Compiler Design Spring 2018

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1 Compiler Design Spring 2018 Thomas R. Gross Computer Science Department ETH Zurich, Switzerland 1

2 Logistics Lecture Tuesdays: 10:15 11:55 Thursdays: 10: :55 In ETF E1 Recitation Announced later Watch lecture website and your ETH Lecture website: via Lecture slides Homework assignment If questions related to assignments: contact assistants (mailing list) If questions related to lecture: write me 2

3 Rules Rule #1: Peace in the lecture hall 3

4 What do you want to get out of this class? Please fill out entry questionnaire It s anonymous 4

5 What I hope to teach you in this class 1. Compiler design: Structure of a simple compiler Simple: 2-3K lines of Java code (maybe a bit more) Industry: C1 compiler in HotSpot VM is considered simple 30K lines of C/C++/assembly code 2. Software engineering: How to design a large(r) software system Sometimes there is no right or wrong Sometimes there is 3. Programming What the programming language design document should tell you How to use that information 8

6 Course structure You will not learn the material from lectures alone Homework is essential! 9

7 Homework Core element of the course You will build a compiler More on this topic (organization, constraints) later 10

8 Compiler design and implementation What is your favorite compiler? Please talk to your neighbor and tell him/her which compiler(s) you used and if you have a favorite compiler. Why? Justify your answer. Can you and your neighbor agree on what matters to you in a compiler? 11

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10 Observations Languages are important Source language L 1 Target language L 2 Host language L H Programs can be executed Program is a sequence of expressions E 1, E 2, A processor contains state Execution of expressions: Each expression E i may read state, modify state, and determine next expression to execute E j A special expression E stop indicates that program execution stops 14

11 Program execution Execution ( elaboration ) of expressions E 1, E 2, by some machine M M realized by hardware physical processor M defined by software virtual machine Other possibilities Expressions E 1, E 2, also referred to as statements or operations Elaboration sometimes referred to as interpretation The word interpretation sometimes hints at direct execution 15

12 Issues Languages: Choices for L 1 and L 2 16

13 Languages Please talk to your neighbor and find at least three languages that could serve as either source language L 1 or target language L 2 for a compiler. Think about compilers you used (or would have liked to use). 17

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16 Languages L 1 and L 2 L 1 C ASM LLVM Java C# Scala JavaScript Python L 2 Machine instruction ASM C LLVM Java Byte Code JavaScript 23

17 (More) languages L 1 and L 2 php html pdf dvi Latex Tex VHDL SQL Lisp Haskell Prolog 24

18 Issues (continued) Languages: Choices for L 1 and L 2 Program written in L 1 (P L 1 ) translated into program written in L 2 (P L 2 ) P L 1 à P L2 Aspects of translation of programs P L 1 à P L2 What does it mean that P L 2 is a translation of P L1 P L 2 should produce the same result as P L1 25

19 Semantics Describes the meaning of programs Meaning of program defined by meaning of statements or operations Formal specification 1. Operational semantics Abstract machine A Sequences of steps interpreted ( elaboration ) Effect on A determines meaning 2. Denotational semantics Mathematical construct describes effect Can be manipulated (composition, projection, ) 3. Axiomatic semantics Assertions on program state and rules that describe the effect of operations Other ways: natural language, reference implementation 26

20 Semantics Translated (target) program P L 2 has the same meaning as the (source) program P L1 At least: computes the same result(s) for all legal inputs Same: must be defined... What about illegal inputs? What about non-functional properties? 27

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22 Reasons for translation A compiler translates a program written in language L 1 into language L 2. Reasons to translate P L 1 à P L2 Faster execution of P L 2 No real machine to run P L 1 No abstract machine (virtual machine) to run P L 1 P L 2 can be realized (in hardware) (L 1 ==L 2 ) P L2 is more readable/optimized/stable Special case: L 1 =asm, binary rewriting tool adds bounds checks P L1 cannot be edited (by humans) Compiler Java byte code to Java P L2 requires less energy 31

23 Complications L 1 and L 2 have different resource models L 1 : no limit on resources, flexible description L 2 : finite resources, inflexible description, hardware-based 32

24 Complications L 1 : no limit on resources number of variables lines of code number of methods data space nesting characters in var name L 2 : finite resources Fixed number of registers Limited storage Finite representation Machine properties matter Caches TLBs NUMA 33

25 Compiler task: Translate P L 1 à P L2 Management of resources Preservation of semantics Is meaning defined? For all possible inputs? Check constraints on P L 1 Bailout: Not every program can be translated Not every aspect can be checked by compiler Escape: compiler inserts code into P L2 to check properties of program during execution ( at runtime ) 34

26 Compiler Design Spring Simple compiler model Thomas R. Gross Computer Science Department ETH Zurich, Switzerland 1

27 1.1 Simple and realistic compiler model Simple: Can be handled in one semester, 8 credits Two persons to work on the same project (more about teams later) Realistic: Experience problems encountered by real compilers Mirrors structure of many compilers 2

28 Compiler model Source program Compiler ASM file Assembler Object file 4

29 Compiler model Compilation prior to execution AOT Ahead of (Execution) Time compilation Commonly used for languages without language-specific execution environments (e.g., C, C++) Available in Java as well (IBM J9, Oracle HotSpot) Other model: Continuous compilation JIT Just in Time compilation Usually: optimization of methods that are frequently invoked (hot) Commonly used with language virtual machines (e.g., Java VM) E.g., HotSpot JVM has two JIT compilers (C1 and C2) 5

30 Compiler model Source program Compiler ASM file Frontend IR Back-end Read input, transform Intermediate representation Manage machine resources Generate code Assembler Object file 7

31 Compiler model Source program Frontend Frontend Frontend Compiler IR ASM file Back-end Assembler Object file 9

32 IR Intermediate representation Compiler-internal representation E.g., compiler must distinguish between names in different scopes E.g., many programs work with variables, computers work with locations Must express all language constructs/concepts Code generator maps IR to assembly code Machine code another option No best IR all are compromises 11

33 Compiler model Source program Frontend Compiler IR Optimizer ASM file Back-end Assembler Native code 13

Programming Concepts

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