Levels of Programming Languages

1. Levels of Programming Languages High-level program class Triangle { ... float surface() return b*h/2; } Low-level program LOAD r1,b LOAD r2,h MUL r1,r2 DIV r1,#2 RET Executable Machine code 0001001001000101001001001110110010101101001...

2. Compilers and other translators Examples: Chinese => English Java => JVM byte codes Scheme => C C => Scheme x86 Assembly Language => x86 binary codes • Other non-traditional examples: • disassembler, decompiler (e.g. JVM => Java)

3. Program P implemented in L Translator implemented in L S -> T L P Machine implemented in hardware Language interpreter in L L M M L Tombstone Diagrams What are they? • diagrams consisting out of a set of “puzzle pieces” we can use to reason about language processors and programs • different kinds of pieces • combination rules (not all diagrams are “well formed”)

4. Syntax Specification Syntax is specified using “Context Free Grammars”: • A finite set of terminal symbols • A finite set of non-terminal symbols • A start symbol • A finite set of production rules Usually CFG are written in “Bachus Naur Form” or BNF notation. A production rule in BNF notation is written as: N ::= a where N is a non terminal and a a sequence of terminals and non-terminals N ::= a | b | ... is an abbreviation for several rules with N as left-hand side.

5. Concrete and Abstract Syntax The previous grammar specified the concrete syntax of mini triangle. The concrete syntax is important for the programmer who needs to know exactly how to write syntactically well-formed programs. The abstract syntax omits irrelevant syntactic details and only specifies the essential structure of programs. Example: different concrete syntaxes for an assignment v := e (set! v e) e -> v v = e

6. Abstract Syntax Trees Abstract Syntax Tree for: d:=d+10*n AssignmentCmd BinaryExpression BinaryExpression VName VNameExp IntegerExp VNameExp SimpleVName SimpleVName SimpleVName Int-Lit Ident Ident Op Ident Op + 10 d d n *

7. Undefined! Scope Rules Type Rules Type error! Contextual Constraints Syntax rules alone are not enough to specify the format of well-formed programs. Example 1: let const m~2 in m + x Example 2: let const m~2 ; var n:Boolean in begin n := m<4; n := n+1 end

8. Semantics Specification of semantics is concerned with specifying the “meaning” of well-formed programs. • Terminology: • Expressions are evaluated and yield values (and may or may not perform side effects) • Commands are executed and perform side effects. • Declarations are elaborated to produce bindings • Side effects: • change the values of variables • perform input/output

9. Phases of a Compiler A compiler’s phases are steps in transforming source code into object code. The different phases correspond roughly to the different parts of the language specification: • Syntax analysis <-> Syntax • Contextual analysis <-> Contextual constraints • Code generation <-> Semantics

10. Compiler Passes • A pass is a complete traversal of the source program, or a complete traversal of some internal representation of the source program. • A pass can correspond to a “phase” but it does not have to! • Sometimes a single “pass” corresponds to several phases that are interleaved in time. • What and how many passes a compiler does over the source program is an important design decision.

11. Syntax Analysis Dataflow chart Source Program Stream of Characters Scanner Error Reports Stream of “Tokens” Parser Error Reports Abstract Syntax Tree

12. Regular Expressions • RE are a notation for expressing a set of strings of terminal symbols. • Different kinds of RE: • e The empty string • t Generates only the string t • X Y Generates any string xy such that x is generated by x • and y is generated by Y • X | Y Generates any string which generated either • by X or by Y • X* The concatenation of zero or more strings generated • by X • (X) For grouping,

13. FA and the implementation of Scanners • Regular expressions, (N)DFA-e and NDFA and DFA’s are all equivalent formalism in terms of what languages can be defined with them. • Regular expressions are a convenient notation for describing the “tokens” of programming languages. • Regular expressions can be converted into FA’s (the algorithm for conversion into NDFA-e is straightforward) • DFA’s can be easily implemented as computer programs.

14. JFlex Lexical Analyzer Generator for Java Definition of tokens Regular Expressions JFlex Java File: Scanner Class Recognizes Tokens

15. Parsing Parsing == Recognition + determining phrase structure (for example by generating AST) • Different types of parsing strategies • bottom up • top down • Recursive descent parsing • What is it • How to implement one given an EBNF specification • Bottom up parsing algorithms

16. Sentence Subject Subject Verb Verb Object Object . Noun Noun Noun Noun The cat sees a rat . Top-down parsing Sentence The The cat cat sees sees a rat rat . .

17. Sentence Subject Object Noun Verb Noun Bottom up parsing The The cat cat sees sees a a rat rat . .

18. Development of Recursive Descent Parser (1) Express grammar in EBNF (2) Grammar Transformations: Left factorization and Left recursion elimination (3) Create a parser class with • private variable currentToken • methods to call the scanner: accept and acceptIt (4) Implement private parsing methods: • add private parseNmethod for each non terminal N • public parsemethod that • gets the first token form the scanner • calls parseS (S is the start symbol of the grammar)

19. LL 1 Grammars • The presented algorithm to convert EBNF into a parser does not work for all possible grammars. • It only works for so called “LL 1” grammars. • Basically, an LL1 grammar is a grammar which can be parsed with a top-down parser with a lookahead (in the input stream of tokens) of one token. • What grammars are LL1? How can we recognize that a grammar is (or is not) LL1? => We can deduce the necessary conditions from the parser generation algorithm.

