PPT-CS 179 Lecture 13

Author : alexa-scheidler | Published Date : 2017-10-25

HostDevice Data Transfer 1 Moving data is slow So far weve only considered performance when the data is already on the GPU This neglects the slowest part of GPU

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CS 179 Lecture 13: Transcript

HostDevice Data Transfer 1 Moving data is slow So far weve only considered performance when the data is already on the GPU This neglects the slowest part of GPU programming getting data on and off of GPU. 22x1 lecture 14x1 lecture 14UIC UIC BioSBioS 101 Nyberg101 NybergReading AssignmentReading Assignment Chapter 12, study the figures and Chapter 12, study the figures and understand the color coding.un for the Teenage Soul. Stories of . Life, Love, and Learning. Created and Compiled by Sally C. Shoemaker. Vocabulary Lists. Vocabulary List #1. raggedy . (. Literature . p. 300). vulnerable - 147. spontaneous - 150. and Brian Voigt © 2011, . except where noted. Lecture 6:. Introduction to Projections and Coordinate Systems. By Austin Troy and Brian Voigt, University of Vermont,. with sections adapted from ESRI’s online course on projections. Troy, Brian Voigt and . Weiqi. Zhou . except where noted © . 2011. Lecture 5:. Introduction to Raster . Analysis. ------Using GIS--. By Austin . Troy, Brian Voigt . and . Weiqi. . Zhou. University . Slide . 1. Intelligent Systems (AI-2). Computer Science . cpsc422. , Lecture . 11. Oct, 2, . 2015. 422 . big . picture: Where are we?. Query. Planning. Deterministic. Stochastic. Value Iteration. Approx. Inference. More than Just Capturing Lectures. Bill King. What is Lecture Capture?. Lecture Capture is the use of a set of technologies to capture and then enable time-shifted and place-shifted viewing of the Lecture Content.. Lecture 10:. Part 1: GPS. Part 2: Interpolation and . geostatistics. By Austin Troy. ------Using GIS--. Lecture Materials by Austin Troy, Weiqi Zhou and Jarlath O’Neil Dunne© 2008. Part 1:. Global Positioning System. Goals of Weeks 5-6. What is machine learning (ML) and when is it useful?. Intro to major techniques and applications. Give examples. How can CUDA help?. Departure from usual pattern: we will give the application first, and the CUDA later. CS 179: GPU Programming Lecture 7 Last Week Memory optimizations using different GPU caches Atomic operations Synchronization with __ syncthreads () Week 3 Advanced GPU-accelerable algorithms “Reductions” to parallelize problems that don’t seem intuitively parallelizable 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) Profiling, AWS Cluster. Synchronization. Ideal case for parallelism: . no resources shared between threads. no communication between threads. . Many algorithms that require just a little bit of resource sharing can still be accelerated by massive parallelism of GPU. 17940E17940E17930E17930E17920E17920E17910E17910E17900E17900E16500S16500S16510S16510S16520S16520S16530S16530S16540S16540S16550S16550S16560S16560SFIJITropical CycloneTC20201215FJIFIJISuvaThe depiction a A0B3670C0D6/ SudarshanGuptaKiranArora*,** *AssistantProfessor,DepartmentofAnatomy,GMERSMedicalCollege,Valsad**Assistantprofessor,DepartmentofAnatomy,GCSMedicalCollege,AhmedabadCorrespondence:e-mail:drsudarshangupt

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