Types Aaron Bloomfield CS 415 Fall 2005 1

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Types Aaron Bloomfield CS 415 Fall 2005 1

Types Aaron Bloomfield CS 415 Fall 2005 1

Why have Types? • Types provide context for operations – a+b what kind of

Why have Types? • Types provide context for operations – a+b what kind of addition? – pointer p = new object how much space? • Limit valid set of operations – int a = “string” know error before run time 2

Computer Hardware & Types • Remember CS 216? – Bits in data are generally

Computer Hardware & Types • Remember CS 216? – Bits in data are generally untyped – Bits interpreted as having a type, such as: • Instruction • Address • Floating-point number • Character • High-level language interprets bits 3

Type assignment • Language-dependent • a+b example again: – Ada: a and b declared

Type assignment • Language-dependent • a+b example again: – Ada: a and b declared with static types – Perl: $a and $b are dynamically typed – Scheme: checked at run-time – Smalltalk: checked at run-time – Bliss: completely untyped 4

Static vs. Dynamic Typing • First, what does strongly typed mean? – Prevents invalid

Static vs. Dynamic Typing • First, what does strongly typed mean? – Prevents invalid operations on a type • Statically typed: strongly typed and type checks can be performed at compile time – Ada, Pascal: almost all type checking done at compile time, with just a few exceptions – C, C++, Java, Ocaml • Dynamically typed: type checking is delayed until run time – Scheme, Smalltalk, Perl 5

Defining/Declaring a Type • Fortran: variable’s type known from name – Letters I-N =

Defining/Declaring a Type • Fortran: variable’s type known from name – Letters I-N = integer type – Otherwise = real (floating-point) type • Scheme, Smalltalk: run-time typing • Most languages (such as C++): user explicitly declares type – int a, char b 6

Classifying types • A type is either one of a small group of built-in

Classifying types • A type is either one of a small group of built-in types or a user-defined composite • Built-in types include: – – – Integers Characters: ASCII, Unicode Boolean (not in C/C++) Floating points (double, real, float, etc. ) Rarer types: • Complex (Scheme, Fortran) • Rational (Scheme) • Fixed point (Ada) 7

Number precision • Is the precision specified by the type in the language implementation?

Number precision • Is the precision specified by the type in the language implementation? – Java, C, C++ Fortran: user specifies precision • Java: byte, short, int, long – Most other languages: implementation – Is the precision consistent across platforms? • Java: Yes • C/C++: No 8

Number precision • If user doesn’t specify type, code can lose portability due to

Number precision • If user doesn’t specify type, code can lose portability due to differences in precision – In C/C++, precision is platform-dependent • Neat example in Haskell – 2 ^ 200 1606938044258990275541962092341162602522202993782792835301376 – Support for infinite precision types – Java provides Big. Integer and Big. Decimal for this purpose 9

Type Equivalence • Determining when the types of two values are the same struct

Type Equivalence • Determining when the types of two values are the same struct student { string name; string address; } struct school { string name; string address; } • Are these the same? be? Should they 10

Structural Equivalence • The same components, put together the same way = same type

Structural Equivalence • The same components, put together the same way = same type • Algol-68, early Pascal, C (with exceptions) – And ML, somewhat • Straightforward and easy to implement • Definition varies from lang to lang – eg. Does the ORDER of the fields matter? • Back to example: are they the same? struct student { string name; string address; } struct school { string name; string address; } • Yes, they are (with structural equivalence) 11

Name Equivalence • • More popular recently A new name or definition = a

Name Equivalence • • More popular recently A new name or definition = a new type Java, current Pascal, Ada Assume that: if programmer wrote two different definitions, then wanted two types – Is this a good or bad assumption to make? • Back to example: are they the same? struct student { string name; string address; } struct school { string name; string address; } • No, they are not (with name equivalence) 12

Problem! Aliases • Modula-2 example: TYPE celsius_temp = REAL; TYPE fahren_temp = REAL; VAR

Problem! Aliases • Modula-2 example: TYPE celsius_temp = REAL; TYPE fahren_temp = REAL; VAR c : celsius_temp; f : fahren_temp; … f : = c; • Modula has loose name equivalence, so this is okay – But normally it probably should be an error 13

Types of Name Equivalence • Strict: aliases are distinct types • Loose: aliases are

