PPT-CPU/GPU

を協調利用する ソフトウェア開発環境 GPGPU 研究会 佐藤 功人 1 滝沢 寛之 1 小林 広明 2 １ 東北大学大学院情報科学研究科. . Acknowledgement: the lecture materials are based on the materials in NVIDIA teaching center CUDA course materials, including materials from Wisconsin (. Negrut. ), North Carolina Charlotte (. Wikinson. Dr A . Sahu. Dept of Comp Sc & . Engg. . . IIT . Guwahati. 1. Outline. Graphics System . GPU Architecture. Memory Model. Vertex Buffer, Texture buffer. GPU Programming Model. DirectX. , OpenGL, . Mehrzad . Samadi . and . Scott Mahlke. University of Michigan. March 2014. Compilers creating custom processors. University of Michigan. Electrical Engineering and Computer Science. Output Quality Monitoring. Alan . Gray. EPCC . The University of Edinburgh. Outline. Why do we want/need accelerators such as GPUs?. Architectural reasons for accelerator performance advantages . Latest accelerator Products. NVIDIA and AMD GPUs. Johan Andersson – technical director. Frostbite. Electronic arts. Simplify advanced development. . Improve performance . Enable developers to innovate . Challenge the status quo. Mantle?. Control. on Heterogeneous . Architectures. Jin . Wang. †. , Norman Rubin. ‡*. , . Haicheng. Wu. †. , . Sudhakar. . Yalamanchili. †. † Georgia Institute of Technology. ‡ . AMD. * The author is now affiliated with NVIDIA Research. Rajat Phull, . Srihari. Cadambi, Nishkam Ravi and Srimat Chakradhar. NEC Laboratories America. Princeton, New Jersey, USA.. www.nec-labs.com. OpenFOAM Overview. OpenFOAM stands for:. ‘. O. pen . F. Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2014. Acknowledgements. CPU slides – Varun Sampath, NVIDIA. GPU . slides. Kayvon . Fatahalian. , CMU. Mike Houston, . NVIDIA. CPU and GPU Trends. Andy Luedke. Halo Development Team. Microsoft Game Studios. Why do Histogram Analysis?. Dynamically adjust post-processing settings based on rendered scene content. Drive tone adjustments by discovering intensity levels and adjusting . Alex Wade. CAP6938 Final Project. Introduction. GPU based implementation of . A Computational Approach to Edge Detection. by John Canny. Paper presents an accurate, localized edge detection method. Purpose. Hui. Li. Geoffrey Fox. Research Goal. provide . a uniform . MapReduce programming . model that works . on HPC . Clusters or . Virtual Clusters cores . on traditional Intel architecture chip, cores on . Jia. Pan and Dinesh Manocha. University . of North Carolina, Chapel Hill, USA. http://gamma.cs.unc.edu/gplanner. Presenter: . Liangjun. Zhang, Stanford University. Real-time Motion Planning. Dynamic/uncertain/deformable environments. Scientific Computing and Visualization. Boston . University. GPU Programming. GPU – graphics processing unit. Originally designed as a graphics processor. Nvidia's. GeForce 256 (1999) – first GPU. Current Goal(s):. Generate . stacktraces. of GPU executions and associate GPU call chains with CPU call graphs. Particular interest on how to determine call chains when in-lined GPU functions are used.