PPT-CS 179 Lecture 13

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. 30pm 730pm 730pm 730pm Hold Your Applause Inventing and Reinventing the C lassical Concert Hold Your Applause Inventing and Reinventing the C lassical Concert Hold Your Applause Inventing and Reinventing the C lassical Concert Hold Your Applause I 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. Assembly Language and . Arduino. Behind the C code (or sketch). C provides a somewhat human-readable interface. but it gets . compiled. into machine instruction set. ultimately just binary (or hex) instructions loaded into the . Unstressed Stressed iambsactors 179(85) 253(114)iambssingers 223(98) 326(125)trocheesactors 179(81) 237(110)trocheessinger 212(84) 303(107)Table2:Shownarethemeanrelativedurationsforbothspeakergroupsan 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 . 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.. Host-Device Data Transfer. 1. Moving data is slow. So far we’ve 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. 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 17940E17940E17930E17930E17920E17920E17910E17910E17900E17900E16500S16500S16510S16510S16520S16520S16530S16530S16540S16540S16550S16550S16560S16560SFIJITropical CycloneTC20201215FJIFIJISuvaThe depiction a SudarshanGuptaKiranArora*,** *AssistantProfessor,DepartmentofAnatomy,GMERSMedicalCollege,Valsad**Assistantprofessor,DepartmentofAnatomy,GCSMedicalCollege,AhmedabadCorrespondence:e-mail:drsudarshangupt Recap. Some algorithms are “less obviously parallelizable”:. Reduction. Sorts. FFT (and certain recursive algorithms). Parallel FFT structure (radix-2). Bit-reversed access. http://staff.ustc.edu.cn/~csli/graduate/algorithms/book6/chap32.htm. 2. Workgroup. Co-Chair. Co-Chair. Next Meeting. Sociotechnical Infrastructure . Mark. Ackerman. Mike. Klinkman. 10/17/17. 9:00. – 10:00 a.m.. THSL, Room 5000. Ethical, Legal and Social Policy. Jody Platt.