PPT-Parallel programs Inf-2202 Concurrent and Data-intensive Programming

Fall 2015 Lars Ailo Bongo larsabcsuitno Course topics Parallel programming The parallelization process Optimization of parallel programs Performance analysis Dataintensive computing Parallel programs.  Rick Molloy. . Program Manager. Microsoft Corporation. TL25. Motivation and Hard Problems. Comprehensive Solutions. Native Concurrency Platform in Visual Studio 2010. Concurrency Runtime. Parallel Pattern Library. Yu David Liu. State University of New York (SUNY). at Binghamton. FOAL 2015. Modular Performance Reasoning of. Data-Intensive Programs. Yu David Liu. State University of New York (SUNY). at Binghamton. 15-213 / 18-213: Introduction to Computer Systems. 22. n. d. Lecture, Nov. 11, 2014. Instructors:. . Greg Ganger, . Greg . Kesden. , . and . Dave . O’Hallaron. Client / Server. Session. Reminder: Iterative echo server. Paradigms Activity at Data Intensive Workshop. . Shantenu Jha represented by . Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . . http://salsahpc.indiana.edu. Pavol. . Cerny. . Vasu. Singh . Thomas . Wies. Part III. – Message Passing Concurrency. The Downsides . of Classical Shared Memory Concurrency. shared memory typically implies . physically. Wilfredo. Velazquez. Outline. Basics of Concurrency. Concepts and Terminology. Advantages and Disadvantages. Amdahl’s Law. Synchronization Techniques. Concurrent Data Structures. Parallel Correctness. Concurrent Objects. -. Second Life, Twitter Systems, and . Molecular Dynamics Computing-. Akinori. . Yonezawa. Christmas Lecture. University of Tokyo, December 22, 2009. What is this talk all about?. Yu David Liu. State University of New York (SUNY). at Binghamton. FOAL 2015. Modular Performance Reasoning of. Data-Intensive Programs. Yu David Liu. State University of New York (SUNY). at Binghamton. . Shantenu Jha represented by . Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . . http://salsahpc.indiana.edu/. . Director, Digital Science Center, Pervasive Technology Institute. 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. S Jha. 1. , J Qiu. 2. , A Luckow. 1. , P Mantha. 1. , Geoffrey Fox. 2. 1 . Rutgers http://radical.rutgers.edu. 2 . Indiana . . http. ://. www.infomall.org. http://. arxiv.org. /abs/1403.1528. Dr. Adrienne Freng, University of Wyoming, Department of Criminal Justice. Introduction. Prevention Coalitions. Coordinated Efforts. Definition of “Good” Programs. Efficient Use of Resources. Develop a Plan: STEP 1. . Kartik . Nayak. With Xiao . Shaun . Wang, . Stratis. Ioannidis, Udi . Weinsberg. , Nina Taft, Elaine Shi. 1. 2. Users. Data. Data. Privacy concern!. Data Mining Engine. Data Model. Data Mining on User Data. 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.