PPT-Finding Optimal Program Abstractions

Mayur Naik Georgia Tech Xin Zhang Georgia Tech Hongseok Yang Oxford Percy Liang Stanford Mooly Sagiv TelAviv U J oint work with Static Analysis 70s to 90s. 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 Korf Computer Science Departmen Univ ersit y of California Los Angeles Los Angeles Ca 90095 Korfcsuclaedu Abstract eha e found the rst optimal solutions to random instances of Rubiks Cub e The median optimal so lution length app ears to b e 18 mo es Xin Zhang. Georgia Tech. Mayur. . Naik. Georgia Tech. Hongseok. Yang. University of Oxford. A Key Challenge for Static Analysis. Precision. Scalability. Our setting. Query q. Program p. Static Analysis S. Francesco Amigoni, Nicola Basilico, Nicola Gatti. {amigoni,basilico,ngatti}@elet.polimi.it. Robotic Patrolling. €. €. €. €. A . p. atrolling. . s. trategy. . determines. the . path. . followed. 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. . 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. 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. 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.