• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.441-448
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• 2008

The molecular docking system needs a large amount of computation and requires super-computing power. Since the experiment requires a large amount of time, the experiment is conducted in the distributed environment or in the grid environment. Recently, researches on using parallel GPU of far higher performance than that of CPU in scientific computing have been very actively conducted. CUDA is an open technique by which a parallel GPU programming is made possible. This study proposes the molecular docking system using CUDA. It also proposes algorithm that parallels energy-minimizing-computation. To verify such experiments, this study conducted a comparative analysis on the time required for experimenting molecular docking in general CPU and the time and performance of the parallel GPU-based molecular docking which is proposed in this study.

• Electronics and Telecommunications Trends
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• v.31 no.4
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• pp.65-76
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• 2016

특정 제조사의 단말 또는 운영체제에 의존성이 없는 플랫폼 독립적인 웹은 높은 이식성, 소프트웨어의 재활용, 개발 생산성, 풍부한 개발자 존재, 유지 보수 면에서 장점을 가지나, 화려한 UI/UX를 제공하는 네이티브 응용에 비해 낮은 성능으로 웹 기반의 응용 개발 및 보급이 크게 활성화되지 못했다. 한편 데스크톱은 물론 모바일 단말의 멀티코어 기반 Graphic Processing Unit(GPU), CPU 탑재 등 HW 고사양화와 웹 응용에서도 HW 가속 기능을 활용할 수 있는 표준 제공으로 성능 제약을 극복할 수 있게 되었다. 본고에서는 GPU 발전동향을 살펴보고, 고속 렌더링 및 병렬 연산처리를 요구하는 웹 응용이 GPU기반 HW 가속 기능을 활용할 수 있는 크로노스 그룹의 그래픽 가속(Web Graphics Library: WebGL) 및 컴퓨팅(Web Computing Language: WebCL) 지원 표준 규격을 정리한다. 또한, 최근 차세대 GPU Application Programming Interface(API)로 발표된 Vulkan에 대해 알아보고, 웹 고속화 기술에 적용 가능성에 대해 전망한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.44-47
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• 2023

SYCL은 C++을 기반으로 하는 언어로 가속기를 사용하는 복잡한 과정을 C++의 특징 중의 하나인 추상화를 사용해 개발자가 쉽게 접근할 수 있게 한다. 그러나, 가속기를 활용하는 측면에서는 성능을 최대한으로 끌어내기 위해 저수준 접근도 필요하다. 특히, NVLink와 같이 효율적인 멀티-GPU 통신을 해주는 인터커넥션 링크 활용을 위해서도 필요하다. 본 논문에서는 SYCL 구현물 중의 하나인 AdaptiveCpp을 가지고 NVLink로 연동된 멀티 GPU 환경에서 효율적으로 프로그래밍을 할 수 있는 방법을 제안하고, SYCL 개발자들이 SYCL의 설계 철학을 따라 프로그래밍을 할 수 있도록 이러한 기능을 추상화하여 담은 MPI wrapper API를 제안한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.583-588
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• 2011

In this paper, we propose an adaptive variable-sized matching window method using the characteristic points of the image and a method to increase the reliability of the cross-consistency check to raise the correctness of the final disparity image. The proposed adaptive variable-sized window method segments the image with the color information, finds the characteristic points inside the window. Also the proposed algorithm implement using a graphic processing unit(GPU). The GPU, we used in this paper is GeForce GTX296 (NVIDIA) and we can use programming based on CUDA. The calculation speed realizes a speed approximately 128 times faster than that of a CPU.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.1-9
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• 2018

Modern graphics processing units (GPUs) have become one of the most attractive platforms in exploiting high thread level parallelism with the support of new programming tools such as CUDA and OpenCL. Recent GPUs has applied cache hierarchy to support irregular memory access patterns; however, L1 data cache (L1D) exhibits poor efficiency in the GPU. This paper shows that the L1D does not always positively affect the applications in terms of performance and energy efficiency for the GPU. The performance of the GPU is even harmed by using the L1D for lots of applications. Our proposed technique exploits the characteristics of the currently-executed applications to predict the performance impact of the L1D on the GPU and then decides whether to continuously use the cache for the application or not. Our experimental results show that the proposed technique improves the GPU performance by 9.4% and saves up to 52.1% of the power consumption in the L1D.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.10
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• pp.407-414
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• 2017

Pig monitoring applications consisting of many tasks can take advantage of inherent data parallelism and enable parallel processing using performance accelerators. In this paper, we propose a task distribution method for pig monitoring applications into a heterogenous computing platform consisting of a multicore-CPU and a manycore-GPU. That is, a parallel program written in OpenCL is developed, and then the most suitable processor is determined based on the measured execution time of each task. The proposed method is simple but very effective, and can be applied to parallelize other applications consisting of many tasks on a heterogeneous computing platform consisting of a CPU and a GPU. Experimental results show that the performance of the proposed task distribution method on three different heterogeneous computing platforms can improve the performance of the typical GPU-only method where every tasks are executed on a deviceGPU by a factor of 1.5, 8.7 and 2.7, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.479-486
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• 2017

Recently, GPGPU has been able to increase the degradation of computer performance, and it is now possible to run physically based real-time simulations on PCs that require high computational complexity. Physical calculations applied in physics simulation can be performed by parallel processing, and can be efficiently performed using parallel computation using Compute shader recently supported by OpenGL 4.3 and Unity 4.0. In this paper, we measure and compare the number of performance in real - time physics simulation in OpenGL running on various platforms and Unity, a content creation tool supporting various platforms. Particle simulation experiments show that particle simulation using Unity performs faster than 136.04%. It is expected that it will be able to select better development tools for future multi - platform support.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.343-344
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• 2012

With the increase of sensitive data and their secure transmission and storage, the use of encryption techniques has become widespread. The performance of encoding majorly depends on the computational time, so a system with less computational time suits more appropriate as compared to its contrary part. Double Random Phase Encoding (DRPE) is an algorithm with many sub functions which consumes more time when executed serially; the computation time can be significantly reduced by implementing important functions in a parallel fashion on Graphics Processing Unit (GPU). Computing convolution using Fast Fourier transform in DRPE is the most important part of the algorithm and it is shown in the paper that by performing this portion in GPU reduced the execution time of the process by substantial amount and can be compared with MATALB for performance analysis. NVIDIA graphic card GeForce 310 is used with CUDA C as a programming language.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.616-625
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• 2011

There are two representative geometry rendering methods. One is Geometry Clipmaps, another is ROAM 2.0. We propose an extended Geometry Clipmaps algorithm which does not focus on CPU operation but the GPU for faster and wider visibility area. The extended algorithm presents mesh configuration method of each level by LOD, how to configurate Mesh network between levels, mesh block method for rendering optimization using VFC, and image mapping method to get high resolution up to 1 m.

• Journal of Broadcast Engineering
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• v.16 no.4
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• pp.596-601
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• 2011

In this paper, we propose a fast depth-image-based rendering method to generate a virtual view image in real-time using a graphic processor unit (GPU) for a 3D broadcasting system. Before the transmission, we encode the input 2D+depth video using the H.264 coding standard. At the receiver, we decode the received bitstream and generate a stereo video using a GPU which can compute in parallel. In this paper, we apply a simple and efficient hole filling method to reduce the decoder complexity and reduce hole filling errors. Besides, we design a vertical parallel structure for a forward mapping process to take advantage of the single instruction multiple thread structure of GPU. We also utilize high speed GPU memories to boost the computation speed. As a result, we can generate virtual view images 15 times faster than the case of CPU-based processing.