Ultra high definition (UHD) game scenes have caused the memory bandwidth problem. The lossless DPCM-GR based compression algorithm [12] using NVIDIA CUDA(Compute Unified Device Architecture) like general purpose GPU (GPGPU) computing relieves the bandwidth problem without sacrificing image quality, which supports bit parallel pipelining. This paper increases the memory bandwidth efficiency using the shared memory of CUDA based on the compression algorithm [12]. Also, various asynchronous transfer configurations which can overlap the kernel execution and data transfer between the Host and the CUDA device are implemented with the page-locked host memory. Experimental results show that GPGPU CUDA computing obtains the maximum 87.5 and 30.6 times speedups for GTX650Ti and GT330, respectively, comparing to Host CPU. Also, the maximum reductions of the compression time for GTX650Ti and GT330 are 54.1% and 30.3%, respectively, among various concurrency transfer configurations.
목차
Abstract 1. Introduction 2. Related Works for Lossless Image Compression 3. DPCM-GR Lossless Image Compression 4. Asynchronous Compression Using GPGPU CUDA 5. Performance Evaluation 5. Conclusion References
보안공학연구지원센터(IJMUE) [Science & Engineering Research Support Center, Republic of Korea(IJMUE)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Multimedia and Ubiquitous Engineering
간기
월간
pISSN
1975-0080
수록기간
2008~2016
등재여부
SCOPUS
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Multimedia and Ubiquitous Engineering Vol.11 No.12