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RISS 인기검색어
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- 저자 : 홍진건 ( Jingun Hong ) (검색결과 2 건)
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홍진건 ( Jingun Hong ),정성욱 ( Sungwook Jung ),김정길 ( Cheong Ghil Kim ),( Bernd Burgstaller ) 한국정보처리학회 2011 한국정보처리학회 학술대회논문집 Vol.18 No.1
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.
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박종태 ( Jongtae Park ),( Beron Facchini ),홍진건 ( Jingun Hong ),( Bernd Burgstaller ) 한국정보처리학회 2010 한국정보처리학회 학술대회논문집 Vol.17 No.2
General Purpose Graphic Processing Unit (GPGPU) computing is a technique that utilizes the highperformance many-core processors of high-end graphic cards for general-purpose computations such as 3D graphics, video/image processing, computer vision, scientific computing, HPC and many more. GPGPUs offer a vast amount of raw computing power, but programming is extremely challenging because of hardware idiosyncrasies. The open computing language (OpenCL) has been proposed as a vendor-independent GPGPU programming interface. OpenCL is very close to the hardware and thus does little to increase GPGPU programmability. In this paper we present how a set of digital camera image signal processing (ISP) filters can be realized efficiently on GPGPUs using OpenCL. Although we found ISP filters to be memory-bound computations, our GPGPU implementations achieve speedups of up to a factor of 64.8 over their sequential counterparts. On GPGPUs, our proposed optimizations achieved speedups between 145% and 275% over their baseline GPGPU implementations. Our experiments have been conducted on a Geforce GTX 275; because of OpenCL we expect our optimizations to be applicable to other architectures as well.
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