Types of Name Equivalence • Strict: aliases are distinct types • Loose: aliases are equivalent types • Ada has both: – type test_score is integer; – type celsius_temp is new integer; type fahren_temp is new integer; • A derived type is incompatible with its parent type • now f : = c will generate an error 14

Type Conversion • Conversion needed when certain types are expected but not received •

Type Conversion • Conversion needed when certain types are expected but not received • a+b expecting either 2 ints or 2 floats • Switching between types may result in overflow – example: floating-point integer 15

Coercion • Can automatically force values to another type for the given context •

Coercion • Can automatically force values to another type for the given context • a+b revisited: – Without coercion, if both aren’t the same type then there’s a compile-time error – With coercion, if either is floating-point, then addition is floating-point; otherwise int addition • Issue: Things are happening without you asking them to occur! Good/Bad? 16

Data Types Records (Structures) Variants (Unions) Arrays Sets 17

Data Types Records (Structures) Variants (Unions) Arrays Sets 17

Records • Also known as ‘structs’ and ‘types’. –C struct resident { char initials[2];

Records • Also known as ‘structs’ and ‘types’. –C struct resident { char initials[2]; int ss_number; bool married; }; • fields – the components of a record, usually referred to using dot notation. 18

Nesting Records • Most languages allow records to be nested within each other. –

Nesting Records • Most languages allow records to be nested within each other. – Pascal type two_chars = array [1. . 2] of char; type married_resident = record initials: two_chars; ss_number: integer; incomes: record husband_income: integer; wife_income: integer; end; 19

Memory Layout of Records • Fields are stored adjacently in memory. • Memory is

Memory Layout of Records • Fields are stored adjacently in memory. • Memory is allocated for records based on the order the fields are created. • Variables are aligned for easy reference. Optimized for space Optimized for memory alignment 4 bytes / 32 bits initials (2 bytes) married (1 byte) initials (2 bytes) ss_number (4 bytes) married (1 byte) 20

Simplifying Deep Nesting • Modifying records with deep nesting can become bothersome. book[3]. volume[7].

Simplifying Deep Nesting • Modifying records with deep nesting can become bothersome. book[3]. volume[7]. issue[11]. name : = ‘Title’; book[3]. volume[7]. issue[11]. cost : = 199; book[3]. volume[7]. issue[11]. in_print : = TRUE; • Fortunately, this problem can be simplified. • In Pascal, keyword with “opens” a record. with book[3]. volume[7]. issue[11] do begin name : = ‘Title’; cost : = 199; in_print : = TRUE; end; 21

Simplifying Deep Nesting • Modula-3 and C provide better methods for manipulation of deeply

Simplifying Deep Nesting • Modula-3 and C provide better methods for manipulation of deeply nested records. – Modula-3 assigns aliases to allow multiple openings with var 1 = var 2 = DO var 1. name var 2. cost END; book[1]. volume[6]. issue[12], book[5]. volume[2]. issue[8] = var 2. name; = var 1. cost; – C allows pointers to types • What could you write in C to mimic the code above? 22

Variant Records • variant records – provide two or more alternative fields. • discriminant

Variant Records • variant records – provide two or more alternative fields. • discriminant – the field that determines which alternative fields to use. • Useful for when only one type of record can be valid at a given time. 23

Variant Records – Pascal Example type resident = record initials: array [1. . 2]

Variant Records – Pascal Example type resident = record initials: array [1. . 2] of char; initials (2 bytes) married (1 byte) husband_income (4 bytes) case married: boolean of true: ( husband_income: integer; wife_income: integer; ); false: ( income: real; ); id_number: integer; end; wife_income (4 bytes) Case is TRUE initials (2 bytes) married (1 byte) income (4 bytes) Case is FALSE 24

Unions • A union is like a record – But the different fields take

Unions • A union is like a record – But the different fields take up the same space within memory union foo { int i; float f; char c[4]; } • Union size is 4 bytes! 25

Union example (from an assembler) union Disasm. Inst { #ifdef BIG_ENDIAN struct { unsigned

Union example (from an assembler) union Disasm. Inst { #ifdef BIG_ENDIAN struct { unsigned char a, b, c, d; } chars; #else struct { unsigned char d, c, b, a; } chars; #endif intv; unsigned unsv; struct { unsigned offset: 16, rt: 5, rs: 5, op: 6; } itype; struct { unsigned offset: 26, op: 6; } jtype; struct { unsigned function: 6, sa: 5, rd: 5, rt: 5, rs: 5, op: 6; } rtype; }; 26

void Check. Endian() { union { charword[4]; unsigned intword; } check; check. charword[0] check.

