source: doc/generic_types/generic_types.tex @ 79b8dc3

aaron-thesisarm-ehcleanup-dtorsdeferred_resndemanglerenumforall-pointer-decayjacob/cs343-translationjenkins-sandboxnew-astnew-ast-unique-exprnew-envno_listpersistent-indexerpthread-emulationresolv-newwith_gc
Last change on this file since 79b8dc3 was 79b8dc3, checked in by Aaron Moss <a3moss@…>, 5 years ago

Some compaction of benchmark code

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4
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47\makeatother
48
49% Useful macros
50\newcommand{\CFA}{C$\mathbf\forall$\xspace} % Cforall symbolic name
51\newcommand{\CC}{\rm C\kern-.1em\hbox{+\kern-.25em+}\xspace} % C++ symbolic name
52\newcommand{\CCeleven}{\rm C\kern-.1em\hbox{+\kern-.25em+}11\xspace} % C++11 symbolic name
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57\newcommand{\CS}{C\raisebox{-0.7ex}{\Large$^\sharp$}\xspace}
58\newcommand{\Textbf}[1]{{\color{red}\textbf{#1}}}
59
60% CFA programming language, based on ANSI C (with some gcc additions)
61\lstdefinelanguage{CFA}[ANSI]{C}{
62        morekeywords={_Alignas,_Alignof,__alignof,__alignof__,asm,__asm,__asm__,_At,_Atomic,__attribute,__attribute__,auto,
63                _Bool,catch,catchResume,choose,_Complex,__complex,__complex__,__const,__const__,disable,dtype,enable,__extension__,
64                fallthrough,fallthru,finally,forall,ftype,_Generic,_Imaginary,inline,__label__,lvalue,_Noreturn,one_t,otype,restrict,_Static_assert,
65                _Thread_local,throw,throwResume,trait,try,ttype,typeof,__typeof,__typeof__,zero_t},
66}%
67
68\lstset{
69language=CFA,
70columns=fullflexible,
71basicstyle=\linespread{0.9}\sf,                                                 % reduce line spacing and use sanserif font
72stringstyle=\tt,                                                                                % use typewriter font
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74xleftmargin=\parindentlnth,                                                             % indent code to paragraph indentation
75%mathescape=true,                                                                               % LaTeX math escape in CFA code $...$
76escapechar=\$,                                                                                  % LaTeX escape in CFA code
77keepspaces=true,                                                                                %
78showstringspaces=false,                                                                 % do not show spaces with cup
79showlines=true,                                                                                 % show blank lines at end of code
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85        {<-}{$\leftarrow$}2 {=>}{$\Rightarrow$}2,
86moredelim=**[is][\color{red}]{`}{`},
87}% lstset
88
89% inline code @...@
90\lstMakeShortInline@%
91
92% ACM Information
93\citestyle{acmauthoryear}
94
95\acmJournal{PACMPL}
96
97\title{Generic and Tuple Types with Efficient Dynamic Layout in \CFA}
98
99\author{Aaron Moss}
100\email{a3moss@uwaterloo.ca}
101\author{Robert Schluntz}
102\email{rschlunt@uwaterloo.ca}
103\author{Peter Buhr}
104\email{pabuhr@uwaterloo.ca}
105\affiliation{%
106        \institution{University of Waterloo}
107        \department{David R. Cheriton School of Computer Science}
108        \streetaddress{Davis Centre, University of Waterloo}
109        \city{Waterloo}
110        \state{ON}
111        \postcode{N2L 3G1}
112        \country{Canada}
113}
114
115\terms{generic, tuple, variadic, types}
116\keywords{generic types, tuple types, variadic types, polymorphic functions, C, Cforall}
117
118\begin{CCSXML}
119<ccs2012>
120<concept>
121<concept_id>10011007.10011006.10011008.10011024.10011025</concept_id>
122<concept_desc>Software and its engineering~Polymorphism</concept_desc>
123<concept_significance>500</concept_significance>
124</concept>
125<concept>
126<concept_id>10011007.10011006.10011008.10011024.10011028</concept_id>
127<concept_desc>Software and its engineering~Data types and structures</concept_desc>
128<concept_significance>500</concept_significance>
129</concept>
130<concept>
131<concept_id>10011007.10011006.10011041.10011047</concept_id>
132<concept_desc>Software and its engineering~Source code generation</concept_desc>
133<concept_significance>300</concept_significance>
134</concept>
135</ccs2012>
136\end{CCSXML}
137
138\ccsdesc[500]{Software and its engineering~Polymorphism}
139\ccsdesc[500]{Software and its engineering~Data types and structures}
140\ccsdesc[300]{Software and its engineering~Source code generation}
141
142\begin{abstract}
143The C programming language is a foundational technology for modern computing with millions of lines of code implementing everything from commercial operating-systems to hobby projects.
144This installation base and the programmers producing it represent a massive software-engineering investment spanning decades and likely to continue for decades more.
145Nonetheless, C, first standardized over thirty years ago, lacks many features that make programming in more modern languages safer and more productive.
146The goal of the \CFA project is to create an extension of C that provides modern safety and productivity features while still ensuring strong backwards compatibility with C and its programmers.
147Prior projects have attempted similar goals but failed to honour C programming-style; for instance, adding object-oriented or functional programming with garbage collection is a non-starter for many C developers.
148Specifically, \CFA is designed to have an orthogonal feature-set based closely on the C programming paradigm, so that \CFA features can be added \emph{incrementally} to existing C code-bases, and C programmers can learn \CFA extensions on an as-needed basis, preserving investment in existing code and engineers.
149This paper describes two \CFA extensions, generic and tuple types, details how their design avoids shortcomings of similar features in C and other C-like languages, and presents experimental results validating the design.
150\end{abstract}
151
152\begin{document}
153\maketitle
154
155
156\section{Introduction and Background}
157
158The C programming language is a foundational technology for modern computing with millions of lines of code implementing everything from commercial operating-systems to hobby projects.
159This installation base and the programmers producing it represent a massive software-engineering investment spanning decades and likely to continue for decades more.
160The \citet{TIOBE} ranks the top 5 most popular programming languages as: Java 16\%, \Textbf{C 7\%}, \Textbf{\CC 5\%}, \CS 4\%, Python 4\% = 36\%, where the next 50 languages are less than 3\% each with a long tail.
