PPT-CSE332: Data Abstractions

Lecture 2 Math Review Algorithm Analysis Dan Grossman Spring 2010 Announcements Project 1 posted Section materials on using Eclipse will be very useful if you have never used it Could also start in a different environment if necessary. Introduction Softwarede64257ned networking SDN has received a lot of attention in recent years as a means of addressing some of the longstanding challenges in networking SDN starts from two simple ideas i generalize network hardware so it provide 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. Chris Rossbach, Microsoft Research. Jon Currey, Microsoft Research. Emmett . Witchel. , University of Texas at Austin. HotOS. 2011. Lots of GPUs. Must they be so hard to use?. We need dataflow…. GPU Haiku . Lecture 5: Binary Heaps, Continued. Tyler Robison. Summer 2010. 1. Review. 2. Priority Queue ADT: . insert. comparable object, . deleteMin. Binary heap data structure: Complete binary tree where each node has a lesser priority than its parent (greater value). Lecture 21: Amortized Analysis. Dan Grossman. Spring 2010. Amortized . Recall our plain-old stack implemented as an array that doubles its size if it runs out of room. How can we claim . push. is . O. 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. . Chapter 1 — Computer Abstractions and Technology — . 2. Classes of Computers. Personal computers. General purpose, variety of software. Subject to cost/performance tradeoff. Server computers. Network based. Graphs and Graph Traversals. Kate Deibel. Summer 2012. July 25, 2012. CSE 332 Data Abstractions, Summer 2012. 1. Last Time. We introduced the idea of graphs and their associated terminology. Key terms included:. Cynthia Lee. CS106B. Today’s Topics. Quick final exam discussion. Details/logistics, topics, sources for practice problems. Quarter wrap-up. Putting it all together: what have we accomplished together this quarter?. 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 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. 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. Lecture . 13: . Introduction to Graphs. Dan Grossman. Fall 2013. Graphs. A graph is a formalism for representing relationships among items. Very general definition because very general concept. A . graph.