Languages and Compilers SProg og Oversttere Bent Thomsen
Languages and Compilers (SProg og Oversættere) Bent Thomsen Department of Computer Science Aalborg University With acknowledgement to Norm Hutchinson whose slides this lecture is based on. 1
Today’s lecture • Three topics – Treating Compilers and Interpreters as black-boxes • Tombstone- or T- diagrams – A first look inside the black-box • Your guided tour – Some Language Design Issues 2
Terminology Q: Which programming languages play a role in this picture? input source program Translator is expressed in the source language output object program is expressed in the target language is expressed in the implementation language A: All of them! 3
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”) Program P implemented in L P L Machine implemented in hardware M Translator implemented in L S -> T L Language interpreter in L M L 4
Tombstone diagrams: Combination rules P M M P L M OK! P S S -> T M M OK! WRONG! P L WRONG! P T OK! S -> T M 5
Compilation Example: Compilation of C programs on an x 86 machine Tetris C C -> x 86 Tetris x 86 6
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Cross compilation Example: A C “cross compiler” from x 86 to PPC A cross compiler is a compiler which runs on one machine (the host machine) but emits code for another machine (the target machine). Tetris C C -> PPC x 86 Tetris PPC download Tetris PPC Host ≠ Target Q: Are cross compilers useful? Why would/could we use them? 8
Two Stage Compilation A two-stage translator is a composition of two translators. The output of the first translator is provided as input to the second translator. Tetris Java->JVM JVM->x 86 x 86 9
Compiling a Compiler Observation: A compiler is a program! Therefore it can be provided as input to a language processor. Example: compiling a compiler. Java->x 86 C -> x 86 C x 86 10
Interpreters An interpreter is a language processor implemented in software, i. e. as a program. Terminology: abstract (or virtual) machine versus real machine Example: The Java Virtual Machine Tetris JVM x 86 Q: Why are abstract machines useful? 11
Interpreters Q: Why are abstract machines useful? 1) Abstract machines provide better platform independence Tetris JVM x 86 Tetris JVM PPC 12
Interpreters Q: Why are abstract machines useful? 2) Abstract machines are useful for testing and debugging. Example: Testing the “Ultima” processor using hardware emulation P Ultima x 86 P Ultima Functional equivalence Note: we don’t have to implement Ultima emulator in x 86 we can use a high-level language and compile it. 13
Interpreters versus Compilers Q: What are the tradeoffs between compilation and interpretation? Compilers typically offer more advantages when – programs are deployed in a production setting – programs are “repetitive” – the instructions of the programming language are complex Interpreters typically are a better choice when – – we are in a development/testing/debugging stage programs are run once and then discarded the instructions of the language are simple the execution speed is overshadowed by other factors • e. g. on a web server where communications costs are much higher than execution speed 14
Interpretive Compilers Why? A tradeoff between fast(er) compilation and a reasonable runtime performance. How? Use an “intermediate language” • more high-level than machine code => easier to compile to • more low-level than source language => easy to implement as an interpreter Example: A “Java Development Kit” for machine M Java->JVM M 15
Interpretive Compilers Example: Here is how we use our “Java Development Kit” to run a Java program P P Java javac P Java->JVM M M java P JVM M M 16
Portable Compilers Example: Two different “Java Development Kits” Kit 1: Java->JVM M Kit 2: Java->JVM JVM M Q: Which one is “more portable”? 17
Portable Compilers In the previous example we have seen that portability is not an “all or nothing” kind of deal. It is useful to talk about a “degree of portability” as the percentage of code that needs to be re-written when moving to a dissimilar machine. In practice 100% portability is as good as impossible. 18
Example: a “portable” compiler kit Portable Compiler Kit: Java->JVM Java Q: Suppose we want to run this kit on some machine M. How could we go about realizing that goal? (with the least amount of effort) 19
Example: a “portable” compiler kit Java->JVM Java Q: Suppose we want to run this kit on some machine M. How could we go about realizing that goal? (with the least amount of effort) JVM Java reimplement JVM C C->M JVM M 20
Example: a “portable” compiler kit This is what we have now: Java->JVM Java JVM M Now, how do we run our Tetris program? Tetris Java->JVM JVM M M Tetris JVM M M 21
Bootstrapping Remember our “portable compiler kit”: Java->JVM Java JVM M We haven’t used this yet! Java->JVM Java Same language! Q: What can we do with a compiler written in itself? Is that useful at all? 22
