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. 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 (. 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. 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. and High-Coverage Tests. Presented by Yoni Leibowitz. EXE & KLEE. Automatically Generating Inputs of Death. David Dill. Vijay . Ganesh. Cristian Cadar. Dawson Engler. Peter Pawlowski. KLEE. EXE. Unassisted & Automatic Generation of High-Coverage Tests for Complex System Programs. Research at UNT. . . Krishna . Kavi. Professor. Director of NSF Industry/University Cooperative Center . for Net-Centric Software and Systems (Net-Centric IUCRC). Computer Science and Engineering. The University of North Texas. Prof. O. . Nierstrasz. Lecture notes by Marcus . Denker. © Marcus . Denker. Optimization. Roadmap. Introduction. Optimizations in the Back-end. The Optimizer. SSA Optimizations. Advanced Optimizations. 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);. .pptx: http://. tinyurl.com/6y7gy8x (or scan QR code). The . paper: http. ://dl.acm.org/citation.cfm?id=1122414. [1] . http://. www.nic.uoregon.edu/iwomp2005/IWOMP_Photos_Day1/IWOMP_Photos-Images/7.jpg. tool . and parallelization. do {. //get the write lock. . KeAcquireSpinLock. (&. devExt. ->. writeListLock. );. . nPacketsOld. = . nPackets. ; . request = . devExt. ->. WriteListHeadVa. 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 . 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. and High-Coverage Tests. Presented by Yoni Leibowitz. EXE & KLEE. Automatically Generating Inputs of Death. David Dill. Vijay . Ganesh. Cristian Cadar. Dawson Engler. Peter Pawlowski. KLEE. EXE. Unassisted & Automatic Generation of High-Coverage Tests for Complex System Programs.