PPT-Machine Learning in Compiler Optimization

Author : myesha-ticknor | Published Date : 2017-07-25

By Namita Dave Overview What are compiler optimizations Challenges with optimizations Current Solutions Machine learning techniques Structure of Adaptive compilers

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "Machine Learning in Compiler Optimizatio..." is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Machine Learning in Compiler Optimization: Transcript


By Namita Dave Overview What are compiler optimizations Challenges with optimizations Current Solutions Machine learning techniques Structure of Adaptive compilers Introduction O ptimization . -- Basic functions. Generally, an independent course, maybe plus one semester on implementation of a compiler.. High level language program .  program in assembly language or object code directly. 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. Computational . Exascale. Workshop. December 2010. Dan Quinlan. Chunhua. Liao, Justin Too, Robb . Matzke. , Peter . Pirkelbauer. Center for Applied Scientific Computing. Lawrence Livermore National Laboratory. Compilers. Basic compiler Functions . (1). A. . high-level. . programming. . language. . is usually. . described. . in. . terms. . of. . a. . grammar.. This. . grammar. . specifies. . the. Size of the source language (bigger = harder). Extent of change during compiler construction (more changes = harder). Performance Criteria. Compiler Speed. Code Quality. Error Diagnostics. Portability. Prof. O. . Nierstrasz. Lecture notes by Marcus . Denker. © Marcus . Denker. Optimization. Roadmap. Introduction. Optimizations in the Back-end. The Optimizer. SSA Optimizations. Advanced Optimizations. Jianfu Chen. Computer Science Department. Stony Brook University. Machine learning learns an idealized model of the . real . world..  .  .  .  . 1 + 1 = 2.  .  . ?. Prod1 -> class1. Sergey Tomin. Machine Learning Applications for Particle Accelerators. SLAC, 28.02.2018. Outline. Introduction . Generic Optimizer. Adaptive Feedback. Machine Learning at the European XFEL. S2e simulations in the control room . 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);. Bahrudin Hrnjica, MVP. Agenda. Intro to ML. Types of ML. dotNET and ML-tools and libraries. Demo01: ANN with C#. Demo02: GP with C#. .NET Tools – Acord.NET, GPdotNET. Summary. Machine Learning?. method of teaching computers to make predictions based on data.. OO. L 2. 0. 12 KY. O. T. O. Briefing & Report. By: Masayuki . Kouno. . (D1) & . Kourosh. . Meshgi. . (D1). Kyoto University, Graduate School of Informatics, Department of Systems Science. Ishii Lab (Integrated System Biology). UNC Collaborative Core Center for Clinical Research Speaker Series. August 14, 2020. Jamie E. Collins, PhD. Orthopaedic. and Arthritis Center for Outcomes Research, Brigham and Women’s Hospital. Department of . V. . Kain. , M. Fraser, B. Goddard, S. . Hirlander. , M. Schenk, F. . Velotti. CERN, EPFL, University of Malta. Lots of input from S. Levine’s lectures on Deep Reinforcement Learning at UC Berkeley .

Download Document

Here is the link to download the presentation.
"Machine Learning in Compiler Optimization"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.

Related Documents