161The top 3 rankings over the past 30 years are:
162\lstDeleteShortInline@%
163\begin{center}
164\setlength{\tabcolsep}{10pt}
165\begin{tabular}{@{}r|c|c|c|c|c|c|c@{}}
166                & 2017  & 2012  & 2007  & 2002  & 1997  & 1992  & 1987          \\
167\hline
168Java    & 1             & 1             & 1             & 1             & 12    & -             & -                     \\
169\hline
170\Textbf{C}      & \Textbf{2}& \Textbf{2}& \Textbf{2}& \Textbf{2}& \Textbf{1}& \Textbf{1}& \Textbf{1}    \\
171\hline
172\CC             & 3             & 3             & 3             & 3             & 2             & 2             & 4                     \\
173\end{tabular}
174\end{center}
175\lstMakeShortInline@%
176Love it or hate it, C is extremely popular, highly used, and one of the few system's languages.
177In many cases, \CC is often used solely as a better C.
178Nonetheless, C, first standardized over thirty years ago, lacks many features that make programming in more modern languages safer and more productive.
179
180\CFA (pronounced ``C-for-all'', and written \CFA or Cforall) is an evolutionary extension of the C programming language that aims to add modern language features to C while maintaining both source compatibility with C and a familiar programming model for programmers.
181The four key design goals for \CFA~\citep{Bilson03} are:
182(1) The behaviour of standard C code must remain the same when translated by a \CFA compiler as when translated by a C compiler;
183(2) Standard C code must be as fast and as small when translated by a \CFA compiler as when translated by a C compiler;
184(3) \CFA code must be at least as portable as standard C code;
185(4) Extensions introduced by \CFA must be translated in the most efficient way possible.
186These goals ensure existing C code-bases can be converted to \CFA incrementally with minimal effort, and C programmers can productively generate \CFA code without training beyond the features being used.
187Unfortunately, \CC is actively diverging from C, so incremental additions require significant effort and training, coupled with multiple legacy design-choices that cannot be updated.
188
189\CFA is currently implemented as a source-to-source translator from \CFA to the GCC-dialect of C~\citep{GCCExtensions}, allowing it to leverage the portability and code optimizations provided by GCC, meeting goals (1)-(3).
190Ultimately, a compiler is necessary for advanced features and optimal performance.
191
192This paper identifies shortcomings in existing approaches to generic and variadic data types in C-like languages and presents a design for generic and variadic types avoiding those shortcomings.
193Specifically, the solution is both reusable and type-checked, as well as conforming to the design goals of \CFA with ergonomic use of existing C abstractions.
194The new constructs are empirically compared with both standard C and \CC; the results show the new design is comparable in performance.
195
196\subsection{Polymorphic Functions}
197\label{sec:poly-fns}
198
199\CFA's polymorphism was originally formalized by \citet{Ditchfield92}, and first implemented by \citet{Bilson03}.
200The signature feature of \CFA is parametric-polymorphic functions where functions are generalized using a @forall@ clause (giving the language its name):
201\begin{lstlisting}
202`forall( otype T )` T identity( T val ) { return val; }
203int forty_two = identity( 42 );                         $\C{// T is bound to int, forty\_two == 42}$
204\end{lstlisting}
205The @identity@ function above can be applied to any complete \emph{object type} (or @otype@).
206The type variable @T@ is transformed into a set of additional implicit parameters encoding sufficient information about @T@ to create and return a variable of that type.
207The \CFA implementation passes the size and alignment of the type represented by an @otype@ parameter, as well as an assignment operator, constructor, copy constructor and destructor.
208If this extra information is not needed, \eg for a pointer, the type parameter can be declared as a \emph{data type} (or @dtype@).
209
210In \CFA, the polymorphism runtime-cost is spread over each polymorphic call, due to passing more arguments to polymorphic functions; preliminary experiments show this overhead is similar to \CC virtual-function calls.
211An advantage of this design is that, unlike \CC template-functions, \CFA polymorphic-functions are compatible with C \emph{separate compilation}, preventing compilation and code bloat.
212
213Since bare polymorphic-types provide only a narrow set of available operations, \CFA provides a \emph{type assertion} mechanism to provide further type information, where type assertions may be variable or function declarations that depend on a polymorphic type-variable.
214For example, the function @twice@ can be defined using the \CFA syntax for operator overloading:
215\begin{lstlisting}
216forall( otype T `| { T ?+?(T, T); }` ) T twice( T x ) { return x + x; } $\C{// ? denotes operands}$
217int val = twice( twice( 3.7 ) );
218\end{lstlisting}
219which works for any type @T@ with a matching addition operator.
220The polymorphism is achieved by creating a wrapper function for calling @+@ with @T@ bound to @double@, then passing this function to the first call of @twice@.
221There is now the option of using the same @twice@ and converting the result to @int@ on assignment, or creating another @twice@ with type parameter @T@ bound to @int@ because \CFA uses the return type, as in~\cite{Ada}, in its type analysis.
222The first approach has a late conversion from @double@ to @int@ on the final assignment, while the second has an eager conversion to @int@.
223\CFA minimizes the number of conversions and their potential to lose information, so it selects the first approach, which corresponds with C-programmer intuition.
224
225Crucial to the design of a new programming language are the libraries to access thousands of external software features.
226Like \CC, \CFA inherits a massive compatible library-base, where other programming languages must rewrite or provide fragile inter-language communication with C.
227A simple example is leveraging the existing type-unsafe (@void *@) C @bsearch@ to binary search a sorted floating-point array:
228\begin{lstlisting}
229void * bsearch( const void * key, const void * base, size_t nmemb, size_t size,
230                                int (* compar)( const void *, const void * ));
231int comp( const void * t1, const void * t2 ) { return *(double *)t1 < *(double *)t2 ? -1 :
232                                *(double *)t2 < *(double *)t1 ? 1 : 0; }
233double vals[10] = { /* 10 floating-point values */ };
234double key = 5.0;
235double * val = (double *)bsearch( &key, vals, 10, sizeof(vals[0]), comp );      $\C{// search sorted array}$
236\end{lstlisting}
237which can be augmented simply with a generalized, type-safe, \CFA-overloaded wrappers:
238\begin{lstlisting}
239forall( otype T | { int ?<?( T, T ); } ) T * bsearch( T key, const T * arr, size_t size ) {
240        int comp( const void * t1, const void * t2 ) { /* as above with double changed to T */ }
241        return (T *)bsearch( &key, arr, size, sizeof(T), comp ); }
242forall( otype T | { int ?<?( T, T ); } ) unsigned int bsearch( T key, const T * arr, size_t size ) {
243        T *result = bsearch( key, arr, size );  $\C{// call first version}$
244        return result ? result - arr : size; }  $\C{// pointer subtraction includes sizeof(T)}$
245double * val = bsearch( 5.0, vals, 10 );        $\C{// selection based on return type}$
246int posn = bsearch( 5.0, vals, 10 );
247\end{lstlisting}
248The nested function @comp@ provides the hidden interface from typed \CFA to untyped (@void *@) C, plus the cast of the result.