Bootstrapping Java->JVM Java Same language! Q: What can we do with a compiler written in itself? Is that useful at all? • By implementing the compiler in (a subset of) its own language, we become less dependent on the target platform => more portable implementation. • But… “chicken and egg problem”? How do to get around that? => BOOTSTRAPPING: requires some work to make the first “egg”. There are many possible variations on how to bootstrap a compiler written in its own language. 23
Bootstrapping an Interpretive Compiler to Generate M code Our “portable compiler kit”: Java->JVM Java JVM M Goal we want to get a “completely native” Java compiler on machine M P P Java->M Java M M M 24
Bootstrapping an Interpretive Compiler to Generate M code (first approach) Step 1: implement Java ->M Java by rewriting Java ->JVM Java Step 2: compile it Java->M Java->JVM JVM M M Step 3: Use this to compile again 25
Bootstrapping an Interpretive Compiler to Generate M code (first approach) Step 3: “Self compile” the Java (in Java) compiler Java->M Java M JVM M M This is our desired compiler! Step 4: use this to compile the P program P Java->M P M M 26
Bootstrapping an Interpretive Compiler to Generate M code (second approach) Idea: we will build a two-stage Java -> M compiler. P Java->JVM M M M We will make this by compiling Java->JVM JVM->M M M P M To get this we implement JVM->M Java and compile it 27
Bootstrapping an Interpretive Compiler to Generate M code (second approach) Step 1: implement JVM->M Java Step 2: compile it JVM->M Java->JVM JVM M M Step 3: compile this 28
Bootstrapping an Interpretive Compiler to Generate M code (second approach) Step 3: “Self compile” the JVM (in JVM) compiler JVM->M M JVM M M This is the second stage of our compiler! Step 4: use this to compile the Java compiler 29
Bootstrapping an Interpretive Compiler to Generate M code Step 4: Compile the Java->JVM compiler into machine code Java->JVM JVM->M M The first stage of our compiler! We are DONE! P Java->JVM M M M JVM->M P M M M 30
Full Bootstrap A full bootstrap is necessary when we are building a new compiler from scratch. Example: We want to implement an Ada compiler for machine M. We don’t currently have access to any Ada compiler (not on M, nor on any other machine). Idea: Ada is very large, we will implement the compiler in a subset of Ada and bootstrap it from a subset of Ada compiler in another language. (e. g. C) v 1 Step 1: build a compiler for Ada-S ->M in another language C 31
Full Bootstrap Step 1 a: build a compiler (v 1) for Ada-S in another language. v 1 Ada-S ->M C Step 1 b: Compile v 1 compiler on M v 1 Ada-S ->M Ada-S->M C M M This compiler can be used for M bootstrapping on machine M but we do not want to rely on it permanently! 32
Full Bootstrap Step 2 a: Implement v 2 of Ada-S compiler in Ada-S v 2 Ada-S ->M Q: Is it hard to rewrite the compiler in Ada-S? Ada-S Step 2 b: Compile v 2 compiler with v 1 compiler v 2 v 1 Ada-S->M Ada-S ->M M M We are now no longer dependent on the availability of a C compiler! 33
Full Bootstrap Step 3 a: Build a full Ada compiler in Ada-S v 3 Ada->M Ada-S Step 3 b: Compile with v 2 compiler v 3 Ada->M v 2 Ada->M M Ada-S ->M M M From this point on we can maintain the compiler in Ada. Subsequent versions v 4, v 5, . . . of the compiler in Ada and compile each with the previous version. 34
Half Bootstrap We discussed full bootstrap which is required when we have no access to a compiler for our language at all. Q: What if we have access to an compiler for our language on a different machine HM but want to develop one for TM ? We have: Ada->HM HM Ada->HM Ada We want: Ada->TM TM Idea: We can use cross compilation from HM to TM to bootstrap the TM compiler. 35
Half Bootstrap Idea: We can use cross compilation from HM to bootstrap the M compiler. Step 1: Implement Ada->TM compiler in Ada->TM Ada Step 2: Compile on HM Ada->TM Ada->HM HM Cross compiler: running on HM but emits TM code 36
Half Bootstrap Step 3: Cross compile our TM compiler. Ada->TM TM HM HM DONE! From now on we can develop subsequent versions of the compiler completely on TM 37
Bootstrapping to Improve Efficiency The efficiency of programs and compilers: Efficiency of programs: - memory usage - runtime Efficiency of compilers: - Efficiency of the compiler itself - Efficiency of the emitted code Idea: We start from a simple compiler (generating inefficient code) and develop more sophisticated version of it. We can then use bootstrapping to improve performance of the compiler. 38
Bootstrapping to Improve Efficiency We have: Ada->Mslow Ada-> Mslow Ada Step 1 We implement: Ada->Mfast Ada-> Mslow M Step 2 Ada->Mfast Ada-> Mfast Mslow Fast compiler that emits fast code! M 39
Conclusion • • • To write a good compiler you may be writing several simpler ones first You have to think about the source language, the target language and the implementation language. Strategies for implementing a compiler 1. Write it in machine code 2. Write it in a lower level language and compile it using an existing compiler 3. Write it in the same language that it compiles and bootstrap • The work of a compiler writer is never finished, there is always version 1. x and version 2. 0 and … 40
