer systems exploits DVFS (Dynamic Voltage/Frequency Scaling) technology for balancing performance and power consumption. The efficiency of DVFS depends on how much performance we get for larger power consumption due to elevated CPU frequency. Especially for memory-bounded applications, higher CPU frequency often does not result in higher performance. In this paper, we present an upper bound of CPU frequency scaling based on memory accesses. It is observed that the performance gain due to higher CPU frequency is limited by memory accesses (last level cache misses) per instructions by experiments. Using the results, we present the CPU frequency upper bound with little performance gain. Experimental results show that for a memory-bounded application, applying the frequency upper bound enhances the energy efficiency of the application by above 30%.

• Korean Journal of Child Studies
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• v.12 no.1
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• pp.21-37
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• 1991

The present research studied the effectiveness of specific strategy training in memory awareness on children's memory performance. The subjects were 60 children, 30 six-year-olds and 30 eight-year-olds. Free recall scores and use of a rehearsal strategy (exposure durations) based on Belmont & Butterfield (1971) were used to measure children's performance in three memory tasks. All subjects were randomly assigned to one of three experimental groups: the control group with no training, the chunking and rehearsal strategy training group, and the chunking and rehearsal strategy training combined with memory awareness strategy. The data were analyzed with two-way ANOVA, three-way ANOVA with repeated measures, and Student-Newman-Keuls post hoc test. There were significant differences among the three groups both in the free recall score and in the use of the rehearsal strategy. The mnemonic strategic training with memory awareness strategy was the most effective on both free recall and use of rehearsal strategy. The effects of the mnemonic strategy training with memory awareness strategy were more effective for the 8-year-olds than the 6-year-olds.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.51-72
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• 2015

Time predictability is crucial in hard real-time and safety-critical systems. Cache memories, while useful for improving the average-case memory performance, are not time predictable, especially when they are shared in multicore processors. To achieve time predictability while minimizing the impact on performance, this paper explores several time-predictable scratch-pad memory (SPM) based architectures for multicore processors. To support these architectures, we propose the dynamic memory objects allocation based partition, the static allocation based partition, and the static allocation based priority L2 SPM strategy to retain the characteristic of time predictability while attempting to maximize the performance and energy efficiency. The SPM based multicore architectural design and the related allocation methods thus form a comprehensive solution to hard real-time multicore based computing. Our experimental results indicate the strengths and weaknesses of each proposed architecture and the allocation method, which offers interesting on-chip memory design options to enable multicore platforms for hard real-time systems.

• 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.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.531-536
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• 2021

As artificial intelligence technology advances, it is being applied to various application fields. Artificial intelligence is performing well in the field of image recognition and classification. Chip design specialized in this field is also actively being studied. Artificial intelligence-specific chips are designed to provide optimal performance for the applications. At the design task, memory component optimization is becoming an important issue. In this study, the optimal algorithm for the memory size exploration is presented, and the optimal memory size is becoming as a important factor in providing a proper design that meets the requirements of performance, cost, and power consumption.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.78-83
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• 2023

This paper proposes a lightweight trace-driven Processing-In-Memory (PIM) simulator, TP-Sim. TP-Sim is a General Purpose PIM (GP-PIM) simulator that evaluates various PIM system performance-related metrics. Based on instruction and memory traces extracted from the Intel Pin tool, TP-Sim can replay trace files for multiple models of PIM architectures to compare its performance. To verify the availability of TP-Sim, we estimated three different system configurations on the STREAM benchmark. Compared to the traditional Host CPU-only systems with conventional memory hierarchy, simple GP-PIM architecture achieved better performance; even the Host CPU has the same number of in-order cores. For further study, we also extend TP-Sim as a part of a heterogeneous system simulator that contains CPU, GPGPU, and PIM as its primary and co-processors.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.1-11
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• 2012

As process technology scales down, the number of cores integrated into a processor increases dramatically, leading to significant performance improvement. Especially, the GPU(Graphics Processing Unit) containing many cores can provide high computational performance by maximizing the parallelism. In the GPU architecture, the access latency to the main memory becomes one of the major reasons restricting the performance improvement. In this work, we analyze the performance improvement of the 3D GPU architecture compared to the 2D GPU architecture quantitatively and investigate the potential problems of the 3D GPU architecture. In general, memory instructions account for 30% of total instructions, and global/local memory instructions constitutes 60% of total memory instructions. Therefore, the performance of the 3D GPU is expected to be improved significantly compared to the 2D GPU by reducing the delay of memory instructions. However, according to our experimental results, the 3D architecture improves the GPU performance only by 2% compared to the 2D architecture due to the memory bottleneck, since the performance reduction due to memory bottleneck in the 3D GPU architecture increases by 245% compared to the 2D architecture. This paper provides the guideline for suitable memory design by analyzing the efficiency of the memory architecture in 3D GPU architecture.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.615-619
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• 2021

This study focuses on memory systems that are optimized to increase performance and energy efficiency in many embedded systems such as IoT, cloud computing, and edge computing, and proposes a performance improvement technique. The proposed technique improves memory system performance based on machine learning algorithms that are widely used in many applications. The machine learning technique can be used for various applications through supervised learning, and can be applied to a data classification task used in improving memory system performance. Data classification based on highly accurate machine learning techniques enables data to be appropriately arranged according to data usage patterns, thereby improving overall system performance.