Unit 1 cd

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  • Unit 1 cd

    1. 1. Compiler Design CodeReplugd
    2. 2. COMPILERS A compiler is a program takes a program written in a source language and translates it into an equivalent program in a target language. source program COMPILER target program ( Normally a program written ( Normally the equivalent in a high-level programming program in machine code – language) relocatable object file) error messagesOther Applications In addition to the development of a compiler, the techniques used in compiler design can be applicable to many problems in computer science.  Techniques used in a lexical analyzer can be used in text editors, information retrieval system, and pattern recognition programs.  Techniques used in a parser can be used in a query processing system such as SQL.  Many software having a complex front-end may need techniques used in compiler design.  A symbolic equation solver which takes an equation as input. That program should parse the given input equation.  Most of the techniques used in compiler design can be used in Natural Language Processing (NLP) systems.
    3. 3.  An interpreter reads an executable source program written in a high-level programming language as well as data for this program, and it runs the program against the data to produce some results. One example is the Unix shell interpreter, which runs operating system commands interactively
    4. 4. Cousins of compiler Preprocessor(Macro processing, file inclusion, Rational preprocessor, Language extensions) Assembler Linkers-A linker takes several object les and libraries as input and produces one executable object file. It retrieves from the input files (and puts them together in the executable object file) the code of all the referenced functions/procedures and it resolves all external references to real addresses. The libraries include the operating system libraries, the language-specic libraries, and, maybe, user-created libraries. Loaders Debuggers-used to determine execution errors in a compiled program Profiler- Collects Statistics on the behavior of the object program during execution Project Managers-used to coordinate and merge the source code produced by different members of the team
    5. 5. What is the Challenge in compiler Design?Many variations: many programming languages (eg, FORTRAN, C++, Java) many programming paradigms (eg, object-oriented, functional, logic) many computer architectures (eg, MIPS, SPARC, Intel, alpha) many operating systems (eg, Linux, Solaris, Windows)Qualities of a compiler (in order of importance): the compiler itself must be bug-free it must generate correct machine code the generated machine code must run fast the compiler itself must run fast (compilation time must be proportional to program size) the compiler must be portable (ie, modular, supporting separate compilation) it must print good diagnostics and error messages the generated code must work well with existing debuggers must have consistent and predictable optimization.Building a compiler requires knowledge of programming languages (parameter passing, variable scoping, memory allocation, etc) theory (automata, context-free languages, etc) algorithms and data structures (hash tables, graph algorithms, dynamic programming etc) computer architecture (assembly programming)
    6. 6. Front end : machine independentphases/Language dependent Lexical analysis Syntax analysis Semantic analysis Intermediate code generation Some code optimizationBack end : machine dependentphases/Language Independent Final code generation Machine-dependent optimizations Analysis: Lexical analysis Syntax analysis Semantic analysis Synthesis: Intermediate code generation code optimization Final code generation Storage a
    7. 7. position := initial + rate * 60 intermediate code generator lexical analyzer temp1 := inttoreal (60) id1 := id2 + id3 * 60 temp2 := id3 * temp1 temp3 := id2 + temp2 id1 := temp3 syntax analyzer The Phases of a Compiler := code optimizer id1 + id2 * temp1 := id3 * 60.0 id1 := id2 + temp1 id3 60 semantic analyzer code generator := MOVF id3, R2 MULF #60.0, R2 MOVF id2, R1 id1 + ADDF R2, R1 MOVF R1, id1 id2 * id3 inttoreal 60
    8. 8. Lexical Analyzer• Lexical Analyzer reads the source program character by character to produce tokens.• Normally a lexical analyzer doesn’t return a list of tokens at one shot, it returns a token when the parser asks a token from it.source token Lexicalprogram Parser Analyzer get next token Notes | CodeReplugd