Compilation So far we have treated language processors (including compilers) as “black boxes” Now we take a first look "inside the box": how are compilers built. And we take a look at the different “phases” and their relationships 41
The “Phases” of a Compiler Source Program Syntax Analysis Error Reports Abstract Syntax Tree Contextual Analysis Error Reports Decorated Abstract Syntax Tree Code Generation Object Code 42
Different Phases of a Compiler The different phases can be seen as different transformation steps to transform 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 43
Example Program We now look at each of the three different phases in a little more detail. We look at each of the steps in transforming an example Triangle program into TAM code. ! This program is useless except for ! illustration let var n: integer; var c: char in begin c : = ‘&’; n : = n+1 end 44
1) Syntax Analysis Source Program Syntax Analysis Error Reports Abstract Syntax Tree Note: Not all compilers construct an explicit representation of an AST. (e. g. on a “single pass compiler” generally no need to construct an AST) 45
1) Syntax Analysis -> AST Program Let. Command Sequential. Declaration Assign. Command Var. Decl Simple. T Ident n Ident Integer Char. Expr Binary. Expr VName. Exp Int. Expr Simple. T Simple. V Ident c Char Simple. V Ident Char. Lit Ident c ‘&’ n Ident Op Int. Lit n + 1 46
2) Contextual Analysis -> Decorated AST Abstract Syntax Tree Contextual Analysis Error Reports Decorated Abstract Syntax Tree Contextual analysis: • Scope checking: verify that all applied occurrences of identifiers are declared • Type checking: verify that all operations in the program are used according to their type rules. Annotate AST: • Applied identifier occurrences => declaration • Expressions => Type 47
2) Contextual Analysis -> Decorated AST Program Let. Command Sequential. Declaration Assign. Command Binary. Expr : int Assign. Command Var. Decl Simple. T Ident n Integer Char. Expr : char VName. Exp Int. Expr : int Simple. T Simple. V : char Ident c Char : int Ident Char. Lit Ident c ‘&’ : int n Ident Op Int. Lit n + 1 48
Contextual Analysis Finds scope and type errors. Example 1: Assign. Command : int ***TYPE ERROR (incompatible types in assigncommand) : char Example 2: foo not found Simple. V ***SCOPE ERROR: undeclared variable foo Ident foo 49
3) Code Generation Decorated Abstract Syntax Tree Code Generation Object Code • Assumes that program has been thoroughly checked and is well formed (scope & type rules) • Takes into account semantics of the source language as well as the target language. • Transforms source program into target code. 50
3) Code Generation let var n: integer; var c: char in begin c : = ‘&’; n : = n+1 end Var. Decl address = 0[SB] PUSH 2 LOADL 38 STORE 1[SB] LOAD 0 LOADL 1 CALL add STORE 0[SB] POP 2 HALT Simple. T Ident n Integer 51
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. 52
Single Pass Compiler A single pass compiler makes a single pass over the source text, parsing, analyzing and generating code all at once. Dependency diagram of a typical Single Pass Compiler: Compiler Driver calls Syntactic Analyzer calls Contextual Analyzer calls Code Generator 53
Multi Pass Compiler A multi pass compiler makes several passes over the program. The output of a preceding phase is stored in a data structure and used by subsequent phases. Dependency diagram of a typical Multi Pass Compiler: Compiler Driver calls Syntactic Analyzer Contextual Analyzer Code Generator input output Source Text AST Decorated AST Object Code 54
Example: The Triangle Compiler Driver public class Compiler { public static void compile. Program(. . . ) { Parser parser = new Parser(. . . ); Checker checker = new Checker(. . . ); Encoder generator = new Encoder(. . . ); Program the. AST = parser. parse(); checker. check(the. AST); generator. encode(the. AST); } } public void main(String[] args) {. . . compile. Program(. . . ). . . } 55
Compiler Design Issues Single Pass Multi Pass Speed better worse Memory Modularity better for large programs worse (potentially) better for small programs better Flexibility worse better “Global” optimization impossible Source Language single pass compilers are not possible for many programming languages 56
Language Issues Example Pascal: Pascal was explicitly designed to be easy to implement with a single pass compiler: – Every identifier must be declared before it is first use. ? var n: integer; procedure inc; begin n: =n+1 end; Undeclared Variable! var n: integer; 57
Language Issues Example Pascal: – Every identifier must be declared before it is used. – How to handle mutual recursion then? procedure ping(x: integer) begin. . . pong(x-1); . . . end; procedure pong(x: integer) begin. . . ping(x); . . . end; 58