249Providing a hidden @comp@ function in \CC is awkward as lambdas do not use C calling-conventions and template declarations cannot appear at block scope.
250As well, an alternate kind of return is made available: position versus pointer to found element.
251\CC's type-system cannot disambiguate between the two versions of @bsearch@ because it does not use the return type in overload resolution, nor can \CC separately compile a templated @bsearch@.
252
253\CFA has replacement libraries condensing hundreds of existing C functions into tens of \CFA overloaded functions, all without rewriting the actual computations.
254For example, it is possible to write a type-safe \CFA wrapper @malloc@ based on the C @malloc@:
255\begin{lstlisting}
256forall( dtype T | sized(T) ) T * malloc( void ) { return (T *)(void *)malloc( (size_t)sizeof(T) ); }
257int * ip = malloc();                                            $\C{// select type and size from left-hand side}$
258double * dp = malloc();
259struct S {...} * sp = malloc();
260\end{lstlisting}
261where the return type supplies the type/size of the allocation, which is impossible in most type systems.
262
263Call-site inferencing and nested functions provide a localized form of inheritance.
264For example, the \CFA @qsort@ only sorts in ascending order using @<@.
265However, it is trivial to locally change this behaviour:
266\begin{lstlisting}
267forall( otype T | { int ?<?( T, T ); } ) void qsort( const T * arr, size_t size ) { /* use C qsort */ }
268{       int ?<?( double x, double y ) { return x `>` y; }       $\C{// locally override behaviour}$
269        qsort( vals, size );                                    $\C{// descending sort}$
270}
271\end{lstlisting}
272Within the block, the nested version of @<@ performs @>@ and this local version overrides the built-in @<@ so it is passed to @qsort@.
273Hence, programmers can easily form local environments, adding and modifying appropriate functions, to maximize reuse of other existing functions and types.
274
275Finally, \CFA allows variable overloading:
276\lstDeleteShortInline@%
277\par\smallskip
278\begin{tabular}{@{}l@{\hspace{\parindent}}|@{\hspace{\parindent}}l@{}}
279\begin{lstlisting}
280short int MAX = ...;
281int MAX = ...;
282double MAX = ...;
283\end{lstlisting}
284&
285\begin{lstlisting}
286short int s = MAX;  // select correct MAX
287int i = MAX;
288double d = MAX;
289\end{lstlisting}
290\end{tabular}
291\smallskip\par\noindent
292\lstMakeShortInline@%
293Hence, the single name @MAX@ replaces all the C type-specific names: @SHRT_MAX@, @INT_MAX@, @DBL_MAX@.
294As well, restricted constant overloading is allowed for the values @0@ and @1@, which have special status in C, \eg the value @0@ is both an integer and a pointer literal, so its meaning depends on context.
295In addition, several operations are defined in terms values @0@ and @1@, \eg:
296\begin{lstlisting}
297int x;
298if (x) x++                                                                      $\C{// if (x != 0) x += 1;}$
299\end{lstlisting}
300Every if statement in C compares the condition with @0@, and every increment and decrement operator is semantically equivalent to adding or subtracting the value @1@ and storing the result.
301Due to these rewrite rules, the values @0@ and @1@ have the types @zero_t@ and @one_t@ in \CFA, which allows overloading various operations for new types that seamlessly connect to all special @0@ and @1@ contexts.
302The types @zero_t@ and @one_t@ have special built in implicit conversions to the various integral types, and a conversion to pointer types for @0@, which allows standard C code involving @0@ and @1@ to work as normal.
303
304
305\subsection{Traits}
306
307\CFA provides \emph{traits} to name a group of type assertions, where the trait name allows specifying the same set of assertions in multiple locations, preventing repetition mistakes at each function declaration:
308\begin{lstlisting}
309trait summable( otype T ) {
310        void ?{}( T *, zero_t );                                $\C{// constructor from 0 literal}$
311        T ?+?( T, T );                                                  $\C{// assortment of additions}$
312        T ?+=?( T *, T );
313        T ++?( T * );
314        T ?++( T * ); };
315forall( otype T `| summable( T )` ) T sum( T a[$\,$], size_t size ) {  // use trait
316        `T` total = { `0` };                                    $\C{// instantiate T from 0 by calling its constructor}$
317        for ( unsigned int i = 0; i < size; i += 1 ) total `+=` a[i]; $\C{// select appropriate +}$
318        return total; }
319\end{lstlisting}
320
321In fact, the set of trait operators is incomplete, as there is no assignment requirement for type @T@, but @otype@ is syntactic sugar for the following implicit trait:
322\begin{lstlisting}
323trait otype( dtype T | sized(T) ) {  // sized is a pseudo-trait for types with known size and alignment
324        void ?{}( T * );                                                $\C{// default constructor}$
325        void ?{}( T *, T );                                             $\C{// copy constructor}$
326        void ?=?( T *, T );                                             $\C{// assignment operator}$
327        void ^?{}( T * ); };                                    $\C{// destructor}$
328\end{lstlisting}
329Given the information provided for an @otype@, variables of polymorphic type can be treated as if they were a complete type: stack-allocatable, default or copy-initialized, assigned, and deleted.
330
331In summation, the \CFA type-system uses \emph{nominal typing} for concrete types, matching with the C type-system, and \emph{structural typing} for polymorphic types.
332Hence, trait names play no part in type equivalence;
333the names are simply macros for a list of polymorphic assertions, which are expanded at usage sites.
334Nevertheless, trait names form a logical subtype-hierarchy with @dtype@ at the top, where traits often contain overlapping assertions, \eg operator @+@.
335Traits are used like interfaces in Java or abstract base-classes in \CC, but without the nominal inheritance-relationships.
336Instead, each polymorphic function (or generic type) defines the structural type needed for its execution (polymorphic type-key), and this key is fulfilled at each call site from the lexical environment, which is similar to Go~\citep{Go} interfaces.
337Hence, new lexical scopes and nested functions are used extensively to create local subtypes, as in the @qsort@ example, without having to manage a nominal-inheritance hierarchy.
