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Advanced Compilers CMPSCI 710 Spring 2004 Lecture 1

Advanced Compilers CMPSCI 710 Spring 2004 Lecture 1. Emery Berger University of Massachusetts, Amherst. Overview. Class format: Participatory & interactive You take notes, read book & papers Course website: www.cs.umass.edu/~emery/cmpsci710/ Grades: 10% class participation

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Advanced Compilers CMPSCI 710 Spring 2004 Lecture 1

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  1. Advanced CompilersCMPSCI 710Spring 2004Lecture 1 Emery Berger University of Massachusetts, Amherst

  2. Overview • Class format: • Participatory & interactive • You take notes, read book & papers • Course website: • www.cs.umass.edu/~emery/cmpsci710/ • Grades: • 10% class participation • 10% homework assignments • 40% exams • 40% project • Materials: • Muchnick, Advanced Compiler Design Implementation (ACDI) • Selected papers

  3. Project Stuff • 1 to 2 person teams • Implement optimization/analysis in: • Jikes RVM (IBM’s research Java compiler) • Shark (UMass fast & safe scripting language) • Others subject to my approval • Due dates (roughly) • 02/11/04: One-page project description. • 02/25/04: 2-4 page project design. • 03/25/04: Project implementation review. • 04/29/04: Implementation due. • 05/06-13/04: In-class presentations. • 05/13/04: Project report.

  4. Class Topics • Some bread & butter analyses & optimizations • Control flow • Data flow analysis & implementation • SSA form • Register allocation • Instruction scheduling • Pointer analysis • Runtime systems (compiler must be aware of these!) • Advanced topics (TBD) • Binary decision diagrams for pointer analysis • Program slicing • Error detection • Run time system interaction • Region analysis…

  5. Why Compilers Matter

  6. 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

  7. Performance Anxiety • But does performance really matter? • Computers are really fast • Moore’s law (roughly):hardware performance doubles every 18 months • Real bottlenecks lie elsewhere: • Disk • Network • Human! (think interactive apps) • At most 120 cps typing • Waste time “thinking”

  8. 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…

  9. Argumentum Ad Absurdum • Compilers don’t help much(Proebsting’s Law) • Don’t use optimizer • Computers are getting faster(Moore’s Law) • Buy a new one – helps the economy too! • Or: wait until one’s fast enough,then run program • Hmmm…

  10. A Big BUT • Why use high-level languages anyway? • Easier to write & maintain • Safer (think Java) • More convenient (think libraries, GC…) • 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…

  11. 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

  12. 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)!)

  13. 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)

  14. 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)

  15. Next Time • Read ACDI Chapters 1, 2, 7

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