PPT-Workflows and Abstractions for Map-Reduce

1 Recap Mapreduce Algorithms with multiple mapreduce steps Naïve bayes test routine for large datasets and large models Cleanly describing these algorithms workflow or dataflow languages . All Programmable Abstractions push beyond traditional RTL design methodologies to automate all aspects of system development and algorithm deployment into all programmable FPGAs SoC and 3D ICs Xilinx and its Alliance members are working together to 1. Abstractions On Top Of . Hadoop. We’ve decomposed some algorithms into a map-reduce “workflow” (series of map-reduce steps). naive Bayes training. naïve Bayes testing. phrase scoring. How else can we express these sorts of computations? Are there some common special cases of map-reduce steps we can parameterize and reuse?. Taverna. Aleksandra . Pawlik. University . of Manchester. materials by . Dr Katy . Wolstencroft. . and Dr . Aleksandra . Pawlik. http. ://www.taverna.org.uk/. This work is licensed under a . Creative Commons Attribution 3.0 Unported License. 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. Dictionary ADT. : Arrays, Lists and . Trees. Kate Deibel. Summer 2012. June 27, 2012. CSE 332 Data Abstractions, Summer 2012. 1. Where We Are. Studying the absolutely essential ADTs of computer science and classic data structures for implementing them. Disjoint Set Union-Find . and . Minimum Spanning Trees. Kate Deibel. Summer 2012. August 13, 2012. CSE 332 Data Abstractions, Summer 2012. 1. Making Connections. You have a set of nodes (numbered 1-9) on a network. . CSE 332 Data Abstractions: A Heterozygous Forest of AVL, Splay, and B Trees Kate Deibel Summer 2012 July 2, 2012 CSE 332 Data Abstractions, Summer 2012 1 From last time… Binary search trees can give us great performance due to providing a structured binary search. Lecture 19: Analysis of Fork-Join Parallel Programs. Dan Grossman. Spring 2010. Where are we. Done:. How to use . fork. , and . join. to write a parallel algorithm. Why using divide-and-conquer with lots of small tasks is best. Lecture 6: Dictionaries; Binary Search Trees. Dan Grossman. Spring 2010. Where we are. Studying the absolutely essential ADTs of computer science and classic data structures for implementing them. ADTs so far:. Lecture 9: B Trees. Dan Grossman. Spring 2010. Our goal. Problem: A dictionary with so much data most of it is on disk. Desire: A balanced tree (logarithmic height) that is even shallower than AVL trees so that we can minimize disk accesses and exploit disk-block size. Lecture 15: Introduction to Graphs. Dan Grossman. Spring 2010. Graphs. A graph is a formalism for representing relationships among items. Very general definition because very general concept. A . graph. HUBzero. : How to Use Pegasus to Execute Computational Pipelines. Ewa Deelman. USC . Information Sciences Institute. Acknowledgement: . Steven Clark, Derrick Kearney, Michael McLennan (. HUBzero. ) . Dictionary ADT. : Arrays, Lists and . Trees. Kate Deibel. Summer 2012. June 27, 2012. CSE 332 Data Abstractions, Summer 2012. 1. Where We Are. Studying the absolutely essential ADTs of computer science and classic data structures for implementing them. Laboratory of Mathematical Chemistry,. Bourgas. University “Prof. . Assen. . Zlatarov. ”. Bulgaria. Outlook. 2. Endpoints. Specificities. Components. Executing module. Algorithm of . Ecotoxicological.