PPT-Friday, April 14, 2017 Programming Abstractions

Spring 2017 Stanford University Computer Science Department Lecturer Chris Gregg reading Programming Abstractions in C Chapter 5456 CS 106B Lecture 6 Sets and Maps b d c a e f. 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 Matei Zaharia, . Benjamin . Hindman. , Andy . Konwinski. , Ali . Ghodsi. , Anthony Joseph, Randy Katz, Scott . Shenker. , Ion . Stoica. Background. Clusters of commodity servers . have become a . major computing . Watercolor Paint - Abstractions. Watercolor Paint – Non-Representational. Pastel Abstractions. 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. 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. . Black Friday. For retailers . – Black Friday is a big shopping day. There are special deals – discounts – limited quantities. Black. = accounting term – black means making a profit; red – means losing money. 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 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. Spring 2018. Stanford University . Computer Science Department. Lecturer: Chris Gregg. reading:. Programming Abstractions in C++, Chapter 5.4-5.6. CS 106B. Lecture 6: Sets and Maps. b. d. c. a. e. f. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand