PPT-Correctness of parallel programs

Shaz Qadeer Research in Software Engineering CSEP 506 Spring 2011 Why is correctness important S oftware development is expensive Testing and debugging significant part of the cost T esting and debugging of parallel and concurrent programs is even more difficult and expensive. Unlike sequential algorithms parallel algorithms cannot be analyzed very well in isolation One of our primary measures of goodness of a parallel system will be its scalability Scalability is the ability of a parallel system to take advantage of incr Perspective Projection projectors converge to a point ITCS 41205120 Geometric Projections ITCS 41205120 Geometric Projections Perspective Projection Projectors converge to a point the center of projection ITCS 41205120 Geometric Projections Parallel The algorithm is an improved version of the bakery algorithm It is specified and proved correct without being decomposed into indivisible atomic operations This allows two different implementations for a conventional nondistributed system Moreover t http://pan.cin.ufpe.br. © Marcelo d’Amorim 2010. Intuition. Program and mathematical formula are similar. Both manipulate symbols and have precise syntax and semantics.. © Marcelo d’Amorim 2010.  . Name . E-mail  . Country, City, University.   . Omer . Subasi.  .  . osubasi@ku.edu.tr.  .  . Turkey,. Istanbul, . Koc. . University .  . Anton . Dergunov.  .  . anton.dergunov@mail.ru. Yancy. Vance . Paredes. Outline. Background. Motivation. Huffman Algorithm. Sample Implementation. Running Time Analysis. Proof of . Correctness. Application. Background. Lossless compression where around 20% to 90% of savings in space. Parallel and Perpendicular Lines. Perpendicular lines. are two lines that intersect to form a 90º. . angle. . Parallel and Perpendicular Lines. Parallel lines. are two lines that, if extended indefinitely, would never cross or touch. Verification. Orna Grumberg. Lectures Material. winter . 2016-17. Lecture 3. Floyd Proof Rule for Partial Correctness. To prove . {q. ₁. }P{q. ₂. } . :. Choose. a set of . cut points . such that:. . Kartik . Nayak. With Xiao . Shaun . Wang, . Stratis. Ioannidis, Udi . Weinsberg. , Nina Taft, Elaine Shi. 1. 2. Users. Data. Data. Privacy concern!. Data Mining Engine. Data Model. Data Mining on User Data. Andrew Ruef. , Michael Hicks, Dave Levin, James Parker, Atif Memon, Jandelyn Plane, P. eter Mardziel. What’s the motivation?. What goes into secure software development?. How could we measure and contrast different styles/languages?. Formal . verification for. Java applications. Teodor. . Parvanov. May 26-27 ‘16, Sofia. 1. About me. I . currently work at VMware Bulgaria, working on standing up the fully automated Software-Defined Data . Annual Meeting. Drew Hirshfeld. . Commissioner for Patents, USPTO. . December 7, . 2017. Patents Workforce . (as of Oct. . 2017. ). 9,970 total employees. 8,152 total Patent Examiners. Of which 186 are Design Examiners. Motivation Security Performance Bibliography ParameterSelectioninRing-LWE-basedFullyHomomorphicEncryptionRachelPlayerInformationSecurityGroup,RoyalHolloway,UniversityofLondonbasedonjointworkswithMarti Fall 2015. Lars Ailo Bongo (larsab@cs.uit.no). Course topics. Parallel programming. The parallelization process. Optimization of parallel programs. Performance analysis. Data-intensive computing. Parallel programs.