請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/61412完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉邦鋒 | |
| dc.contributor.author | Chung-Yao Kao | en |
| dc.contributor.author | 高崇堯 | zh_TW |
| dc.date.accessioned | 2021-06-16T13:02:30Z | - |
| dc.date.available | 2016-08-17 | |
| dc.date.copyright | 2013-08-17 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-06 | |
| dc.identifier.citation | Bibliography
[1] Amazon elastic compute cloud. http://aws.amazon.com/ec2/. [2] Amazon high performance computing on cloud. http://aws.amazon.com/hpc-applications/. [3] Amd. http://www.amd.com/. [4] Amd app acceleration. http://www.amd.com/stream. [5] J.A. Anderson, C.D. Lorenz, and A. Travesset. General purpose molecular dynamics simulations fully implemented on graphics processing units. In Journal of Computational Physics, 2008, pages 5342–5359, February 2008. [6] S. Asano, T. Maruyama, and Y. Yamaguchi. Performance comparison of fpga, gpu and cpu in image processing. In Field Programmable Logic and Applications, 2009. FPL 2009. International Conference on, pages 126 –131, 31 2009-sept. 2 2009. [7] I. Buck, T. Foley, D. Horn, J. Sugerman, K. Fatahalian, M. Houston, and P.Hanrahan. Brook for gpus: Stream computing on graphics hardware. In ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH 2004, pages 777–786, August 2004. [8] G. Chen, G. Li, S. Pei, and B. Wu. Gpgpu supported cooperative acceleration in molecular dynamics. In Computer Supported Cooperative Work in Design (CSCWD), 2009. 13th International Conference on, pages 113–118, April 2009. [9] Cuda. http://www.nvidia.com/content/cuda/cuda-toolkit.html. [10] G. Giunta, R. Montella, G. Agrillo, and G. Coviello. A gpgpu transparent virtualization component for high performance computing clouds. In Euro-Par 2010 Parallel Processing, pages 379–391, September 2010. [11] Google cloud platform. http://cloud.google.com/. [12] Gpgpu. http://gpgpu.org/. [13] Hoopoe. http://www.hoopoe-cloud.com/. [14] Ibm. http://www.ibm.com/. [15] Intel. http://www.intel.com/. [16] Json. http://www.json.org/. [17] Kepler, next generation cuda compute architecture. http://www.nvidia.com.tw/content/PDF/kepler/NVIDIA-Kepler-GK110-Architecture- Whitepaper.pdf. [18] Kvm. http://www.linux-kvm.org/. [19] Network file system. http://nfs.sourceforge.net/. [20] Nvidia. http://www.nvidia.com/. [21] Nvidia fermi architecture. http://tinyurl.com/6vdsl4q. [22] Opencl. http://www.khronos.org/opencl/. [23] Shane Ryoo, Christopher I. Rodrigues, Sara S. Baghsorkhi, Sam S. Stone, David B. Kirk, and Wen-mei W. Hwu. Optimization principles and application performance evaluation of a multithreaded gpu using cuda. In Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming, PPoPP ’08, pages 73–82, New York, NY, USA, 2008. ACM. [24] L. Shi, H. Chen, and J. Sun. vcuda: Gpu-accelerated high-performance computing in virtual machines. In IEEE International Symposium on Parallel & Distributed Processing, pages 1–11, May 2009. [25] Using inline ptx assembly in cuda. http://tinyurl.com/866zchu. [26] Nvidia vgx. http://www.nvidia.com/object/grid-vgx-software.html. [27] Vincent A. Voelz, Gregory R. Bowman, Kyle Beauchamp, and Vijay S. Pande. Molecular simulation of ab initio protein folding for a millisecond folder ntl9(1-39). Journal of the American Chemical Society, 132(5):1526–1528, 2010. PMID: 20070076. [28] Windows azure, microsoft’s cloud platform. http://www.windowsazure.com/. [29] Xen. http://xen.org. [30] C.Y. Yeh, C.Y. Kao, W.S. Hung, C.C. Lin, P. Liu, J.J. Wu, and K.C. Liu. Gpu virtualization support in cloud system. In 8th International Conference, GPC 2013 and Colocated Workshops, Seoul, Korea, May 9-11, 2013. Proceedings, pages 423–432, May 2013. [31] zillians. http://www.zillians.com/. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/61412 | - |
