PPT-Proving Optimizations Correct

using Parameterized Program Equivalence University of California San Diego Sudipta Kundu Zachary Tatlock Sorin Lerner Compiler Correctness Difficult to develop reliable compilers large complex programs. brPage 1br CORRECT INCORRECT RECOGNIZED 4 0 INTERRUPT 4 4 BLURT 0 0 CORRECT INCORRECT RECOGNIZED 4 0 INTERRUPT 4 4 BLURT 0 0 CORRECT INCORRECT RECOGNIZED 4 0 INTERRUPT 4 4 BLURT 0 0 COR 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 Jeff Craven, Marcia Cronce, and Steve Davis. NOAA/NWS Milwaukee-Sullivan WI. 7. th. GOES Users’ Conference (GUC. ) Oct 20. th. 2011. 2010 CIMSS MKX GOES-R Proving Ground. May to August 2010. 27 CIMSS GRPG shifts scheduled (. Bruce Worthington. Software Development Manager. Microsoft Corporation. Key Takeaways. Be a leader in advancing 64-bit computing. Adopt best practices and new tools. Let’s partner on new hardware directions. Yongsub. Lim. Applied Algorithm Laboratory. KAIST. Definition. A class is . PAC learnable . by a hypothesis class if. there is an algorithm such that. over . Nuno Lopes . and. José Monteiro. Deriving preconditions by hand is hard; WPs are often non-trivial. WPs derived by hand are often wrong!. Weaker preconditions expose more optimization opportunities. in . Dyninst. Andrew . Bernat. , Matthew Legendre. Instrumentation is Complicated. User perspective: . “Insert some new code here, here, and here.”. Dyninst’s. perspective:. Relocation. – Move code to make space for instrumentation. By Namita Dave. Overview. What are compiler optimizations?. Challenges with optimizations. Current Solutions. Machine learning techniques. Structure of Adaptive compilers. Introduction. O. ptimization . Prof. O. . Nierstrasz. Lecture notes by Marcus . Denker. © Marcus . Denker. Optimization. Roadmap. Introduction. Optimizations in the Back-end. The Optimizer. SSA Optimizations. Advanced Optimizations. using Parameterized Program Equivalence. University of California, San Diego. Sudipta. . Kundu. Zachary . Tatlock. Sorin. Lerner. Compiler Correctness. Difficult to develop reliable compilers:. large, complex programs. Ross Tate. Michael Stepp. Sorin Lerner. University of California, San Diego. Optimizing by Hand. Original. for (i = 0; i < 50; i ). . for (j = 0; j < 50; j ). . img[i*50 j] = f(i, j);. T. Chen, T. Moreau, Z. Jiang, L. Zheng, S. Jiao, E. Yan, H. Shen, M. Cowan, L. Wang, Y. Hu, L. . Ceze. , C. . Guestrin. , and A. Krishnamurthy . Presentation by Grzegorz . Overview of resources for selecting optimizationsHow Technical Support can helpUsing optimizations to customize for each patientAn orthotist shares how she chooseoptimizationsOrthotist explains optimi w Page 1 M ateria M edica text books & A uthors Dr Ajith K umar D S Dept. of Physio logy & Biochemistry, Govt. Homeopathic Medical College. calicut.10 Email : ajithdevarajan@yahoo.co.in Sl:no B