PPT-Parallelizing and Optimizing Programs for GPU Acceleration

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Parallelizing and Optimizing Programs for GPU Acceleration: Transcript

Martin Burtscher Department of Computer Science CUDA Optimization Tutorial Martin Burtscher burtschertxstateedu httpwwwcstxstateeduburtscher Tutorial slides httpwwwcstxstateeduburtschertutorialsCOT5slidespptx. 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 . 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. 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. : Exploiting Parallelism To Scale Software Routers. Mihai . Dobrescu. . et al.. SOSP 2009 Best Paper. Presentor. : Hun . Namkung. Contents. Introduction. Parallelizing Across Servers. Parallelizing Within Servers. . 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 . Add GPUs: Accelerate Science Applications. © NVIDIA 2013. Small Changes, Big Speed-up. Application Code. . GPU. C. PU. Use GPU to Parallelize. Compute-Intensive Functions. Rest of Sequential. CPU Code. MSU. Echo . Wu, Ph.D. . 1. 2. Outline. What is Enrichment?. Process, Content & Product. Strategies for Enrichment. Within & Out-of-School Enrichment. What is Acceleration?. Strategies for Enrichment. CS 179: GPU Programming Lecture 7 Week 3 Goals: Advanced GPU- accelerable algorithms CUDA libraries and tools This Lecture GPU- accelerable algorithms: Reduction Prefix sum Stream compaction Sorting (quicksort) Acceleration is a change in velocity. Speed or direction. Centripetal Acceleration. Acceleration can mean a change in direction, not just speed. Centripetal acceleration. Acceleration that occurs in a circular motion. Acceleration is a measure of . the change in velocity per unit of time. When the velocity of an object is changing, it is . accelerating. Negative acceleration: . slowing down. Positive acceleration: . . SYFTET. Göteborgs universitet ska skapa en modern, lättanvänd och . effektiv webbmiljö med fokus på användarnas förväntningar.. 1. ETT UNIVERSITET – EN GEMENSAM WEBB. Innehåll som är intressant för de prioriterade målgrupperna samlas på ett ställe till exempel:. Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2013. Lectures. Monday and Wednesday. 6-7:30pm. Towne . 307. Fall. and . Spring. 2012 lectures were recorded. Attendance is required for guest lectures. Mihai . Dobrescu. . et al.. SOSP 2009 Best Paper. Presentor. : Hun . Namkung. Contents. Introduction. Parallelizing Across Servers. Parallelizing Within Servers. Evaluation. Conclusion. HW Router. RouteBricks.

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