20. LR parsing • The algorithm makes use of a stack. • The first item on the stack is the initial state of a DFA • A state of the automaton is a set of LR0/LR1 items. • The initial state is constructed from productions of the form S:= •a [, $] (where S is the start symbol of the CFG) • The stack contains (in alternating) order: • A DFA state • A terminal symbol or part (subtree) of the parse tree being constructed • The items on the stack are related by transitions of the DFA • There are two basic actions in the algorithm: • shift: get next input token • reduce: build a new node (remove children from stack)

21. Bottom Up Parsers: Overview of Algorithms • LR0 : The simplest algorithm, theoretically important but rather weak (not practical) • SLR : An improved version of LR0 more practical but still rather weak. • LR1 : LR0 algorithm with extra lookahead token. • very powerful algorithm. Not often used because of large memory requirements (very big parsing tables) • LALR : “Watered down” version of LR1 • still very powerful, but has much smaller parsing tables • most commonly used algorithm today

22. JavaCUP: A LALR generator for Java Definition of tokens Regular Expressions Grammar BNF-like Specification JFlex JavaCUP Java File: Scanner Class Recognizes Tokens Java File: Parser Class Uses Scanner to get TokensParses Stream of Tokens Syntactic Analyzer

23. VarDecl SimpleT Contextual Analysis -> Decorated AST Annotations: Program result of identification LetCommand :typeresult of type checking SequentialCommand SequentialDeclaration AssignCommand :int AssignCommand BinaryExpr VarDecl Char.Expr VNameExp Int.Expr :char :int :int :int SimpleT SimpleV SimpleV :char :int Ident Ident Ident Ident Ident Char.Lit Ident Ident Op Int.Lit n c n n Integer Char c ‘&’ + 1

24. Nested Block Structure A language exhibits nested block structure if blocks may be nested one within another (typically with no upper bound on the level of nesting that is allowed). Nested • There can be any number of scope levels (depending on the level of nesting of blocks): • Typical scope rules: • no identifier may be declared more than once within the same block (at the same level). • for any applied occurrence there must be a corresponding declaration, either within the same block or in a block in which it is nested.

25. Type Checking For most statically typed programming languages, a bottom up algorithm over the AST: • Types of expression AST leaves are known immediately: • literals => obvious • variables => from the ID table • named constants => from the ID table • Types of internal nodes are inferred from the type of the children and the type rule for that kind of expression

26. Runtime organization • Data Representation: how to represent values of the source language on the target machine. • Primitives, arrays, structures, unions, pointers • Expression Evaluation: How to organize computing the values of expressions (taking care of intermediate results) • Register vs. stack machine • Storage Allocation: How to organize storage for variables (considering different lifetimes of global, local and heap variables) • Activation records, static links • Routines: How to implement procedures, functions (and how to pass their parameters and return values) • Value vs. reference, closures, recursion • Object Orientation: Runtime organization for OO languages • Method tables

27. Tricky sort identity n:23 check check p i:88 n:15 check check p i:88 n:7 check check p i:88 n:88 check identity p

28. JVM External representation platform independent JVM internal representation implementation dependent .class files load classes primitive types integers objects arrays methods The JVM is an abstract machine in the true sense of the word. The JVM spec. does not specify implementation details (can be dependent on target OS/platform, performance requirements etc.) The JVM spec defines a machine independent “class file format” that all JVM implementations must support.

29. Inspecting JVM code % javac Factorial.java % javap -c -verbose Factorial Compiled from Factorial.java public class Factorial extends java.lang.Object { public Factorial(); /* Stack=1, Locals=1, Args_size=1 */ public int fac(int); /* Stack=2, Locals=4, Args_size=2 */ } Method Factorial() 0 aload_0 1 invokespecial #1 <Method java.lang.Object()> 4 return

30. Inspecting JVM Code ... // address: 0 1 2 3 Method int fac(int) // stack: this n result i 0 iconst_1 // stack: this n result i 1 1 istore_2 // stack: this n result i 2 iconst_2 // stack: this n result i 2 3 istore_3 // stack: this n result i 4 goto 14 7 iload_2 // stack: this n result i result 8 iload_3 // stack: this n result i result i 9 imul // stack: this n result i result i 10 istore_2 11 iinc 3 1 14 iload_3 // stack: this n result i i 15 iload_1 // stack: this n result i i n 16 if_icmple 7 // stack: this n result i 19 iload_2 // stack: this n result i result 20 ireturn

31. ~ ~ Code Generation Source Program Target program let var n: integer; var c: charin begin c := ‘&’; n := n+1end PUSH 2LOADL 38STORE 1[SB]LOAD 0LOADL 1CALL addSTORE 0[SB]POP 2HALT Source and target program must be “semantically equivalent” Semantic specification of the source language is structured in terms of phrases in the SL: expressions, commands, etc. => Code generation follows the same “inductive” structure.

32. Specifying Code Generation with Code Templates The code generation functions for Mini Triangle Phrase Class Function Effect of the generated code Run program P then halt. Starting and finishing with empty stack Execute Command C. May update variables but does not shrink or grow the stack! Evaluate E, net result is pushing the value of E on the stack. Push value of constant or variable on the stack. Pop value from stack and store in variable V Elaborate declaration, make space on the stack for constants and variables in the decl. run P executeC evaluateE fetchV assignV elaborate D Program Command Expres- sion V-name V-name Decla-ration

33. Code Generation with Code Templates While command • execute [whileE doC] = • JUMP h • g: execute [C] • h: evaluate[E] • JUMPIF(1) g C E

34. Code improvement (optimization) The code generated by our compiler is not efficient: • It computes values at runtime that could be known at compile time • It computes values more times than necessary We can do better! • Constant folding • Common sub-expression elimination • Code motion • Dead code elimination

35. Optimization implementation • Is the optimization correct or safe? • Is the optimization an improvement? • What sort of analyses do we need to perform to get the required information? • Local • Global