338(Nominal inheritance can be approximated with traits using marker variables or functions, as is done in Go.)
339
340% Nominal inheritance can be simulated with traits using marker variables or functions:
341% \begin{lstlisting}
342% trait nominal(otype T) {
343%     T is_nominal;
344% };
345% int is_nominal;                                                               $\C{// int now satisfies the nominal trait}$
346% \end{lstlisting}
347%
348% Traits, however, are significantly more powerful than nominal-inheritance interfaces; most notably, traits may be used to declare a relationship \emph{among} multiple types, a property that may be difficult or impossible to represent in nominal-inheritance type systems:
349% \begin{lstlisting}
350% trait pointer_like(otype Ptr, otype El) {
351%     lvalue El *?(Ptr);                                                $\C{// Ptr can be dereferenced into a modifiable value of type El}$
352% }
353% struct list {
354%     int value;
355%     list *next;                                                               $\C{// may omit "struct" on type names as in \CC}$
356% };
357% typedef list *list_iterator;
358%
359% lvalue int *?( list_iterator it ) { return it->value; }
360% \end{lstlisting}
361% In the example above, @(list_iterator, int)@ satisfies @pointer_like@ by the user-defined dereference function, and @(list_iterator, list)@ also satisfies @pointer_like@ by the built-in dereference operator for pointers. Given a declaration @list_iterator it@, @*it@ can be either an @int@ or a @list@, with the meaning disambiguated by context (\eg @int x = *it;@ interprets @*it@ as an @int@, while @(*it).value = 42;@ interprets @*it@ as a @list@).
362% While a nominal-inheritance system with associated types could model one of those two relationships by making @El@ an associated type of @Ptr@ in the @pointer_like@ implementation, few such systems could model both relationships simultaneously.
363
364
365\section{Generic Types}
366
367One of the known shortcomings of standard C is that it does not provide reusable type-safe abstractions for generic data structures and algorithms.
368Broadly speaking, there are three approaches to create data structures in C.
369One approach is to write bespoke data structures for each context in which they are needed.
370While this approach is flexible and supports integration with the C type-checker and tooling, it is also tedious and error-prone, especially for more complex data structures.
371A second approach is to use @void *@--based polymorphism, \eg the C standard-library functions @bsearch@ and @qsort@, and does allow the use of common code for common functionality.
372However, basing all polymorphism on @void *@ eliminates the type-checker's ability to ensure that argument types are properly matched, often requiring a number of extra function parameters, pointer indirection, and dynamic allocation that would not otherwise be needed.
373A third approach to generic code is to use preprocessor macros, which does allow the generated code to be both generic and type-checked, but errors may be difficult to interpret.
374Furthermore, writing and using preprocessor macros can be unnatural and inflexible.
375
376Other languages use \emph{generic types}, \eg \CC and Java, to produce type-safe abstract data-types.
377\CFA also implements generic types that integrate efficiently and naturally with the existing polymorphic functions, while retaining backwards compatibility with C and providing separate compilation.
378However, for known concrete parameters, the generic type can be inlined, like \CC templates.
379
380A generic type can be declared by placing a @forall@ specifier on a @struct@ or @union@ declaration, and instantiated using a parenthesized list of types after the type name:
381\begin{lstlisting}
382forall( otype R, otype S ) struct pair {
383        R first;
384        S second;
385};
386forall( otype T ) T value( pair( const char *, T ) p ) { return p.second; }
387forall( dtype F, otype T ) T value_p( pair( F *, T * ) p ) { return *p.second; }
388pair( const char *, int ) p = { "magic", 42 };
389int magic = value( p );
390pair( void *, int * ) q = { 0, &p.second };
391magic = value_p( q );
392double d = 1.0;
393pair( double *, double * ) r = { &d, &d };
394d = value_p( r );
395\end{lstlisting}
396
397\CFA classifies generic types as either \emph{concrete} or \emph{dynamic}.
398Concrete have a fixed memory layout regardless of type parameters, while dynamic vary in memory layout depending on their type parameters.
399A type may have polymorphic parameters but still be concrete, called \emph{dtype-static}.
400Polymorphic pointers are an example of dtype-static types, \eg @forall(dtype T) T *@ is a polymorphic type, but for any @T@, @T *@  is a fixed-sized pointer, and therefore, can be represented by a @void *@ in code generation.
401
402\CFA generic types also allow checked argument-constraints.
403For example, the following declaration of a sorted set-type ensures the set key supports equality and relational comparison:
404\begin{lstlisting}
405forall( otype Key | { _Bool ?==?(Key, Key); _Bool ?<?(Key, Key); } ) struct sorted_set;
406\end{lstlisting}
407
408
409\subsection{Concrete Generic-Types}
410
411The \CFA translator template-expands concrete generic-types into new structure types, affording maximal inlining.
412To enable inter-operation among equivalent instantiations of a generic type, the translator saves the set of instantiations currently in scope and reuses the generated structure declarations where appropriate.
413For example, a function declaration that accepts or returns a concrete generic-type produces a declaration for the instantiated struct in the same scope, which all callers may reuse.
414For example, the concrete instantiation for @pair( const char *, int )@ is:
415\begin{lstlisting}
416struct _pair_conc1 {
417        const char * first;
418        int second;
419};
420\end{lstlisting}
421
422A concrete generic-type with dtype-static parameters is also expanded to a structure type, but this type is used for all matching instantiations.
423In the above example, the @pair( F *, T * )@ parameter to @value_p@ is such a type; its expansion is below and it is used as the type of the variables @q@ and @r@ as well, with casts for member access where appropriate:
424\begin{lstlisting}
425struct _pair_conc0 {
426        void * first;
427        void * second;
428};
429\end{lstlisting}
430
431
432\subsection{Dynamic Generic-Types}
433
434Though \CFA implements concrete generic-types efficiently, it also has a fully general system for dynamic generic types.
435As mentioned in Section~\ref{sec:poly-fns}, @otype@ function parameters (in fact all @sized@ polymorphic parameters) come with implicit size and alignment parameters provided by the caller.
436Dynamic generic-types also have an \emph{offset array} containing structure member-offsets.
437A dynamic generic-union needs no such offset array, as all members are at offset 0 but size and alignment are still necessary.
438Access to members of a dynamic structure is provided at runtime via base-displacement addressing with the structure pointer and the member offset (similar to the @offsetof@ macro), moving a compile-time offset calculation to runtime.
439
440The offset arrays are statically generated where possible.