    9. 9. Token• Token represents a set of strings described by a pattern. – Identifier represents a set of strings which start with a letter continues with letters and digits – The actual string (newval) is called as lexeme. – Tokens: identifier, number, addop, delimeter, …• Since a token can represent more than one lexeme, additional information should be held for that specific lexeme. This additional information is called as the attribute of the token.• For simplicity, a token may have a single attribute which holds the required information for that token. – For identifiers, this attribute a pointer to the symbol table, and the symbol table holds the actual attributes for that token.• Some attributes: – <id,attr> where attr is pointer to the symbol table – <assgop,_> no attribute is needed (if there is only one assignment operator) – <num,val> where val is the actual value of the number.• Token type and its attribute uniquely identifies a lexeme.• Regular expressions are widely used to specify patterns. Notes | CodeReplugd
    10. 10. Terminology of Languages• Alphabet : a finite set of symbols (ASCII characters)• String : – Finite sequence of symbols on an alphabet – Sentence and word are also used in terms of string – ε is the empty string – |s| is the length of string s.• Language: sets of strings over some fixed alphabet – ∅ the empty set is a language. – {ε} the set containing empty string is a language – The set of well-formed C programs is a language – The set of all possible identifiers is a language.• Operators on Strings: – Concatenation: xy represents the concatenation of strings x and y. s ε = s εs= s – sn = s s s .. s ( n times) s0 = ε Notes | CodeReplugd
    11. 11. Operations on Languages• Concatenation: – L1L2 = { s1s2 | s1 ∈ L1 and s2 ∈ L2 }• Union – L1 ∪ L2 = { s | s ∈ L1 or s ∈ L2 }• Exponentiation: – L0 = {ε} L1 = L L2 = LL• Kleene Closure – L* =• Positive Closure – L+ = Notes | CodeReplugd
    12. 12. Example• L1 = {a,b,c,d} L2 = {1,2}• L1L2 = {a1,a2,b1,b2,c1,c2,d1,d2}• L1 ∪ L2 = {a,b,c,d,1,2}• L13 = all strings with length three (using a,b,c,d}• L1* = all strings using letters a,b,c,d and empty string• L1+ = doesn’t include the empty string Notes | CodeReplugd
    13. 13. Regular Expressions• We use regular expressions to describe tokens of a programming language.• A regular expression is built up of simpler regular expressions (using defining rules)• Each regular expression denotes a language.• A language denoted by a regular expression is called as a regular set. Notes | CodeReplugd
    14. 14. Regular Expressions (Rules)Regular expressions over alphabet Σ Reg. Expr Language it denotes ε {ε} a∈ Σ {a} (r1) | (r2) L(r1) ∪ L(r2) (r1) (r2) L(r1) L(r2) (r)* (L(r))* (r) L(r)• (r)+ = (r)(r)*• (r)? = (r) | ε Notes | CodeReplugd
    15. 15. Regular Expressions (cont.)• We may remove parentheses by using precedence rules. – * highest – concatenation next – | lowest• ab*|c means (a(b)*)|(c)• Ex: – Σ = {0,1} – 0|1 => {0,1} – (0|1)(0|1) => {00,01,10,11} – 0* => {ε ,0,00,000,0000,....} – (0|1)* => all strings with 0 and 1, including the empty string Notes | CodeReplugd
    16. 16. Regular Definitions• To write regular expression for some languages can be difficult, because their regular expressions can be quite complex. In those cases, we may use regular definitions.• We can give names to regular expressions, and we can use these names as symbols to define other regular expressions.• A regular definition is a sequence of the definitions of the form: d1 → r1 where di is a distinct name and d2 → r2 ri is a regular expression over symbols in . Σ∪{d1,d2,...,di-1} dn → rn basic symbols previously defined names Notes | CodeReplugd
    17. 17. Regular Definitions (cont.)• Ex: Identifiers in Pascal letter → A | B | ... | Z | a | b | ... | z digit → 0 | 1 | ... | 9 id → letter (letter | digit ) * – If we try to write the regular expression representing identifiers without using regular definitions, that regular expression will be complex. (A|...|Z|a|...|z) ( (A|...|Z|a|...|z) | (0|...|9) ) *• Ex: Unsigned numbers in Pascal digit → 0 | 1 | ... | 9 digits → digit + opt-fraction → ( . digits ) ? opt-exponent → ( E (+|-)? digits ) ? unsigned-num → digits opt-fraction opt-exponent Notes | CodeReplugd
