PPT-Modern GPU Architectures

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Varun Sampath University of Pennsylvania CIS 565 Spring 2012 AgendaGPU Decoder Ring Fermi GF100 GeForce GTX 480 Fermi Refined GF110 GeForce GTX 580 Little Fermi

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Modern GPU Architectures: Transcript

Varun Sampath University of Pennsylvania CIS 565 Spring 2012 AgendaGPU Decoder Ring Fermi GF100 GeForce GTX 480 Fermi Refined GF110 GeForce GTX 580 Little Fermi GF104 GeForce GTX 460. . 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. Goals for Rest of Course. Learn how to program massively parallel processors and achieve. high performance. functionality and maintainability. scalability across future generations. Acquire technical knowledge required to achieve the above goals. ITK v4 . . w. inter . meeting. Feb 2. nd. 2011. Won-. Ki. . Jeong. , . Harvard University. (. wkjeong@seas.harvard.edu. ). Overview. Introduction. Current status in GPU ITK v4. GPU managers. GPU image. 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. Software Architecture. Describe how the various software components are to be organized and how they should interact.. It describe the organization and interaction of software components.. System Architecture. Sathish. . Vadhiyar. Parallel Programming. GPU. Graphical Processing Unit. A single GPU consists of large number of cores – hundreds of cores.. Whereas a single CPU can consist of 2, 4, 8 or 12 cores. M. Zollhöfer, E. Sert, G. Greiner and J. Süßmuth. Computer Graphics Group, University Erlangen-Nuremberg, Germany. Motivation/Requirements. Intuitive modeling. Handle-based. Direct manipulation. 2. on Heterogeneous . Architectures. Jin . Wang. †. , Norman Rubin. ‡*. , . Haicheng. Wu. †. , . Sudhakar. . Yalamanchili. †. † Georgia Institute of Technology. ‡ . AMD. * The author is now affiliated with NVIDIA Research. 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 . . Installation. CS5100 Advanced . Computer Architecture. Introduction. . of. . Gem5-GPU. It. . merges . 2 popular simulators: gem5 and . gpgpu. -sim. Simulates . CPUs, GPUs, and the interactions between . Samuel Naffziger. AMD Corporate Fellow . June 14. th. , 2011. VLSI Technology Symposium 2011. Introduction. The new workloads and demands on computation. Characteristics of serial and parallel computation. Phitchaya. . Mangpo. . Phothilimthana. Jason . Ansel. Jonathan . Ragan-Kelley. Saman. . Amarasinghe. Computer Science and Artificial Intelligence Laboratory. Massachusetts Institute of Technology. Programming, Part 3. -- Streaming, Library and Tuning. CSCE 790: Parallel Programming Models for Multicore and . Manycore. Processors. Department of Computer Science and Engineering. Yonghong. Yan.

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