void Check. Endian() { union { charword[4]; unsigned intword; } check; check. charword[0] check. charword[1] check. charword[2] check. charword[3] = = Another union example 1; 2; 3; 4; #ifdef BIG_ENDIAN if (check. intword != 0 x 01020304) { /* big */ cout << "ERROR: Host machine is not Big Endian. n. Exiting. n"; exit (1); } #else #ifdef LITTLE_ENDIAN if (check. intword != 0 x 04030201) { /* little */ cout << "ERROR: Host machine is not Little Endian. n. Exiting. n"; exit (1); } #else cout << "ERROR: Host machine not defined as Big or Little Endian. n"; cout << "Exiting. n"; exit (1); #endif // LITTLE_ENDIAN #endif // BIG_ENDIAN } 27

Arrays • Group a homogenous type into indexed memory. • Language differences: A(3) vs.

Arrays • Group a homogenous type into indexed memory. • Language differences: A(3) vs. A[3]. – Brackets are preferred since parenthesis are typically used for functions/subroutines. • Subscripts are usually integers, though most languages support any discrete type. 28

Array Dimensions • C uses 0 -> (n-1) as the array bounds. – float

Array Dimensions • C uses 0 -> (n-1) as the array bounds. – float values[10]; // ‘values’ goes from 0 -> 9 • Fortran uses 1 -> n as the array bounds. – real(10) values ! ‘values’ goes from 1 -> 10 • Some languages let the programmer define the array bounds. – var values: array [3. . 12] of real; (* ‘values’ goes from 3 -> 12 *) 29

Multidimensional Arrays • Two ways to make multidimensional arrays – Both examples from Ada

Multidimensional Arrays • Two ways to make multidimensional arrays – Both examples from Ada – Construct specifically as multidimensional. matrix: array (1. . 10, 1. . 10) of real; -- Reference example: matrix(7, 2) • Looks nice, but has limited functionality. – Construct as being an array of arrays. matrix: array (1. . 10) of real; -- Reference example: matrix(7)(2) • Allows us to take ‘slices’ of data. 30

Array Memory Allocation • An array’s “shape” (dimensions and bounds) determines how its memory

Array Memory Allocation • An array’s “shape” (dimensions and bounds) determines how its memory is allocated. – The time at which the shape is determined also plays a role in determining allocation. • At least 5 different cases for determining memory allocation: 31

Array Memory Allocation • Global lifetime, static shape: – The array’s shape is known

Array Memory Allocation • Global lifetime, static shape: – The array’s shape is known at compile time, and exists throughout the entire program. • Array can be allocated in static global memory. • int global_var[30]; void main() { }; • Local lifetime, static shape: – The array’s shape is known at compile time, but exists only as locally needed. • Array is allocated in subroutine’s stack frame. • void main() { int local_var[30]; } 32

Array Memory Allocation • Local lifetime, bound at elaboration time: – Array’s shape is

Array Memory Allocation • Local lifetime, bound at elaboration time: – Array’s shape is not known at compile time, and exists only as locally needed. • Array is allocated in subroutine’s stack frame and divided into fixed-size and variable-sized parts. • main() { var_ptr = new int[size]; } • Arbitrary lifetime, bound at elaboration time: – Array is just references to objects. • Java does not allocate space; just makes a reference to either new or existing objects. • var_ptr = new int[size]; 33

Array Memory Allocation • Arbitrary lifetime, dynamic shape – The array may shrink or

Array Memory Allocation • Arbitrary lifetime, dynamic shape – The array may shrink or grow as a result of program execution. • The array must be allocated from the heap. • Increasing size usually requires allocating new memory, copying from old memory, then de-allocating the old memory. 34

Memory Layout Options • Ordering of array elements can be accomplished in two ways:

Memory Layout Options • Ordering of array elements can be accomplished in two ways: – row-major order – Elements travel across rows, then across columns. – column-major order – Elements travel across columns, then across rows. Row-major Column-major 35

