PPT-Precise Program Analysis with Data Structures

Collaborators George Necula Xavier Rival INRIA BorYuh Evan Chang University of California Berkeley FebruaryApril 2008 Precise Program Analysis with Data Structures by Designing with the User in Mind. Swarnendu Biswas, . Jipeng Huang, Aritra Sengupta, and Michael D. Bond. The Ohio State University. PLDI 2014. Impact of Concurrency Bugs. Impact of Concurrency Bugs. Northeastern blackout, 2003. Impact of Concurrency Bugs. What can we compare?. 3D shapes (RMSD). Atomic motions (B-value, RMSF). Solvent accessibilities (SASA). AMIGOS - Reads an RNA PDB file and outputs a complete table of torsion angle calculations.. APBS - Software for evaluating the electrostatic properties of . Anthony . Cozzie. , Frank Stratton, . Hui. . Xue. , Sam King. University of Illinois at Urbana-Champaign. The Current Antivirus Situation. Virus Stealth Techniques. Signature checkers are basically . Komondoor. V. . Raghavan. Indian Institute of Science, Bangalore. The problem of program slicing. Given a . program. . P. , and a statement . c . (the . criterion. ). , . identify statements and conditionals in the program that are . Contents. Single structures. Arrays of structures. Structures as function arguments. Linked lists. Dynamic data structure allocation. Unions. Common programming errors. Single Structures. Creating and using a structure involves two steps. Wilfredo. Velazquez. Outline. Basics of Concurrency. Concepts and Terminology. Advantages and Disadvantages. Amdahl’s Law. Synchronization Techniques. Concurrent Data Structures. Parallel Correctness. Michael T. Goodrich. Dept. of Computer . Science. University of California, Irvine. The Need for Good Algorithms. T. o . facilitate improved network analysis, we need . fast algorithms . and . efficient data structures. Swarnendu Biswas, . Jipeng Huang, Aritra Sengupta, and Michael D. Bond. The Ohio State University. PLDI 2014. Impact of Concurrency Bugs. Impact of Concurrency Bugs. Northeastern blackout, 2003. Impact of Concurrency Bugs. Yue Li. , . Tian. Tan, . Yifei. Zhang and Jingling . Xue. UNSW Australia. ECOOP 2016. Rome, Italy. 1. Program Slicing. Program slicing. [Weiser 1981] citation: 3950. 2. Program Slicing. Debugging. Maintenance. Fall . 2015. See online syllabus (also available through . BlueLine. ). : . . http://dave-reed.com/csc321. Course goals:. To understand fundamental data structures (lists, stacks, queues, sets, maps, and linked structures) and be able to implement software solutions to problems using these data structures. . Collaborators: George . Necula. , Xavier Rival (INRIA). Bor-Yuh. Evan Chang. University of California, Berkeley. February-April 2008. Precise Program Analysis with Data Structures. . by Designing with the User in Mind. for an experimental nuclear physicist in a large . collaboration. Jeff Porter. . The large collaboration . provides & maintains many. . tools & services . needed by. . the experimental scientist (E1):. Jean . Shimer. . and Patti . Fougere. , MA Part C. Karen Walker, WA Part . C. Karie. Taylor, AZ Part C. Abby . Winer, . DaSy. , ECTA. Tony Ruggiero, . DaSy. , . IDC. 2014 Improving Data, Improving Outcomes Conference. Cong Chen. , Paul Clarke, Lora Frayling, Sally Vernon, Brian Shand, Pesh Doubleday, Jem Rashbass. Overview. Context and goals of this talk. Background: our motivating problem. What is synthetic data and how does it help?.