PPT-Why GPU Computing GPU CPU

Add GPUs Accelerate Science Applications NVIDIA 2013 Small Changes Big Speedup Application Code GPU C PU Use GPU to Parallelize ComputeIntensive Functions Rest of Sequential CPU Code. ITS Research Computing. Mark Reed . Objectives. Learn why computing with accelerators is important. Understand accelerator hardware. Learn what types of problems are suitable for accelerators. Survey the programming models available. Computing Platform. Publication:. Ra . Inta. , David J. Bowman, and Susan M. Scott. . Int. J. . Reconfig. . . Comput. . 2012, . Article . 2 (January 2012), 1 pages. . DOI=10.1155/2012/241439.  . Naveen R. Iyer Kowshick . 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. OpenCL. Programs on Multiple Heterogeneous Devices. Prasanna Pandit. R. . Govindarajan. Supercomputer Education and Research Centre. Indian Institute of Science, Bangalore. CGO—2014. Outline. Introduction. ITS Research Computing. Lani. Clough, Mark Reed. markreed@unc.edu. . Objectives. Introductory. level MATLAB course for people who want to learn . parallel and GPU computing . in MATLAB.. Help participants . Single-chip Heterogeneous Processors. Euijin Kwon. 1,2. Jae Young Jang. 2. Jae W. Lee. 2. Nam Sung Kim. 2,3. 1. 2. 3. Single-chip heterogeneous processors. 2. Compared to systems based on discrete components. 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. Betkaoui, B.; Thomas, D.B.; Luk, W., "Comparing performance and energy efficiency of FPGAs and GPUs for high productivity computing," . Field-Programmable Technology (FPT), 2010 International Conference on. on Heterogeneous . Architectures. Jin . Wang. †. , Norman Rubin. ‡*. , . Haicheng. Wu. †. , . Sudhakar. . Yalamanchili. †. † Georgia Institute of Technology. ‡ . AMD. * The author is now affiliated with NVIDIA Research. Department of Geography and Planning. University at Albany. What is a GPU?. A GPU is a . graphics processing unit. Modern GPUs are composed of multiple processors. Each of these processors can perform operations similar to those of CPUs. ITS Research Computing. Mark Reed . Objectives. Learn why computing with accelerators is important. Understand accelerator hardware. Learn what types of problems are suitable for accelerators. Survey the programming models available. 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. Cooperative Computing. Di Wu, Fan Zhang, . Naiyong. . Ao. , Gang Wang, . Xiaoguang. Liu, Jing Liu. Nankai. -Baidu Joint Lab, . Nankai. University. Introduction. Cooperative Model. GPU Batching Algorithm. Jerry Adams. 1. , Bradley Hittle. 2. , Eliot Prokop. 3. , . Ronny Antequera. 3. , Dr.Prasad Calyam. 3. University of Hawaii-West Oahu. 1. , . The . Ohio State University. 2. , University of Missouri-Columbia.