PPT-Finding Optimum Abstractions in Parametric Dataflow Analysis

Finding Optimum Abstractions in Parametric Dataflow Analysis Xin Zhang Georgia Tech Mayur Naik Georgia Tech Hongseok Yang University of Oxford A Key Challenge for Static Analysis Precision Scalability. (and the Naiad system). Frank McSherry. , Derek G. Murray,. Rebecca Isaacs, . Michael Isard. Microsoft Research, Silicon Valley. Data-parallel dataflow. 1. 2. 3. 4. 5. 1. 4. 2. 3. 6. 6. 5. A. B. C. D. Watercolor Paint - Abstractions. Watercolor Paint – Non-Representational. Pastel Abstractions. Unit 3. What are . parametrics. ?. Normally we define functions in terms of . one . variable. – . for example, . y . as a function of x. . . Suppose in a graph, each (x, y) depended on a . third variable . 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. Eurocrypt. May 1. st. , 2017. Rafael Pass, Elaine Shi, . Florian Tramèr. Trusted hardware: . Different . communities. , different . world views. Crypto. Architecture. Systems. & . Security. “Minimal” trusted . 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. Introduction to Engineering Design. Parameters. 3D CAD programs use . parameters. to define a model of a design . solution. A parameter is . a property of a system whose value determines how the . system will . 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 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. Shail Dave. 1. , . Youngbin. Kim. 2. , . Sasikanth. Avancha. 3,. Kyoungwoo. Lee. 2. , Aviral Shrivastava. 1. [1] Compiler Microarchitecture Lab, Arizona State University. [2] Department of Computer Science, Yonsei University.