Row Pointers vs. Contiguous Allocation • Row pointers – an array of pointers to

Row Pointers vs. Contiguous Allocation • Row pointers – an array of pointers to an array. Creates a new dimension out of allocated memory. • Avoids allocating holes in memory. day[0] day[1] day[2] day[3] day[4] day[5] day[6] S u n d a y T u M o n e s d a y W e d n e s d a y T h u r s d a y F r i d a y S a u r d a y t Array = 57 bytes Pointers = 28 bytes Total Space = 85 bytes 36

Row Pointers vs. Contiguous Allocation • Contiguous allocation - array where each element has

Row Pointers vs. Contiguous Allocation • Contiguous allocation - array where each element has a row of allocated space. • This is a true multi-dimensional array. – It is also a ragged array S u n d a y M o n d a y T u e s d a y W e d n e s d a T h u r s d a y F r i d a y S a t u r d a y y Array = 70 bytes 37

Array Address Calculation • Calculate the size of an element (1 D) • Calculate

Array Address Calculation • Calculate the size of an element (1 D) • Calculate the size of a row (2 D) – row = element_size * (Uelement - Lelement + 1) • Calculate the size of a plane (3 D) – plane = row_size * (Urows - Lrows + 1) • Calculate the size of a cube (4 D) : : 38

Array Address Calculation • Address of a 3 -dimenional array A(i, j, k) is:

Array Address Calculation • Address of a 3 -dimenional array A(i, j, k) is: address of A + ((i - Lplane) * size of plane) + ((j - Lrow) * size of row) + ((k - Lelement) * size of element) A A(i) A(i, j, k) Memory 39

Sets • Introduced by Pascal, found in most recent languages as well. • Common

Sets • Introduced by Pascal, found in most recent languages as well. • Common implementation uses a bit vector to denote “is a member of”. – Example: U = {‘a’, ‘b’, …, ‘g’} A = {‘a’, ‘c’, ‘e’, ‘g’} = 1010101 • Hash tables needed implementations. for larger – Set of integers = (232 values) / 8 = 536, 870, 912 bytes 40

Enumerations 41

Enumerations 41

Enumerations • enumeration – set of named elements – Values are usually ordered, can

Enumerations • enumeration – set of named elements – Values are usually ordered, can compare enum weekday {sun, mon, tue, wed, thu, fri, sat} if (my. Var. Today > mon) {. . . } • Advantages – More readable code – Compiler can catch some errors • Is sun==0 and mon==1? – C/C++: yes; Pascal: no • Can also choose ordering in C enum weekday {mon=0, tue=1, wed=2…} 42

Lists 43

Lists 43

Lists • list – the empty list or a pair consisting of an object

Lists • list – the empty list or a pair consisting of an object (list or atom) and another list (a. (b. (c. (d. nil)))) • improper list – list whose final pair contains two elements, as opposed to the empty list (a. (b. (c. d))) • basic operations: cons, car, cdr, append • list comprehensions (e. g. Miranda and Haskell) [i * i | i <- [1. . 100]; i mod 2 = 1] 44

Recursive Types 45

Recursive Types 45

Recursive Types • recursive type - type whose objects may contain references to other

Recursive Types • recursive type - type whose objects may contain references to other objects of the same type – Most are records (consisting of reference objects and other “data” objects) – Used for linked data structures: lists, trees struct Node { Node *left, *right; int data; } 46

Recursive Types • In reference model of variables (e. g. Lisp, Java), recursive type

Recursive Types • In reference model of variables (e. g. Lisp, Java), recursive type needs no special support. Every variable is a reference anyway. • In value model of variables (e. g. Pascal, C), need pointers. 47

Value vs. Reference • Functional languages – almost always reference model • Imperative languages

Value vs. Reference • Functional languages – almost always reference model • Imperative languages – value model (e. g. C) – reference model (e. g. Smalltalk) • implementation approach: use actual values for immutable objects – combination (e. g. Java) 48

Pointers • pointer – a variable whose value is a reference to some object

Pointers • pointer – a variable whose value is a reference to some object – pointer use may be restricted or not • only allow pointers to point to heap (e. g. Pascal) • allow “address of” operator (e. g. ampersand in C) – pointers not equivalent to addresses! – how reclaim heap space? • explicit (programmer’s duty) • garbage collection (language implementation’s duty) 49

Value Model – More on C • Pointers and single dimensional arrays interchangeable, though