| dc.description.abstract | Recently NVidia proposed their first cloud based GPU architecture called Kepler. One of the key features of Kepler GPU is that it allows multiple user processes to access the GPU {em concurrently}. We use this feature to design a cloud computing system that allow multiple users to share the computing power of a Kepler board anytime, anywhere over the internet. Our system improves the utilization of the Kepler GPU and lowers the cost in providing GPU cloud services. We conduct experiments to evaluate the overhead of our system, and preliminary results indicatse that our system provides convienent services with very little overhead. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T13:02:30Z (GMT). No. of bitstreams: 1 ntu-102-R00922057-1.pdf: 1186268 bytes, checksum: 4ae111008fe3219355f9d6e9202ac301 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | Contents
Acknowledgement i Chinese Abstract ii Abstract iii 1 Introduction 1 2 Related works 4 3 System Architecture 9 3.1 GPU Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.1.1 Listener . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.2 Code Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.3 Resource Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.4 Executor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Client Control Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2.1 Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2.2 Server-Client Communication API . . . . . . . . . . . . . . . . . . . . . 11 3.3 System Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4 System implementation 14 4.1 GPU server implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.1.1 Listener . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.1.2 Code Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.1.3 Resource Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.1.4 Executor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.2 Client Control Interface implementation . . . . . . . . . . . . . . . . . . . . . . 15 4.2.1 Server-Client Communication API . . . . . . . . . . . . . . . . . . . . . 15 4.2.2 Coverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 5 Experiments 17 5.1 Experiment surrouding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2 Resource sharing model experiments . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2.1 Comparison between Kepler and 9800GT on multiple program launches . 17 5.2.2 Resouce sharing model running multiple programs . . . . . . . . . . . . 19 5.2.3 More multiple program launching test . . . . . . . . . . . . . . . . . . . 19 5.3 System overhead measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5.3.1 Shared file system overhead . . . . . . . . . . . . . . . . . . . . . . . . 20 5.3.2 Code generation overhead . . . . . . . . . . . . . . . . . . . . . . . . . 21 5.3.3 Compilation overhead . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 5.3.4 Execution overhead . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 5.3.5 Overall overhead . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 6 Conclusion and Future Works 23 | |
| dc.language.iso | en | |
| dc.subject | 雲端運算 | zh_TW |
| dc.subject | 圖形顯示晶片 | zh_TW |
| dc.subject | 通用圖形顯示晶片 | zh_TW |
| dc.subject | 圖形顯示晶片虛擬化 | zh_TW |
| dc.subject | 虛擬機器 | zh_TW |
| dc.subject | GPU | en |
| dc.subject | GPGPU | en |
| dc.subject | GPU Virtualization | en |
| dc.subject | Virtual Machines | en |
| dc.subject | Cloud Computing | en |
| dc.title | 使用nVidia Kepler 圖形顯示處理器建造通用圖形處理器虛擬化系統 | zh_TW |
| dc.title | GPGPU Virtualization System Using NVidia Kepler Series GPU | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 吳真貞,洪士灝 | |
| dc.subject.keyword | 雲端運算,圖形顯示晶片,通用圖形顯示晶片,圖形顯示晶片虛擬化,虛擬機器, | zh_TW |
| dc.subject.keyword | Cloud Computing,GPU,GPGPU,GPU Virtualization,Virtual Machines, | en |
| dc.relation.page | 25 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-08-06 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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|---|---|---|---|
| ntu-102-1.pdf 未授權公開取用 | 1.16 MB | Adobe PDF |
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