• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4545-4553
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• 2014

Mobile GPU has led to the rapid development of smart phone graphic technology. Most recent smart phones are equipped with high-performance multi-core GPU. How a multi-core mobile GPU can be utilized efficiently will be a critical issue for improving the smart phone performance. On the other hand, most current research has focused on a single-core mobile GPU; studies of multi-core mobile GPU are rare. In this paper, the job scheduling patterns and the efficiency of multi-core mobile GPU are analyzed. In the profiling result, despite the higher number of GPU cores, the total processing time required for certain graphics applications were increased. In addition, when GPU is processing for 3D games, a substantial amount of overhead is caused by communication between not only the CPU and GPU, but also within the GPUs. These results confirmed that more active research for multi-core mobile GPU should be performed to optimize the present mobile GPUs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.321-324
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• 2010

There have been lots of researches on a multi-core processor. The enhancement has been performed through parallelization method. Multi-core architecture in the mobile environment has emerged. But, there is a limit to a mobile CPU's performance. GP-GPU(General-Purpose computing on Graphics Processing Units) can improve performance without adding other dedicated hardware. This paper presents the implementation of Inverse Quantization, Inverse DCT and Color Space Conversion module in H.264/AVC decoder using Multi-Core GP-GPU for a mobile environments. The proposed architecture improves approximately 50% of performance when it use all the features.

• Journal of IKEEE
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• v.14 no.2
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• pp.69-75
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• 2010

A 3D graphics pipeline is largely divided into geometry stage and rendering stage. In this paper, we propose a method that accelerates a geometry processing in multi-core GP-GPU, using dual-phase structure. It can be improved by parallel data processing using SIMD of GP-GPU, dual-phase structure and memory prefetch. The proposed architecture improves approximately 19% of performance when it use all the features.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.250-253
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• 2011

Chip manufacture nowadays turned their attention from accelerating uniprocessors to integrating multiple cores on a chip. Moreover desktop graphic hardware is now starting to support general purpose computation. Desktop users are able to use multi-core CPU and GPU as a high performance computing resources these days. However exploiting parallel computing resources are still challenging because of lack of higher programming abstraction for parallel programming. The 2-dimensional discrete cosine transform (2D-DCT) algorithms are most computational intensive part of JPEG encoding. There are many fast 2D-DCT algorithms already studied. We implemented several algorithms and estimated its runtime on multi-core CPU and GPU environments. Experiments show that data parallelism can be fully exploited on CPU and GPU architecture. We expect parallelized DCT bring performance benefit towards its applications such as JPEG and MPEG.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.253-260
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• 2009

Many algorithms for computer vision and pattern recognition have recently been implemented on GPU (graphic processing unit) for faster computational times. However, the implementation has two problems. First, the programmer should master the fundamentals of the graphics shading languages that require the prior knowledge on computer graphics. Second, in a job that needs much cooperation between CPU and GPU, which is usual in image processing and pattern recognition contrary to the graphic area, CPU should generate raw feature data for GPU processing as much as possible to effectively utilize GPU performance. This paper proposes more quick and efficient implementation of neural networks on both GPU and multi-core CPU. We use CUDA (compute unified device architecture) that can be easily programmed due to its simple C language-like style instead of GPU to solve the first problem. Moreover, OpenMP (Open Multi-Processing) is used to concurrently process multiple data with single instruction on multi-core CPU, which results in effectively utilizing the memories of GPU. In the experiments, we implemented neural networks-based text extraction system using the proposed architecture, and the computational times showed about 15 times faster than implementation on only GPU without OpenMP.

• Journal of Advanced Navigation Technology
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• v.15 no.2
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• pp.281-286
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• 2011

Data sorting is one of important pre-process to utilize huge data in modern society, but sorting spends a lot of time by sorting itself. In this paper, we presented hybrid sorting algorithm that splits array to sort concurrently in CPU and GPU. To do this, we decided most effective range of array based on hardware performance, then accomplished reducing whole sorting time by concurrent sorting on CPU and GPU. As shown in results of experiment, hybrid sorting improved about eight percent of sorting time in comparison with the sorting time using only GPU.

• Journal of IKEEE
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• v.15 no.3
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• pp.248-254
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• 2011

Recently, processing burden of CPU is growing because of graphical user interface according to enhance the performance of mobile devices and various graphical effects and creation of contents with 3D graphical effect or Flash animation. Therefore, the GPU are introduced to mobile device for support to variety contents. In this paper, OpenVG accelerator was implemented based on multi-core GP-GPU. OpenVG accelerator is verified using the sample image provided by Khronos group, and overall function is processed by only instruction set without dedicate hardware. The performance of processing the Tiger Image was 2 frames/sec.

• Journal of KIISE
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• v.42 no.4
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• pp.522-529
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• 2015

Ever since the R-tree was proposed to index multi-dimensional data, many efforts have been made to improve its query performances. One common trend to improve query performance is to parallelize query processing with the use of multi-core architectures. To this end, a GPU-base R-tree has been recently proposed. However, even though a GPU-based R-tree can exhibit an improvement in query performance, it is limited in its ability to handle large volumes of data because GPUs have limited physical memory. To address this problem, we propose MGR-tree (Multi-GPU R-tree), which can manage large volumes of data by dividing nodes into multiple GPUs. Our experiments show that MGR-tree is up to 9.1 times faster than a sequential search on a GPU and up to 1.6 times faster than a conventional GPU-based R-tree.

• ETRI Journal
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• v.43 no.3
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• pp.524-537
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• 2021

High-performance Linpack (HPL) is among the most popular benchmarks for evaluating the capabilities of computing systems and has been used as a standard to compare the performance of computing systems since the early 1980s. In the initial system-design stage, it is critical to estimate the capabilities of a system quickly and accurately. However, the original HPL mathematical model based on a single core and single communication layer yields varying accuracy for modern processors and accelerators comprising large numbers of cores. To reduce the performance-estimation gap between the HPL model and an actual system, we propose a mathematical model for multi-communication layered HPL. The effectiveness of the proposed model is evaluated by applying it to a GPU cluster and well-known systems. The results reveal performance differences of 1.1% on a single GPU. The GPU cluster and well-known large system show 5.5% and 4.1% differences on average, respectively. Compared to the original HPL model, the proposed multi-communication layered HPL model provides performance estimates within a few seconds and a smaller error range from the processor/accelerator level to the large system level.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.21-31
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• 2014

Singular value decomposition (SVD) has been widely used to identify unique features from a data set in various fields. However, a complex matrix calculation of SVD requires tremendous computation time. This paper improves the performance of a representative one-sided block Jacoby algorithm using a two-dimensional (2D) multi-core system. In addition, this paper explores an optimal multi-core system by varying the number of processing elements in the 2D multi-core system with the same 400MHz clock frequency and TSMC 28nm technology for each matrix-based one-sided block Jacoby algorithm ($128{\times}128$, $64{\times}64$, $32{\times}32$, $16{\times}16$). Moreover, this paper demonstrates the potential of the 2D multi-core system for the one-sided block Jacoby algorithm by comparing the performance of the multi-core system with a commercial high-performance graphics processing unit (GPU).