Language Issues Example Pascal: – Every identifier must be declared before it is used. – How to handle mutual recursion then? forward procedure pong(x: integer) procedure ping(x: integer) begin. . . pong(x-1); . . . end; OK! procedure pong(x: integer) begin. . . ping(x); . . . end; 59
Language Issues Example Java: – identifiers can be declared before they are used. – thus a Java compiler need at least two passes Class Example { void inc() { n = n + 1; } int n; void use() { n = 0 ; inc(); } } 60
Scope of Variable • Range of program that can reference that variable (ie access the corresponding data object by the variable’s name) • Variable is local to program or block if it is declared there • Variable is nonlocal to program unit if it is visible there but not declared there 61
Static vs. Dynamic Scope • Under static, sometimes called lexical, scope, sub 1 will always reference the x defined in big • Under dynamic scope, the x it references depends on the dynamic state of execution procedure big; var x: integer; procedure sub 1; begin {sub 1}. . . x. . . end; {sub 1} procedure sub 2; var x: integer; begin {sub 2}. . . sub 1; . . . end; {sub 2} begin {big}. . . sub 1; sub 2; . . . end; {big} 62
Static Scoping • Scope computed at compile time, based on program text • To determine the name of a used variable we must find statement declaring variable • Subprograms and blocks generate hierarchy of scopes – Subprogram or block that declares current subprogram or contains current block is its static parent • General procedure to find declaration: – First see if variable is local; if yes, done – If non-local to current subprogram or block recursively search static parent until declaration is found – If no declaration is found this way, undeclared variable error detected 63
Example program main; var x : integer; procedure sub 1; var x : integer; begin { sub 1 } …x… end; { sub 1 } begin { main } …x… end; { main } 64
Dynamic Scope • Now generally thought to have been a mistake • Main example of use: original versions of LISP – Scheme uses static scope – Perl allows variables to be declared to have dynamic scope • Determined by the calling sequence of program units, not static layout • Name bound to corresponding variable most recently declared among still active subprograms and blocks 65
Example program main; var x : integer; procedure sub 1; begin { sub 1 } …x… end; { sub 1 } procedure sub 2; var x : integer; begin { sub 2 } … call sub 1 … end; { sub 2 } … call sub 2… end; { main } 66
Binding • Binding: an association between an attribute and its entity • Binding Time: when does it happen? • … and, when can it happen? 67
Binding of Data Objects and Variables • Attributes of data objects and variables have different binding times • If a binding is made before run time and remains fixed through execution, it is called static • If the binding first occurs or can change during execution, it is called dynamic 68
Binding Time Static • • • Language definition time Language implementation time Program writing time Compile time Link time Load time Dynamic • Run time – – At the start of execution (program) On entry to a subprogram or block When the expression is evaluated When the data is accessed 69
X = X + 10 • • • Set of types for variable X Type of variable X Set of possible values for variable X Value of variable X Scope of X – lexical or dynamic scope • Representation of constant 10 – Value (10) – Value representation (10102) • big-endian vs. little-endian – Type (int) – Storage (4 bytes) • stack or global allocation • Properties of the operator + – Overloaded or not 70
Little- vs. Big-Endians • Big-endian – A computer architecture in which, within a given multi-byte numeric representation, the most significant byte has the lowest address (the word is stored `big-end-first'). – Motorola and Sun processors • Little-endian – a computer architecture in which, within a given 16 - or 32 -bit word, bytes at lower addresses have lower significance (the word is stored `little-end-first'). – Intel processors from The Jargon Dictionary - http: //info. astrian. net/jargon 71
Binding Times summary • Language definition time: – language syntax and semantics, scope discipline • Language implementation time: – interpreter versus compiler, – aspects left flexible in definition, – set of available libraries • Compile time: – some initial data layout, internal data structures • Link time (load time): – binding of values to identifiers across program modules • Run time (execution time): – actual values assigned to non-constant identifiers The Programming language designer and compiler implementer have to make decisions about binding times 72
Syntax Design Criteria • Readability – syntactic differences reflect semantic differences – verbose, redundant • Writeability – concise • Ease of translation – simple language – simple semantics • Lack of ambiguity – dangling else – Fortran’s A(I, J) • Ease of verifiability – simple semantics 73
