PPT-Lecture 2: Parallel Reduction Algorithms & Their Analys

Shantanu Dutt Univ of Illinois at Chicago An example of an SPMD messagepassing parallel program 2 SPMD messagepassing parallel program contd 3 node xor D 1 Reduction Computations amp Their Parallelization. We then consider the complications introduced by the introduction of parallelism and look at some proposed parallel frameworks Analysing Sequential Algorithms The design and analysis of sequential algorithms is a well developed 64257eld with a large 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.. Roselyn. . Sands. Thomas . McCabe. 1. Context van de opdracht. 2. (. Remember. Darwin?). Survival. of the . Fittest. :. How « fit » are . you. ?. Do . you. « . work. smart »:. Right . person. Andrew Lumsdaine. Indiana University. lums@osl.iu.edu. My Goal in Life. Performance with elegance. Introduction. Overview of our high-performance, industrial strength, graph library. Comprehensive features. Programming & Cluster Computing. Applications and. Types of Parallelism. Joshua Alexander, U Oklahoma. Ivan . Babic. , Earlham College. Michial. Green, Contra Costa College. Mobeen. . Ludin. , Earlham College. Anjul Patney, Stanley Tzeng, and John D. Owens. University of California, Davis. Parallelism in Interactive Graphics. Well-expressed in hardware as well as APIs. Consistently growing in degree & expression. Applications and. Types of Parallelism. Henry Neeman, Director. OU Supercomputing Center for Education & Research. University of Oklahoma Information Technology. Oklahoma Supercomputing Symposium, Tue Oct . CompSci. 590.03. Instructor: . Ashwin. . Machanavajjhala. 1. Lecture 3 : 590.03 Fall 12. Announcements. Project ideas are posted on the site. . You are welcome to send me (or talk to me about) your own ideas.. Dr. Yingwu Zhu. Chapter 27. Motivation. We have discussed . serial algorithms. that are suitable for running on a . uniprocessor. computer. We will now extend our model to . parallel algorithms. that can run on a . . 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. Lecture . 17: More . Dijkstra. ’s. and. Minimum Spanning Trees. Aaron Bauer. Winter 2014. Dijkstra’s. Algorithm: Idea. Winter 2014. 2. CSE373: Data Structures & Algorithms. Initially, start node has cost 0 and all other nodes have cost . Lecture . 18: Comparison Sorting. Dan Grossman. Fall 2013. Introduction to Sorting. Stacks. , queues, priority queues, and dictionaries . all . focused on providing one element at a time. But often we know we want “all the things” in some order. BIOINFORMATICS: DEFINATIONS. http://www.ittc.ku.edu/bioinfo_seminar/images/wheel.gif. WHY WE SELECTED THESE PAPERS? . What is Gene Expression?. It is the process by which information from a gene is used in the synthesis of a functional gene product. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand