PPT-Parallel Programming With Spark

Author : sherrill-nordquist | Published Date : 2016-07-17

Matei Zaharia UC Berkeley Fast expressive cluster computing system compatible with Apache Hadoop Works with any Hadoopsupported storage system HDFS S3 Avro Improves

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Parallel Programming With Spark: Transcript


Matei Zaharia UC Berkeley Fast expressive cluster computing system compatible with Apache Hadoop Works with any Hadoopsupported storage system HDFS S3 Avro Improves efficiency through. 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 Pro .Net Programming in C#, Adam Freeman. Parallel C# . Task Parallel Library (TPL). TPL tasks vs. Threads. Benefits & Pitfalls of Parallel Programming. Overhead. Coordinating fata. Scaling Applications. 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. Jost Berthold. Simon Marlow. Abyd Al Zain. Kevin Hammond. The Parallel Haskell Landscape. research into parallelism using Haskell has been ongoing since the late 1980s. semi-implicit, deterministic programming model: . 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 . Huang, Ph.D., Professor. Email. :. yhuang@nju.edu.cn. NJU-PASA Lab for Big Data Processing. Department of Computer Science and Technology. Nanjing University. May 29, 2015, India. A Unified Programming Model . Going Beyond Serial MATLAB Applications. MATLAB . Desktop (Client). Worker. Worker. Worker. Worker. Worker. Worker. Programming Parallel Applications (CPU). Built-in support. with t. oolboxes. Ease of Use. 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. 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. David Dayan, Moshe Goldstein, . Elad. . Bussani. Levy, Moshe . Naaman. , . Mor. Nagar, . Ditsa. . Soudry. , Raphael B. . Yehezkael. EuroPython. 2016. 1. agenda. Motivation. Objectives. EFL Programming Model. 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. 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:. 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.

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