PPT-Generating Compiler Optimizations from Proofs

Author : tatyana-admore | Published Date : 2018-11-17

Ross Tate Michael Stepp Sorin Lerner University of California San Diego Optimizing by Hand Original for i 0 i lt 50 i for j 0 j lt 50 j imgi50 j fi j

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Generating Compiler Optimizations from Proofs: Transcript


Ross Tate Michael Stepp Sorin Lerner University of California San Diego Optimizing by Hand Original for i 0 i lt 50 i for j 0 j lt 50 j imgi50 j fi j. ucsdedu Abstract We present an automated technique for generating compiler op timizations from examples of concrete programs before and after improvements have been made to them The key technical insight of our technique is that a proof of equivalenc Lo Susan J Eggers Henry M Levy Sujay S Parekh and Dean M Tullsen Dept of Computer Science and Engineering Box 352350 University of Washington Seattle WA 981952350 jlo sparekh eggers levycswashingtonedu Dept of Computer Science and Engineering Univer 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. 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. 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);. 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);. Prof. Gennady . Pekhimenko. University of Toronto. Winter 2018. The content of this lecture is adapted from the lectures of . Todd Mowry and Phillip Gibbons. CSC D70: . Compiler Optimization. Introduction, Logistics. 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 . Preconditions . for . Compiler Optimizations. Nuno Lopes. Advisor. : José Monteiro. Automatic. . Synthesis. . of. . Weakest. . Preconditions. for . Compiler. . Optimizations. Expectations for Compilers. 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. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand

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