PPT-CS/EE 217 – GPU Architecture and Parallel Programming

Midterm Review Material on exam Module 110 Module 5 is mixed in there somewhere Chapters 36 8 and 9 Understand the CUDA C programming model Understand the architecture limitations and how to navigate them to improve the performance of your code . Unlike sequential algorithms parallel algorithms cannot be analyzed very well in isolation One of our primary measures of goodness of a parallel system will be its scalability Scalability is the ability of a parallel system to take advantage of incr Parallel programming 5 - 2 arallel Version 1. Our first parallel version simply splits the outer loop: id := process_fork (p); for (i=id; in ; i=i+p) { y [i] = 0; for (j=0; jn Hardware Parallelism. Computing: execute instructions that operate on data.. Flynn’s . taxonomy (Michael Flynn, 1967) classifies computer architectures based on the number of instructions that can be executed and how they operate on data.. Time for Hardware Upgrade. Uzi Vishkin. . . The pompous version. After 40 years of “wandering in the desert”, general-purpose parallelism is very close to capturing the “promised land” of mainstream computing. Anthony Waterman. Topics to Discuss. Are online games . c. onceptually. . p. arallel?. What portions of a game benefit from parallelization?. Graphics Processing Units (GPUs) . General-Purpose . C. omputing . 6/16/2010. Parallel Programming Abstractions. 1. Tasks . vs. Threads. Similar but not the same.. 6/16/2010. Parallel Programming Abstractions. 2. h/w processors. Operating System. T. hreads. Task Scheduler. Async. . and . Await. ”. Joe Hummel, PhD. joe@joehummel.net. Technical Staff: . Pluralsight. Adjunct Professor: UIC, LUC. http://www.joehummel.net/downloads.html. Motivation. Execution model. Parallel programming with Tasks. The Tyranny of. the Storage Hierarchy. Henry Neeman, University of Oklahoma. Charlie Peck, Earlham College. Tuesday October 11 2011. Parallel Programming: . Storage . Hierarchy. OK Supercomputing Symposium, Tue Oct 11 2011. Parallel Processing, Multicores. Prof. Gennady . Pekhimenko. University of Toronto. Fall 2017. The content of this lecture is adapted from the lectures of . Onur. . Mutlu. @ CMU. Summary . Parallelism. modelS. CS 5802 . Monica Borra. Overview. Types of parallel programming models. Shared memory Model. OpenMP. POSIX Threads. Cilk. /. Cilk. Plus/. Cilk. Plus . Plus. Thread Building Blocks. Types of Parallel Programming Models:. Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2014. Lectures. Monday. 6-9pm. Moore 212. Fall. and . Spring. 2012 lectures were recorded. Attendance is required for guest lectures. Image from . 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. Prof. . Smruti. R. . Sarangi. IIT Delhi. 1. What is computer architecture? . The study of computer processors and computer systems. 2. What does a computer do? . Almost everything: . . Financial calculations. Fall 2015. Lars Ailo Bongo (larsab@cs.uit.no). Course topics. Parallel programming. The parallelization process. Optimization of parallel programs. Performance analysis. Data-intensive computing. Parallel programs.