Value Model – More on C • Pointers and single dimensional arrays interchangeable, though space allocation at declaration different int a[10]; int *b; • For subroutines, pointer to array is passed, not full array • Pointer arithmetic – <Pointer, Integer> addition int a[10]; int n; n = *(a+3); – <Pointer, Pointer> subtraction and comparison int int a[10]; * x = a + 4; * y = a + 7; closer_to_front = x < y; 50

Dangling References • dangling reference – a live pointer that no longer points to

Dangling References • dangling reference – a live pointer that no longer points to a valid object – to heap object: in explicit reclamation, programmer reclaims an object to which a pointer still refers – to stack object: subroutine returns while some pointer in wider scope still refers to local object of subroutine • How do we prevent them? 51

Dangling References • Prevent pointer from pointing to objects with shorter lifetimes (e. g.

Dangling References • Prevent pointer from pointing to objects with shorter lifetimes (e. g. Algol 68, Ada 95). Difficult to enforce • Tombstones • Locks and Keys 52

Tombstones • Idea – Introduce another level of indirection: pointer contain the address of

Tombstones • Idea – Introduce another level of indirection: pointer contain the address of the tombstone; tombstone contains address of object – When object is reclaimed, mark tombstone (zeroed) • Time overheads – Create tombstone – Check validity of access – Double indirection • Space overheads – when to reclaim? ? • Extra benefits – easy to compact heap – works for heap and stack 53

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Locks and Keys • Idea – Every pointer is <address, key> tuple – Every

Locks and Keys • Idea – Every pointer is <address, key> tuple – Every object starts with same lock as pointer’s key – When object is reclaimed, object’s lock marked (zeroed) • Advantages – No need to keep tombstones around • Disadvantages – Objects need special key field (usually implemented only for heap objects) – Probabilistic protection • Time overheads – Lock to key comparison costly 55

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Garbage Collection • Language implementation notices when objects are no longer useful and reclaims

Garbage Collection • Language implementation notices when objects are no longer useful and reclaims them automatically – essential for functional languages – trend for imperative languages • When is object no longer useful? – Reference counts – Mark and sweep – “Conservative” collection 57

Reference Counts • Idea – Counter in each object that tracks number of pointers

Reference Counts • Idea – Counter in each object that tracks number of pointers that refer to object – Recursively decrement counts for objects and reclaim those objects with count of zero • Must identify every pointer – in every object (instance of type) – in every stack frame (instance of method) – use compiler-generated type descriptors 58

Type descriptors example my. Func type descriptor at 0 x 104 public class My.

Type descriptors example my. Func type descriptor at 0 x 104 public class My. Program { public void my. Func () { Car c; } } public class Car { char a, b, c; Engine e; Wheel w; } public class Engine { char x, y; Valve v; } i offset address 0 0 (Car) 0 x 018 Car type descriptor at 0 x 018 i offset address 0 4 (Engine) 0 x 0 A 2 1 5 (Wheel) 0 x 005 Engine type descriptor at 0 x 0 A 2 i offset address 0 3 (Valve) 0 x. B 05 public class Wheel {. . . } public class Valve {. . . } Wheel type descriptor at 0 x 005 Valve type descriptor at 0 x. B 05 59

Mark-and-Sweep • Idea … when space low 1. Mark every block “useless” 2. Beginning

Mark-and-Sweep • Idea … when space low 1. Mark every block “useless” 2. Beginning with pointers outside the heap, recursively explore all linked data structures and mark each traversed as useful 3. Return still marked blocks to freelist • Must identify pointers – in every block – use type descriptors 60

Garbage Collection Comparison • Reference Count – Will never reclaim circular data structures –

Garbage Collection Comparison • Reference Count – Will never reclaim circular data structures – Must record counts • Mark-and-Sweep – Lower overhead during regular operation – Bursts of activity (when collection performed, usually when space is low) 61

Conservative collection • Idea – Number of blocks in heap is much smaller than

Conservative collection • Idea – Number of blocks in heap is much smaller than number of possible addresses (232) – a word that could be a pointer into heap is probably pointer into heap – Scan all word-aligned quantities outside the heap; if any looks like block address, mark block useful and recursively explore words in block • Advantages – No need for type descriptors – Usually safe, though could “hide” pointers • Disadvantages – Some garbage is unclaimed – Can not compact (not sure what is pointer and what isn’t) 62