441If a dynamic generic-type is declared to be passed or returned by value from a polymorphic function, the translator can safely assume the generic type is complete (\ie has a known layout) at any call-site, and the offset array is passed from the caller;
442if the generic type is concrete at the call site, the elements of this offset array can even be statically generated using the C @offsetof@ macro.
443As an example, @p.second@ in the @value@ function above is implemented as @*(p + _offsetof_pair[1])@, where @p@ is a @void *@, and @_offsetof_pair@ is the offset array passed into @value@ for @pair( const char *, T )@.
444The offset array @_offsetof_pair@ is generated at the call site as @size_t _offsetof_pair[] = { offsetof(_pair_conc1, first), offsetof(_pair_conc1, second) }@.
445
446In some cases the offset arrays cannot be statically generated.
447For instance, modularity is generally provided in C by including an opaque forward-declaration of a structure and associated accessor and mutator functions in a header file, with the actual implementations in a separately-compiled @.c@ file.
448\CFA supports this pattern for generic types, but the caller does not know the actual layout or size of the dynamic generic-type, and only holds it by a pointer.
449The \CFA translator automatically generates \emph{layout functions} for cases where the size, alignment, and offset array of a generic struct cannot be passed into a function from that function's caller.
450These layout functions take as arguments pointers to size and alignment variables and a caller-allocated array of member offsets, as well as the size and alignment of all @sized@ parameters to the generic structure (un@sized@ parameters are forbidden from being used in a context that affects layout).
451Results of these layout functions are cached so that they are only computed once per type per function. %, as in the example below for @pair@.
452Layout functions also allow generic types to be used in a function definition without reflecting them in the function signature.
453For instance, a function that strips duplicate values from an unsorted @vector(T)@ would likely have a pointer to the vector as its only explicit parameter, but use some sort of @set(T)@ internally to test for duplicate values.
454This function could acquire the layout for @set(T)@ by calling its layout function with the layout of @T@ implicitly passed into the function.
455
456Whether a type is concrete, dtype-static, or dynamic is decided solely on the type parameters and @forall@ clause on a declaration.
457This design allows opaque forward declarations of generic types, \eg @forall(otype T) struct Box@ -- like in C, all uses of @Box(T)@ can be separately compiled, and callers from other translation units know the proper calling conventions to use.
458If the definition of a structure type is included in deciding whether a generic type is dynamic or concrete, some further types may be recognized as dtype-static (\eg @forall(otype T) struct unique_ptr { T* p }@ does not depend on @T@ for its layout, but the existence of an @otype@ parameter means that it \emph{could}.), but preserving separate compilation (and the associated C compatibility) in the existing design is judged to be an appropriate trade-off.
459
460
461\subsection{Applications}
462\label{sec:generic-apps}
463
464The reuse of dtype-static structure instantiations enables useful programming patterns at zero runtime cost.
465The most important such pattern is using @forall(dtype T) T *@ as a type-checked replacement for @void *@, \eg creating a lexicographic comparison for pairs of pointers used by @bsearch@ or @qsort@:
466\begin{lstlisting}
467forall(dtype T) int lexcmp( pair( T *, T * ) * a, pair( T *, T * ) * b, int (* cmp)( T *, T * ) ) {
468        return cmp( a->first, b->first ) ? : cmp( a->second, b->second );
469}
470\end{lstlisting}
471%       int c = cmp( a->first, b->first );
472%       if ( c == 0 ) c = cmp( a->second, b->second );
473%       return c;
474Since @pair(T *, T * )@ is a concrete type, there are no implicit parameters passed to @lexcmp@, so the generated code is identical to a function written in standard C using @void *@, yet the \CFA version is type-checked to ensure the fields of both pairs and the arguments to the comparison function match in type.
475
476Another useful pattern enabled by reused dtype-static type instantiations is zero-cost \emph{tag-structures}.
477Sometimes information is only used for type-checking and can be omitted at runtime, \eg:
478\begin{lstlisting}
479forall(dtype Unit) struct scalar { unsigned long value; };
480struct metres {};
481struct litres {};
482
483forall(dtype U) scalar(U) ?+?( scalar(U) a, scalar(U) b ) {
484        return (scalar(U)){ a.value + b.value };
485}
486scalar(metres) half_marathon = { 21093 };
487scalar(litres) swimming_pool = { 2500000 };
488scalar(metres) marathon = half_marathon + half_marathon;
489scalar(litres) two_pools = swimming_pool + swimming_pool;
490marathon + swimming_pool;                                       $\C{// compilation ERROR}$
491\end{lstlisting}
492@scalar@ is a dtype-static type, so all uses have a single structure definition, containing @unsigned long@, and can share the same implementations of common functions like @?+?@.
493These implementations may even be separately compiled, unlike \CC template functions.
494However, the \CFA type-checker ensures matching types are used by all calls to @?+?@, preventing nonsensical computations like adding a length to a volume.
495
496
497\section{Tuples}
498\label{sec:tuples}
499
500In many languages, functions can return at most one value;
501however, many operations have multiple outcomes, some exceptional.
502Consider C's @div@ and @remquo@ functions, which return the quotient and remainder for a division of integer and floating-point values, respectively.
503\begin{lstlisting}
504typedef struct { int quo, rem; } div_t;
505div_t div( int num, int den );
506double remquo( double num, double den, int * quo );
507div_t qr = div( 13, 5 );                                        $\C{// return quotient/remainder aggregate}$
508int q;
509double r = remquo( 13.5, 5.2, &q );                     $\C{// return remainder, alias quotient}$
510\end{lstlisting}
511@div@ aggregates the quotient/remainder in a structure, while @remquo@ aliases a parameter to an argument.
512Both approaches are awkward.
513Alternatively, a programming language can directly support returning multiple values, \eg in \CFA:
514\begin{lstlisting}
515[ int, int ] div( int num, int den );           $\C{// return two integers}$
516[ double, double ] div( double num, double den ); $\C{// return two doubles}$
517int q, r;                                                                       $\C{// overload variable names}$
518double q, r;
519[ q, r ] = div( 13, 5 );                                        $\C{// select appropriate div and q, r}$
520[ q, r ] = div( 13.5, 5.2 );
521\end{lstlisting}
522Clearly, this approach is straightforward to understand and use;
523therefore, why do few programming languages support this obvious feature or provide it awkwardly?
524The answer is that there are complex consequences that cascade through multiple aspects of the language, especially the type-system.
