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 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 Partial correctness assertions are represented by intuitionistic linear implica tion We prove soundness and completeness over relational and trace models As a corollary we obtain a complete sequent calculus for inclusion and equivalence of regular e berkeleyedu Abstract Writing correct explicitlyparallel programs can be very challenging While the functional correctness of a pro gram can often be understood largely sequentially a software engineer must simultaneously reason about the nondetermini Susan . Owicki. & David . Gries. Presented by Omer Katz. Seminar in Distributed Algorithms Spring 2013. 29/04/13. What’s next?. What are we trying to do?. The sequential solution. The parallel solution. 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. : Parallel Secure Computation Made Easy. . Kartik . Nayak. With Xiao . Shaun . Wang, . Stratis. Ioannidis, Udi . Weinsberg. , Nina Taft, Elaine Shi. 1. 2. Users. Data. Data. Privacy concern!. Data Mining Engine. Ali Mili, NJIT. Third Spring Festival Workshop. Karuizawa. , March 2017. Acknowledgements. In collaboration . with. M. . Frias (Argentina), . J. . . Desharnais. (CDN), . W. . . Ghardallou. (TN), . N. Divergent views. “healthy expansion of moral concern” – Noam Chomsky. “new strain of . idealogical. virus” – Michael Barnard. “dictatorship of the well meaning and pure of heart” - Morris Dickstein. . 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. . 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?. Se-Joon Chung. Background and Key Challenges. The trend in computing hardware is parallel systems.. It is challenging for programmers is to develop applications that transparently scales its parallelism to leverage the increasing number of processor cores.. 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.