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Optimizing Compilers CISC 673 Spring 2009 Course Overview

Optimizing Compilers CISC 673 Spring 2009 Course Overview. John Cavazos University of Delaware. About Me. John Cavazos <cavazos@cis> Assistant Professor, CIS Research Applying machine learning to hard systems problems Compiling for advanced architectures

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Optimizing Compilers CISC 673 Spring 2009 Course Overview

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  1. Optimizing CompilersCISC 673Spring 2009Course Overview John Cavazos University of Delaware

  2. About Me • John Cavazos <cavazos@cis> • Assistant Professor, CIS • Research • Applying machine learning to hard systems problems • Compiling for advanced architectures • Interplay between compiler and architecture • Nitty-gritty things that happen in compiler back-ends • Rethinking fundamental structure of optimizing compilers

  3. Overview • Class format: • Participatory & interactive • You take notes, read book & papers • Course website: • www.cis.udel.edu/~cavazos/cisc673/

  4. Overview • Grades: • 5% class participation • 10% homework assignments • 35% exams • 50% project • Materials: • Selected papers • Compilers books on reserve in library

  5. Project Stuff • 1 to 2 person teams • Implement optimization/analysis in: • JikesRVM (IBM’s research Java compiler) • Others subject to my approval • Due dates (roughly) • 02/19/09: One-page project description. • 03/05/09: 2-4 page project design. • 04/07/09: Project implementation review. • 05/05/09: Implementation due. • 05/12-19/09: In-class presentations. • 05/19/09: Project report.

  6. Class Topics • Some bread & butter analyses & optimizations • Control flow • Data flow analysis & implementation • Loop Transformations • SSA form • Register allocation • Instruction scheduling • Pointer analysis • Runtime systems (compiler must be aware of these!)

  7. Class Topics • Advanced topics (TBD) • Autotuning • Optimizations for Parallel Programs • Program slicing • Error detection • Run time system interaction

  8. Why Compilers Matter

  9. What are Compilers, Anyway? • Compiler: translates program in one language to executable program in other language • Typically lowers abstraction level • E.g., Java/C++ to assembler • Optimizing compiler: • Misnomer! • Optimal compilation intractable • Improves program performance

  10. Compilers Don’t Help Much • Do compilers improve performance anyway? • Proebsting’s law(Todd Proebsting, Microsoft Research): • Difference between optimizing and non-optimizing compiler ~ 4x • Assume compiler technology represents 36 years of progress (actually more) • Compilers double program performance every 18 years! • Not quite Moore’s Law…

  11. Argumentum Ad Absurdum • Compilers don’t help much(Proebsting’s Law) • Don’t use optimizer • Hmmm…

  12. A Big BUT • But: people will not accept massive performance hit for these gains • Compile with optimization! • Still use C and C++!! • Hand-optimize their code!!! • Even write assembler code (gasp)!!!! • Apparently performance does matter… • Multicores makes compilers even MORE important!

  13. Why Compilers Matter • Key part of compiler’s job:make the costs of abstraction reasonable • Remove performance penalty for: • Using objects • Safety checks (e.g., array-bounds) • Writing clean code (e.g., recursion) • Use program analysis to transform code: primary topic of this course

  14. Analysis & The Holy Grail • Other great uses for program analysis: • Static error-checking • Detect information leaks • Avoid security holes • Informing runtime system (e.g., GC) • Even better optimizations! • E.g., locality-improving transformations (“memory wall”) • Holy Grail for compiler research: • Programmer: writes simple but O(2^n) algorithm • Compiler: changes it to O(n log n) (or O(1)!)

  15. Levels of Analysis (in order of increasing detail & complexity) • Local (single-block) [1960’s] • Straight-line code • Simple to analyze; limited impact • Intraprocedural [1970’s – today] • Whole procedure • Dataflow & dependence analysis • Interprocedural [late 1970’s – today] • Whole-program analysis • Tricky: • Very time and space intensive • Hard for some PL’s (e.g., Java)

  16. Optimization =Analysis + Transformation • Key analyses: • Control-flow • if-statements, branches, loops, procedure calls • Data-flow • definitions and uses of variables • Representations: • Control-flow graph • Control-dependence graph • Def/use, use/def chains • SSA (Static Single Assignment)

  17. About You

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