525This section show these consequences and how \CFA deals with them.
526
527
528\subsection{Tuple Expressions}
529
530The addition of multiple-return-value functions (MRVF) are useless without a syntax for accepting multiple values at the call-site.
531The simplest mechanism for capturing the return values is variable assignment, allowing the values to be retrieved directly.
532As such, \CFA allows assigning multiple values from a function into multiple variables, using a square-bracketed list of lvalue expressions (as above), called a \emph{tuple}.
533
534However, functions also use \emph{composition} (nested calls), with the direct consequence that MRVFs must also support composition to be orthogonal with single-returning-value functions (SRVF), \eg:
535\begin{lstlisting}
536printf( "%d %d\n", div( 13, 5 ) );                      $\C{// return values seperated into arguments}$
537\end{lstlisting}
538Here, the values returned by @div@ are composed with the call to @printf@.
539However, the \CFA type-system must support significantly more complex composition:
540\begin{lstlisting}
541[ int, int ] foo$\(_1\)$( int );
542[ double ] foo$\(_2\)$( int );
543void bar( int, double, double );
544bar( foo( 3 ), foo( 3 ) );
545\end{lstlisting}
546The type-resolver only has the tuple return-types to resolve the call to @bar@ as the @foo@ parameters are identical, which involves unifying the possible @foo@ functions with @bar@'s parameter list.
547No combination of @foo@s are an exact match with @bar@'s parameters, so the resolver applies C conversions.
548The minimal cost is @bar( foo@$_1$@( 3 ), foo@$_2$@( 3 ) )@, giving (@int@, {\color{green}@int@}, @double@) to (@int@, {\color{green}@double@}, @double@) with one {\color{green}safe} (widening) conversion from @int@ to @double@ versus ({\color{red}@double@}, {\color{green}@int@}, {\color{green}@int@}) to ({\color{red}@int@}, {\color{green}@double@}, {\color{green}@double@}) with one {\color{red}unsafe} (narrowing) conversion from @double@ to @int@ and two safe conversions.
549
550
551\subsection{Tuple Variables}
552
553An important observation from function composition is that new variable names are not required to initialize parameters from an MRVF.
554\CFA also allows declaration of tuple variables that can be initialized from an MRVF, since it can be awkward to declare multiple variables of different types.
555As a consequence, \CFA allows declaration of \emph{tuple variables} that can be initialized from an MRVF, \eg:
556\begin{lstlisting}
557[ int, int ] qr = div( 13, 5 );                         $\C{// tuple-variable declaration and initialization}$
558[ double, double ] qr = div( 13.5, 5.2 );
559\end{lstlisting}
560where the tuple variable-name serves the same purpose as the parameter name(s).
561Tuple variables can be composed of any types, except for array types, since array sizes are generally unknown.
562
563One way to access the tuple-variable components is with assignment or composition:
564\begin{lstlisting}
565[ q, r ] = qr;                                                          $\C{// access tuple-variable components}$
566printf( "%d %d\n", qr );
567\end{lstlisting}
568\CFA also supports \emph{tuple indexing} to access single components of a tuple expression:
569\begin{lstlisting}
570[int, int] * p = &qr;                                           $\C{// tuple pointer}$
571int rem = qr.1;                                                         $\C{// access remainder}$
572int quo = div( 13, 5 ).0;                                       $\C{// access quotient}$
573p->0 = 5;                                                                       $\C{// change quotient}$
574bar( qr.1, qr );                                                        $\C{// pass remainder and quotient/remainder}$
575rem = [42, div( 13, 5 )].0.1;                           $\C{// access 2nd component of 1st component of tuple expression}$
576\end{lstlisting}
577
578
579\subsection{Flattening and Restructuring}
580
581In function call contexts, tuples support implicit flattening and restructuring conversions.
582Tuple flattening recursively expands a tuple into the list of its basic components.
583Tuple structuring packages a list of expressions into a value of tuple type, \eg:
584\lstDeleteShortInline@%
585\par\smallskip
586\begin{tabular}{@{}l@{\hspace{\parindent}}|@{\hspace{\parindent}}l@{}}
587\begin{lstlisting}
588int f( int, int );
589int g( [int, int] );
590int h( int, [int, int] );
591[int, int] x;
592\end{lstlisting}
593&
594\begin{lstlisting}
595int y;
596f( x );                 $\C[1in]{// flatten}$
597g( y, 10 );             $\C{// structure}$
598h( x, y );              $\C{// flatten and structure}\CRT{}$
599\end{lstlisting}
600\end{tabular}
601\smallskip\par\noindent
602\lstMakeShortInline@%
603In the call to @f@, @x@ is implicitly flattened so the components of @x@ are passed as the two arguments.
604In the call to @g@, the values @y@ and @10@ are structured into a single argument of type @[int, int]@ to match the parameter type of @g@.
605Finally, in the call to @h@, @x@ is flattened to yield an argument list of length 3, of which the first component of @x@ is passed as the first parameter of @h@, and the second component of @x@ and @y@ are structured into the second argument of type @[int, int]@.
606The flexible structure of tuples permits a simple and expressive function call syntax to work seamlessly with both SRVF and MRVF, and with any number of arguments of arbitrarily complex structure.
607
608
609\subsection{Tuple Assignment}
610
611An assignment where the left side is a tuple type is called \emph{tuple assignment}.
612There are two kinds of tuple assignment depending on whether the right side of the assignment operator has a tuple type or a non-tuple type, called \emph{multiple} and \emph{mass assignment}, respectively.
613\lstDeleteShortInline@%
614\par\smallskip
615\begin{tabular}{@{}l@{\hspace{\parindent}}|@{\hspace{\parindent}}l@{}}
616\begin{lstlisting}
617int x = 10;
618double y = 3.5;
619[int, double] z;
620
621\end{lstlisting}
622&
623\begin{lstlisting}
624z = [x, y];             $\C[1in]{// multiple assignment}$
625[x, y] = z;             $\C{// multiple assignment}$
626z = 10;                 $\C{// mass assignment}$
627[y, x] = 3.14$\C{// mass assignment}\CRT{}$
628\end{lstlisting}
629\end{tabular}
630\smallskip\par\noindent
631\lstMakeShortInline@%
632Both kinds of tuple assignment have parallel semantics, so that each value on the left and right side is evaluated before any assignments occur.
633As a result, it is possible to swap the values in two variables without explicitly creating any temporary variables or calling a function, \eg, @[x, y] = [y, x]@.