    18. 18. Finite Automata• A recognizer for a language is a program that takes a string x, and answers “yes” if x is a sentence of that language, and “no” otherwise.• We call the recognizer of the tokens as a finite automaton.• A finite automaton can be: deterministic(DFA) or non-deterministic (NFA)• This means that we may use a deterministic or non-deterministic automaton as a lexical analyzer.• Both deterministic and non-deterministic finite automaton recognize regular sets.• Which one? – deterministic – faster recognizer, but it may take more space – non-deterministic – slower, but it may take less space – Deterministic automatons are widely used lexical analyzers.• First, we define regular expressions for tokens; Then we convert them into a DFA to get a lexical analyzer for our tokens. – Algorithm1: Regular Expression  NFA  DFA (two steps: first to NFA, then to DFA) – Algorithm2: Regular Expression  DFA (directly convert a regular expression into a DFA) Notes | CodeReplugd
    19. 19. Non-Deterministic Finite Automaton (NFA)• A non-deterministic finite automaton (NFA) is a mathematical model that consists of: – S - a set of states – Σ - a set of input symbols (alphabet) – move – a transition function move to map state-symbol pairs to sets of states. – s0 - a start (initial) state – F – a set of accepting states (final states)• ε- transitions are allowed in NFAs. In other words, we can move from one state to another one without consuming any symbol.• A NFA accepts a string x, if and only if there is a path from the starting state to one of accepting states such that edge labels along this path spell out x. Notes | CodeReplugd
    20. 20. NFA (Example) a 0 is the start state s0 {2} is the set of final states F a b Σ = {a,b} 0 1 2 start S = {0,1,2} b Transition Function: a b 0 {0,1} {0} Transition graph of the NFA 1 _ {2} 2 _ _The language recognized by this NFA is (a|b) * a b Notes | CodeReplugd
    21. 21. Deterministic Finite Automaton (DFA)• A Deterministic Finite Automaton (DFA) is a special form of a NFA. • no state has ε- transition • for each symbol a and state s, there is at most one labeled edge a leaving s. i.e. transition function is from pair of state-symbol to state (not set of states) a b a The language recognized by a b 0 1 2 this DFA is also (a|b) * a b b Notes | CodeReplugd
    22. 22. Implementing a DFA• Le us assume that the end of a string is marked with a special symbol (say eos). The algorithm for recognition will be as follows: (an efficient implementation) s s0 { start from the initial state } c  nextchar { get the next character from the input string } while (c != eos) do { do until the en dof the string } begin s  move(s,c) { transition function } c  nextchar end if (s in F) then { if s is an accepting state } return “yes” else return “no” Notes | CodeReplugd
    23. 23. Implementing a NFA S  ε-closure({s0}) { set all of states can be accessible from s0 by ε- transitions } c  nextchar while (c != eos) { begin s  ε-closure(move(S,c)) { set of all states can be accessible from a state in S c  nextchar by a transition on c } end if (S∩F != Φ) then { if S contains an accepting state } return “yes” else return “no”• This algorithm is not efficient. Notes | CodeReplugd
    24. 24. Converting A Regular Expression into A NFA (Thomson’s Construction)• This is one way to convert a regular expression into a NFA.• There can be other ways (much efficient) for the conversion.• Thomson’s Construction is simple and systematic method. It guarantees that the resulting NFA will have exactly one final state, and one start state.• Construction starts from simplest parts (alphabet symbols). To create a NFA for a complex regular expression, NFAs of its sub-expressions are combined to create its NFA, Notes | CodeReplugd
    25. 25. Thomson’s Construction (cont.) ε• To recognize an empty string ε i f a• To recognize a symbol a in the alphabet Σ i f• If N(r1) and N(r2) are NFAs for regular expressions r1 andr2 • For regular expression r1 | r2 ε N(r1) ε i ε f NFA for r1 | r2 ε N(r2) Notes | CodeReplugd
    26. 26. Thomson’s Construction (cont.)• For regular expression r1 r2 i N(r1) N(r2) f Final state of N(r2) become final state of N(r1r2) NFA for r1 r2• For regular expression r* ε ε ε i N(r) f ε NFA for r* Notes | CodeReplugd
    27. 27. Thomson’s Construction (Example - (a|b) * a ) a aa: ε ε (a | b) ε ε b bb: ε a ε ε ε (a|b) * ε ε ε b ε ε a ε ε ε ε a (a|b) * a ε b ε ε Notes | CodeReplugd
