PPT-ECE/CS 757: Advanced Computer Architecture II Massively Parallel Processors

Massively Parallel Processors InstructorMikko H Lipasti Spring 2017 University of WisconsinMadison Lecture notes based on slides created by John Shen Mark Hill David Wood Guri Sohi. Instructor:Mikko. H . Lipasti. Spring 2015. University of Wisconsin-Madison. Lecture notes based on slides created by John . Shen. , Mark Hill, David Wood, . Guri. . Sohi. , Jim Smith, Natalie Enright . Instructor:Mikko. H . Lipasti. Spring . 2015. University of Wisconsin-Madison. Lecture notes based on slides created by John . Shen. , Mark Hill, David Wood, . Guri. . Sohi. , Jim Smith, Natalie Enright . Instructor:Mikko. H . Lipasti. Spring . 2015. University of Wisconsin-Madison. Lecture notes based on slides created by John . Shen. , Mark Hill, David Wood, . Guri. . Sohi. , Jim Smith, Natalie Enright . 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. CUDA Lecture 1. Introduction to Massively Parallel Computing. A quiet revolution and potential buildup. Computation: TFLOPs . vs. . 100 GFLOPs. CPU in every PC – massive volume and potential impact. Improving Computer Performance. What performance translates into:. Time taken to do computation. Improving performance . → reducing time taken. What key benefits improving performance can bring:. Can solve “now-computationally-attainable” problems in . Contents . Vector processor. Vector instructions. Vector pipelines. Scalar pipeline execution. Vector pipeline execution. Symbolic processors. Attributes. Characteristics. Vector Processors. A vector processor is specially designed to perform vector computations.. Vasilios Mitrokostas. 2. An introduction to Internet Telephony. How VoIP works. Modern VoIP implementations. VoIP in massively multiplayer online gaming. Final thoughts. 2. Voice over Internet Protocol. Dr Susan Cartwright. Dept of Physics and Astronomy. University of Sheffield. Parallel Universes. Are you unique?. Could there be another “you” differing only in what you had for breakfast this morning?. Instructor:Mikko. H . Lipasti. Spring . 2017. University of Wisconsin-Madison. Lecture notes based on slides created by John . Shen. , Mark Hill, David Wood, . Guri. . Sohi. , Jim Smith, Natalie Enright . Lecture 2. CISC and . Microcoding. Benjamin Lee. Electrical and Computer Engineering. Duke University. www.duke.edu/~bcl15. www.duke.edu/~bcl15/class/class_ece252fall12.html. ECE 552 / CPS 550. 2. Microarchitectures. Lecture 12. Memory – Part 1. Benjamin Lee. Electrical and Computer Engineering. Duke University. www.duke.edu/~bcl15. www.duke.edu/~bcl15/class/class_ece252fall12.html. ECE 552 / CPS 550. 2. ECE552 . Lecture 3. Early Microarchitectures. Benjamin Lee. Electrical and Computer Engineering. Duke University. www.duke.edu/~bcl15. www.duke.edu/~bcl15/class/class_ece252fall12.html. ECE 552 / CPS 550. 2. ECE 552 . Quarter: Summer 2017. CSE 373: Data Structures and Algorithms. Lecture . 23: Parallelism: Map, Reduce, Analysis. Today. More on parallelism. Map & Reduce . Analysis of Efficiency. Reminder: . C. ome visit my office hours to pick up midterm.