634This semantics means mass assignment differs from C cascading assignment (\eg @a = b = c@) in that conversions are applied in each individual assignment, which prevents data loss from the chain of conversions that can happen during a cascading assignment.
635For example, @[y, x] = 3.14@ performs the assignments @y = 3.14@ and @x = 3.14@, yielding @y == 3.14@ and @x == 3@;
636whereas C cascading assignment @y = x = 3.14@ performs the assignments @x = 3.14@ and @y = x@, yielding @3@ in @y@ and @x@.
637Finally, tuple assignment is an expression where the result type is the type of the left-hand side of the assignment, just like all other assignment expressions in C.
638This example shows mass, multiple, and cascading assignment used in one expression:
639\begin{lstlisting}
640void f( [int, int] );
641f( [x, y] = z = 1.5 );                                          $\C{// assignments in parameter list}$
642\end{lstlisting}
643
644
645\subsection{Member Access}
646
647It is also possible to access multiple fields from a single expression using a \emph{member-access}.
648The result is a single tuple-valued expression whose type is the tuple of the types of the members, \eg:
649\begin{lstlisting}
650struct S { int x; double y; char * z; } s;
651s.[x, y, z] = 0;
652\end{lstlisting}
653Here, the mass assignment sets all members of @s@ to zero.
654Since tuple-index expressions are a form of member-access expression, it is possible to use tuple-index expressions in conjunction with member tuple expressions to manually restructure a tuple (\eg rearrange, drop, and duplicate components).
655\lstDeleteShortInline@%
656\par\smallskip
657\begin{tabular}{@{}l@{\hspace{\parindent}}|@{\hspace{\parindent}}l@{}}
658\begin{lstlisting}
659[int, int, long, double] x;
660void f( double, long );
661
662\end{lstlisting}
663&
664\begin{lstlisting}
665x.[0, 1] = x.[1, 0];    $\C[1in]{// rearrange: [x.0, x.1] = [x.1, x.0]}$
666f( x.[0, 3] );            $\C{// drop: f(x.0, x.3)}\CRT{}$
667[int, int, int] y = x.[2, 0, 2]; // duplicate: [y.0, y.1, y.2] = [x.2, x.0.
668x.2]
669\end{lstlisting}
670\end{tabular}
671\smallskip\par\noindent
672\lstMakeShortInline@%
673It is also possible for a member access to contain other member accesses, \eg:
674\begin{lstlisting}
675struct A { double i; int j; };
676struct B { int * k; short l; };
677struct C { int x; A y; B z; } v;
678v.[x, y.[i, j], z.k];                                           $\C{// [v.x, [v.y.i, v.y.j], v.z.k]}$
679\end{lstlisting}
680
681
682\begin{comment}
683\subsection{Casting}
684
685In C, the cast operator is used to explicitly convert between types.
686In \CFA, the cast operator has a secondary use as type ascription.
687That is, a cast can be used to select the type of an expression when it is ambiguous, as in the call to an overloaded function:
688\begin{lstlisting}
689int f();     // (1)
690double f()// (2)
691
692f();       // ambiguous - (1),(2) both equally viable
693(int)f()// choose (2)
694\end{lstlisting}
695
696Since casting is a fundamental operation in \CFA, casts should be given a meaningful interpretation in the context of tuples.
697Taking a look at standard C provides some guidance with respect to the way casts should work with tuples:
698\begin{lstlisting}
699int f();
700void g();
701
702(void)f()// (1)
703(int)g()// (2)
704\end{lstlisting}
705In C, (1) is a valid cast, which calls @f@ and discards its result.
706On the other hand, (2) is invalid, because @g@ does not produce a result, so requesting an @int@ to materialize from nothing is nonsensical.
707Generalizing these principles, any cast wherein the number of components increases as a result of the cast is invalid, while casts that have the same or fewer number of components may be valid.
708
709Formally, a cast to tuple type is valid when $T_n \leq S_m$, where $T_n$ is the number of components in the target type and $S_m$ is the number of components in the source type, and for each $i$ in $[0, n)$, $S_i$ can be cast to $T_i$.
710Excess elements ($S_j$ for all $j$ in $[n, m)$) are evaluated, but their values are discarded so that they are not included in the result expression.
711This approach follows naturally from the way that a cast to @void@ works in C.
712
713For example, in
714\begin{lstlisting}
715[int, int, int] f();
716[int, [int, int], int] g();
717
718([int, double])f();           $\C{// (1)}$
719([int, int, int])g();         $\C{// (2)}$
720([void, [int, int]])g();      $\C{// (3)}$
721([int, int, int, int])g();    $\C{// (4)}$
722([int, [int, int, int]])g()$\C{// (5)}$
723\end{lstlisting}
724
725(1) discards the last element of the return value and converts the second element to @double@.
726Since @int@ is effectively a 1-element tuple, (2) discards the second component of the second element of the return value of @g@.
727If @g@ is free of side effects, this expression is equivalent to @[(int)(g().0), (int)(g().1.0), (int)(g().2)]@.
728Since @void@ is effectively a 0-element tuple, (3) discards the first and third return values, which is effectively equivalent to @[(int)(g().1.0), (int)(g().1.1)]@).
729
730Note that a cast is not a function call in \CFA, so flattening and structuring conversions do not occur for cast expressions\footnote{User-defined conversions have been considered, but for compatibility with C and the existing use of casts as type ascription, any future design for such conversions would require more precise matching of types than allowed for function arguments and parameters.}.
731As such, (4) is invalid because the cast target type contains 4 components, while the source type contains only 3.
732Similarly, (5) is invalid because the cast @([int, int, int])(g().1)@ is invalid.
733That is, it is invalid to cast @[int, int]@ to @[int, int, int]@.
734\end{comment}
735
736
737\subsection{Polymorphism}
738
739Tuples also integrate with \CFA polymorphism as a kind of generic type.
740Due to the implicit flattening and structuring conversions involved in argument passing, @otype@ and @dtype@ parameters are restricted to matching only with non-tuple types, \eg:
741\begin{lstlisting}
742forall(otype T, dtype U) void f( T x, U * y );
743f( [5, "hello"] );
744\end{lstlisting}
745where @[5, "hello"]@ is flattened, giving argument list @5, "hello"@, and @T@ binds to @int@ and @U@ binds to @const char@.
746Tuples, however, may contain polymorphic components.
747For example, a plus operator can be written to add two triples together.