Lexical Elements • • • Character set Identifiers Operators Keywords Noise words Elementary data • Comments • Blank space • Layout – Free- and fixed-field formats – numbers • integers • floating point – strings – symbols • Delimiters 74
Some nitty gritty decisions • Primitive data – Integers, floating points, bit strings – Machine dependent or independent (standards like IEEE) – Boxed or unboxed • Character set – ASCII, EBCDIC, UNICODE • Identifiers – Length, special start symbol (#, $. . . ), type encode in start letter • Operator symbols – Infix, prefix, postfix, precedence • Comments – REM, /* …*/, //, !, … • Blanks • Delimiters and brackets • Reserved words or Keywords 75
Syntactic Elements • • Definitions Declarations Expressions Statements • • Separate subprogram definitions (Module system) Separate data definitions Nested subprogram definitions Separate interface definitions 76
Overall Program Structure • Subprograms – shallow definitions • C – nested definitions • Pascal • Data (OO) – shallow definitions • C++, Java, Smalltalk • Separate Interface – C, Fortran – ML, Ada • Mixed data and programs – C – Basic • Others – Cobol • Data description separated from executable statements • Data and procedure division 77
Some more Programming Language Design Issues • A Programming model (sometimes called the computer) is defined by the language semantics – More about this in the semantics course • Programming model given by the underlying system – Hardware platform and operating system • The mapping between these two programming models (or computers) that the language processing system must define can be influenced in both directions – E. g. low level features in high level languages • Pointers, arrays, for-loops – Hardware support for fast procedure calls 78
Programming Language Implementation • Develop layers of machines, each more primitive than the previous • Translate between successive layers • End at basic layer • Ultimately hardware machine at bottom • To design programming languages and compilers, we thus need to understand a bit about computers ; -) 79
Why So Many Computers? • It is economically feasible to produce in hardware (or firmware) only relatively simple computers • More complex or abstract computers are built in software • There are exceptions – EDS machine to run prolog (or rather WAM) – Alice Machine to run Hope 80
Machines • Hardware computer: built out of wires, gates, circuit boards, etc. – An elaboration of the Von Neumann Machine • Software simulated computer: that implemented in software, which runs on top of another computer • • • Data Primitive Operations Sequence Control Data Access Storage Management Operating Environment 81
Memory and data • Memory – Registers • PC, data or address – Main memory (fixed length words 32 or 64 bits) – Cache – External • Disc, CD-ROM, memory stick, tape drives – Order of magnitude in access speed • Nanoseconds vs. milliseconds • Built-in data types – integers, floating point, fixed length strings, fixed length bit strings 82
Hardware computer • Operations – – Arithmetic on primitive data Tests (test for zero, positive or negative) Primitive access and modification Jumps (unconditional, return) • Sequence control – Next instruction in PC (location counter) – Some instructions modify PC • Data access – Reading and writing – Words from main memory, Blocks from external storage • Storage management – Wait for data or multi-programming – Paging – Cache (32 K usually gives 95% hit rate) 83
Virtual Computers • How can we execute programs written in the high-level computer, given that all we have is the low-level computer? – Compilation • Translate instructions to the high-level computer to those of the low-level – Simulation (interpretation) • create a virtual machine – Sometimes the simulation is done by hardware • This is called firmware 84
Micro Program interpretation and execution Fetch next instruction Decode instruction Operation and operands Fetch designated operands Branch to designated operation Execute Primitive Operation Execute halt 85
A Six-Level Computer Level 5 Applications Application Level Compilers, Editors, Navigators Assembly Language Level 3 Assembler, Linker, Loader Operating System Machine Level 2 Software Level 4 Operating System Instruction Set Architecture Level Microprogram or hardware Microarchitecture Level 0 Hardware Digital Logic Level from Andrew S. Tanenbaum, Structured Computer Organization, 4 th Edition, Prentice Hall, 1999. Hardware Level 1 86
Keep in mind There are many issues influencing the design of a new programming language: – Choice of paradigm – Syntactic preferences – Even the compiler implementation • e. g no of passes • available tools There are many issues influencing the design of new compiler: – No of passes – The source, target and implementation language – Available tools 87
Some advice from an expert • • Programming languages are for people Design for yourself and your friends Give the programmer as much control as possible Aim for brevity 88
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