    28. 28. Converting a NFA into a DFA (subset construction) put ε-closure({s0}) as an unmarked state into the set of DFA (DS) while (there is one unmarked S1 in DS) do ε-closure({s0}) is the set of all states can be accessible begin from s0 by ε-transition. mark S1 for each input symbol a dofrom a state s in S there is a transition on set of states to which a 1 begin S2  ε-closure(move(S1,a)) if (S2 is not in DS) then add S2 into DS as an unmarked state transfunc[S1,a]  S2 end end• a state S in DS is an accepting state of DFA if a state in S is an accepting state of NFA• the start state of DFA is ε-closure({s0}) Notes | CodeReplugd
    29. 29. Converting a NFA into a DFA (Example) 2 3 a ε 0 1 ε ε 6 7 8 ε a ε ε 4 5 b εS0 = ε-closure({0}) = {0,1,2,4,7} S0 into DS as an unmarked state ⇓ mark S0ε-closure(move(S0,a)) = ε-closure({3,8}) = {1,2,3,4,6,7,8} = S1 S1 into DSε-closure(move(S0,b)) = ε-closure({5}) = {1,2,4,5,6,7} = S2 S2 into DS transfunc[S0,a]  S1 transfunc[S0,b]  S2 ⇓ mark S1ε-closure(move(S1,a)) = ε-closure({3,8}) = {1,2,3,4,6,7,8} = S1ε-closure(move(S1,b)) = ε-closure({5}) = {1,2,4,5,6,7} = S2 transfunc[S1,a]  S1 transfunc[S1,b]  S2 ⇓ mark S2ε-closure(move(S2,a)) = ε-closure({3,8}) = {1,2,3,4,6,7,8} = S1ε-closure(move(S2,b)) = ε-closure({5}) = {1,2,4,5,6,7} = S2 transfunc[S2,a]  S1 transfunc[S2,b]  S2 Notes | CodeReplugd
    30. 30. Converting a NFA into a DFA (Example – cont.)S0 is the start state of DFA since 0 is a member of S0={0,1,2,4,7}S1 is an accepting state of DFA since 8 is a member of S1 = {1,2,3,4,6,7,8} a S1 a S0 b a b S2 b Notes | CodeReplugd
    31. 31. Converting Regular Expressions Directly to DFAs• We may convert a regular expression into a DFA (without creating a NFA first).• First we augment the given regular expression by concatenating it with a special symbol #. r  (r)# augmented regular expression• Then, we create a syntax tree for this augmented regular expression.• In this syntax tree, all alphabet symbols (plus # and the empty string) in the augmented regular expression will be on the leaves, and all inner nodes will be the operators in that augmented regular expression.• Then each alphabet symbol (plus #) will be numbered (position numbers). Notes | CodeReplugd
    32. 32. Regular Expression  DFA (cont.)(a|b) * a  (a|b) * a # augmented regular expression • Syntax tree of (a|b) * a # • # 4 * a 3 • each symbol is numbered (positions) | • each symbol is at a leave a b 1 2 • inner nodes are operators Notes | CodeReplugd
    33. 33. followposThen we define the function followpos for the positions (positionsassigned to leaves). followpos(i) -- is the set of positions which can follow the position i in the strings generated by the augmented regular expression.For example, ( a | b) * a # 1 2 3 4 followpos(1) = {1,2,3} followpos(2) = {1,2,3} followpos is just defined for leaves, it is not defined for inner nodes. followpos(3) = {4} followpos(4) = {} Notes | CodeReplugd
    34. 34. firstpos, lastpos, nullable• To evaluate followpos, we need three more functions to be defined for the nodes (not just for leaves) of the syntax tree.• firstpos(n) -- the set of the positions of the first symbols of strings generated by the sub-expression rooted by n.• lastpos(n) -- the set of the positions of the last symbols of strings generated by the sub-expression rooted by n.• nullable(n) -- true if the empty string is a member of strings generated by the sub-expression rooted by n false otherwise Notes | CodeReplugd
    35. 35. How to evaluate firstpos, lastpos, nullable n nullable(n) firstpos(n) lastpos(n)leaf labeled ε true Φ Φleaf labeled false {i} {i}with position i | nullable(c1) or firstpos(c1) ∪ firstpos(c2) lastpos(c1) ∪ lastpos(c2) c1 c2 nullable(c2) • nullable(c1) and if (nullable(c1)) if (nullable(c2)) c1 c2 nullable(c2) firstpos(c1) ∪ firstpos(c2) lastpos(c1) ∪ lastpos(c2) * true else firstpos(c1) firstpos(c1) else lastpos(c2) lastpos(c1) c1 Notes | CodeReplugd
    36. 36. How to evaluate followpos• Two-rules define the function followpos:1. If n is concatenation-node with left child c1 and right child c2, and i is a position in lastpos(c1), then all positions in firstpos(c2) are in followpos(i).2. If n is a star-node, and i is a position in lastpos(n), then all positions in firstpos(n) are in followpos(i).• If firstpos and lastpos have been computed for each node, followpos of each position can be computed by making one depth- first traversal of the syntax tree. Notes | CodeReplugd
