PDF-[DOWLOAD]-Programming Massively Parallel Processors: A Hands-on Approach

The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand. vs. Sequential Algorithms. Design of efficient algorithms. A parallel computer is of little use unless efficient parallel algorithms are available.. The issue in designing parallel algorithms are very different from those in designing their sequential counterparts.. 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 . 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. Part 2. Tyler Patton. Discussion:. Chess Engine Basics. Everything Parallel. What Next?. Background. : Scope. First estimate of the number of positions:. 64! / 32!*(8!). 2. *(2!). 6. =10. 43. . (Shannon) . Tyler Patton. Discussion:. Background. Sequential Optimizations. Parallelization of chess. Backgroun. d. : What is Chess?. Strategic 2 player game. 64 tiles. 16 pieces per player. Objective to capture the. Computing. Jie Liu. , . Ph.D.. Professor. Computer Science Division. Western Oregon University. Monmouth, Oregon, USA. liuj@wou.edu. outline. The . fastest computers. The PRAM model. The O(1) algorithm that finds the max. 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. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand 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. Massively Parallel Processors. 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.