    37. 37. Example -- ( a | b) * a # green – firstpos {1,2,3} • {4} blue – lastpos {1,2,3} {3} {4}# {4} • 4 {1,2}*{1,2}{3}a{3} 3 Then we can calculate followpos {1,2} | {1,2} {1} a {1} {2}b {2} followpos(1) = {1,2,3} 1 2 followpos(2) = {1,2,3} followpos(3) = {4} followpos(4) = {}• After we calculate follow positions, we are ready to create DFA for the regular expression. Notes | CodeReplugd
    38. 38. Algorithm (RE  DFA)• Create the syntax tree of (r) #• Calculate the functions: followpos, firstpos, lastpos, nullable• Put firstpos(root) into the states of DFA as an unmarked state.• while (there is an unmarked state S in the states of DFA) do – mark S – for each input symbol a do • let s1,...,sn are positions in S and symbols in those positions are a • S’  followpos(s1) ∪ ... ∪ followpos(sn) • move(S,a)  S’ • if (S’ is not empty and not in the states of DFA) – put S’ into the states of DFA as an unmarked state.• the start state of DFA is firstpos(root)• the accepting states of DFA are all states containing the position of # Notes | CodeReplugd
    39. 39. Example -- ( a | b) * a # 1 2 3 4followpos(1)={1,2,3} followpos(2)={1,2,3} followpos(3)={4} followpos(4)={}S1=firstpos(root)={1,2,3} ⇓ mark S1a: followpos(1) ∪ followpos(3)={1,2,3,4}=S2 move(S1,a)=S2b: followpos(2)={1,2,3}=S1 move(S1,b)=S1 ⇓ mark S2a: followpos(1) ∪ followpos(3)={1,2,3,4}=S2 move(S2,a)=S2b: followpos(2)={1,2,3}=S1 move(S2,b)=S1 b a a S1 S2 bstart state: S1accepting states: {S2} Notes | CodeReplugd
    40. 40. Example -- ( a | ε) b c* # 1 2 3 4followpos(1)={2} followpos(2)={3,4} followpos(3)={3,4}followpos(4)={}S1=firstpos(root)={1,2} ⇓ mark S1a: followpos(1)={2}=S2 move(S1,a)=S2b: followpos(2)={3,4}=S3 move(S1,b)=S3 ⇓ mark S2b: followpos(2)={3,4}=S3 move(S2,b)=S3 S2 ⇓ mark S3 a bc: followpos(3)={3,4}=S3 move(S3,c)=S3 S1 b S3 cstart state: S1 Notes | CodeReplugd
    41. 41. Minimizing Number of States of a DFA• partition the set of states into two groups: – G1 : set of accepting states – G2 : set of non-accepting states• For each new group G – partition G into subgroups such that states s1 and s2 are in the same group iff for all input symbols a, states s1 and s2 have transitions to states in the same group.• Start state of the minimized DFA is the group containing the start state of the original DFA.• Accepting states of the minimized DFA are the groups containing the accepting states of the original DFA. Notes | CodeReplugd
    42. 42. Minimizing DFA - Example a G1 = {2} 2 a G2 = {1,3} 1 b a b 3 G2 cannot be partitioned because move(1,a)=2 move(1,b)=3 b move(3,a)=2 move(2,b)=3So, the minimized DFA (with minimum states) b a {1,3} a {2} b Notes | CodeReplugd
    43. 43. Minimizing DFA – Another Example a 2 a a Groups: {1,2,3} {4} 1 b 4 a b {1,2} {3} a b 3 b no more partitioning 1->2 1->3 2->2 2->3 b 3->4 3->3So, the minimized DFA b {3} a b {1,2} a b a {4} Notes | CodeReplugd
    44. 44. Some Other Issues in Lexical Analyzer• The lexical analyzer has to recognize the longest possible string. – Ex: identifier newval -- n ne new newv newva newval• What is the end of a token? Is there any character which marks the end of a token? – It is normally not defined. – If the number of characters in a token is fixed, in that case no problem: + - – But <  < or <> (in Pascal) – The end of an identifier : the characters cannot be in an identifier can mark the end of token. – We may need a lookhead • In Prolog: p :- X is 1. p :- X is 1.5. The dot followed by a white space character can mark the end of a number. But if that is not the case, the dot must be treated as a part of the number. Notes | CodeReplugd
    45. 45. Some Other Issues in Lexical Analyzer (cont.)• Skipping comments – Normally we don’t return a comment as a token. – We skip a comment, and return the next token (which is not a comment) to the parser. – So, the comments are only processed by the lexical analyzer, and the don’t complicate the syntax of the language.• Symbol table interface – symbol table holds information about tokens (at least lexeme of identifiers) – how to implement the symbol table, and what kind of operations. • hash table – open addressing, chaining • putting into the hash table, finding the position of a token from its lexeme.• Positions of the tokens in the file (for the error handling). Notes | CodeReplugd

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