748\begin{lstlisting}
749forall(otype T | { T ?+?( T, T ); }) [T, T, T] ?+?( [T, T, T] x, [T, T, T] y ) {
750        return [x.0 + y.0, x.1 + y.1, x.2 + y.2];
751}
752[int, int, int] x;
753int i1, i2, i3;
754[i1, i2, i3] = x + ([10, 20, 30]);
755\end{lstlisting}
756
757Flattening and restructuring conversions are also applied to tuple types in polymorphic type assertions.
758\begin{lstlisting}
759int f( [int, double], double );
760forall(otype T, otype U | { T f( T, U, U ); })
761void g( T, U );
762g( 5, 10.21 );
763\end{lstlisting}
764Hence, function parameter and return lists are flattened for the purposes of type unification allowing the example to pass expression resolution.
765This relaxation is possible by extending the thunk scheme described by \citet{Bilson03}.
766Whenever a candidate's parameter structure does not exactly match the formal parameter's structure, a thunk is generated to specialize calls to the actual function:
767\begin{lstlisting}
768int _thunk( int _p0, double _p1, double _p2 ) {
769        return f( [_p0, _p1], _p2 );
770}
771\end{lstlisting}
772so the thunk provides flattening and structuring conversions to inferred functions, improving the compatibility of tuples and polymorphism.
773These thunks take advantage of GCC C nested-functions to produce closures that have the usual function pointer signature.
774
775
776\subsection{Variadic Tuples}
777\label{sec:variadic-tuples}
778
779To define variadic functions, \CFA adds a new kind of type parameter, @ttype@ (tuple type).
780Matching against a @ttype@ parameter consumes all remaining argument components and packages them into a tuple, binding to the resulting tuple of types.
781In a given parameter list, there must be at most one @ttype@ parameter that occurs last, which matches normal variadic semantics, with a strong feeling of similarity to \CCeleven variadic templates.
782As such, @ttype@ variables are also called \emph{argument packs}.
783
784Like variadic templates, the main way to manipulate @ttype@ polymorphic functions is via recursion.
785Since nothing is known about a parameter pack by default, assertion parameters are key to doing anything meaningful.
786Unlike variadic templates, @ttype@ polymorphic functions can be separately compiled.
787For example, a generalized @sum@ function written using @ttype@:
788\begin{lstlisting}
789int sum$\(_0\)$() { return 0; }
790forall(ttype Params | { int sum( Params ); } ) int sum$\(_1\)$( int x, Params rest ) {
791        return x + sum( rest );
792}
793sum( 10, 20, 30 );
794\end{lstlisting}
795Since @sum@\(_0\) does not accept any arguments, it is not a valid candidate function for the call @sum(10, 20, 30)@.
796In order to call @sum@\(_1\), @10@ is matched with @x@, and the argument resolution moves on to the argument pack @rest@, which consumes the remainder of the argument list and @Params@ is bound to @[20, 30]@.
797The process continues, @Params@ is bound to @[]@, requiring an assertion @int sum()@, which matches @sum@\(_0\) and terminates the recursion.
798Effectively, this algorithm traces as @sum(10, 20, 30)@ $\rightarrow$ @10 + sum(20, 30)@ $\rightarrow$ @10 + (20 + sum(30))@ $\rightarrow$ @10 + (20 + (30 + sum()))@ $\rightarrow$ @10 + (20 + (30 + 0))@.
799
800It is reasonable to take the @sum@ function a step further to enforce a minimum number of arguments:
801\begin{lstlisting}
802int sum( int x, int y ) { return x + y; }
803forall(ttype Params | { int sum( int, Params ); } ) int sum( int x, int y, Params rest ) {
804        return sum( x + y, rest );
805}
806\end{lstlisting}
807One more step permits the summation of any summable type with all arguments of the same type:
808\begin{lstlisting}
809trait summable(otype T) {
810        T ?+?( T, T );
811};
812forall(otype R | summable( R ) ) R sum( R x, R y ) {
813        return x + y;
814}
815forall(otype R, ttype Params | summable(R) | { R sum(R, Params); } ) R sum(R x, R y, Params rest) {
816        return sum( x + y, rest );
817}
818\end{lstlisting}
819Unlike C variadic functions, it is unnecessary to hard code the number and expected types.
820Furthermore, this code is extendable so any user-defined type with a @?+?@ operator.
821Summing arbitrary heterogeneous lists is possible with similar code by adding the appropriate type variables and addition operators.
822
823It is also possible to write a type-safe variadic print function to replace @printf@:
824\begin{lstlisting}
825struct S { int x, y; };
826forall(otype T, ttype Params | { void print(T); void print(Params); }) void print(T arg, Params rest) {
827        print(arg);
828        print(rest);
829}
830void print( char * x ) { printf( "%s", x ); }
831void print( int x ) { printf( "%d", x ); }
832void print( S s ) { print( "{ ", s.x, ",", s.y, " }" ); }
833print( "s = ", (S){ 1, 2 }, "\n" );
834\end{lstlisting}
835This example showcases a variadic-template-like decomposition of the provided argument list.
836The individual @print@ functions allow printing a single element of a type.
837The polymorphic @print@ allows printing any list of types, as long as each individual type has a @print@ function.
838The individual print functions can be used to build up more complicated @print@ functions, such as for @S@, which is something that cannot be done with @printf@ in C.
839
840Finally, it is possible to use @ttype@ polymorphism to provide arbitrary argument forwarding functions.
841For example, it is possible to write @new@ as a library function:
842\begin{lstlisting}
843struct pair( otype R, otype S );
844forall( otype R, otype S ) void ?{}( pair(R, S) *, R, S );  // (1)
845forall( dtype T, ttype Params | sized(T) | { void ?{}( T *, Params ); } ) T * new( Params p ) {
846        return ((T*)malloc( sizeof(T) )){ p }; // construct into result of malloc
847}
848pair( int, char ) * x = new( 42, '!' );
849\end{lstlisting}
850The @new@ function provides the combination of type-safe @malloc@ with a \CFA constructor call, making it impossible to forget constructing dynamically allocated objects.
851This function provides the type-safety of @new@ in \CC, without the need to specify the allocated type again, thanks to return-type inference.
852
853
854\subsection{Implementation}
855
856Tuples are implemented in the \CFA translator via a transformation into generic types.
857For each $N$, the first time an $N$-tuple is seen in a scope a generic type with $N$ type parameters is generated, \eg:
858\begin{lstlisting}
859[int, int] f() {
860        [double, double] x;
861        [int, double, int] y;
862}
863\end{lstlisting}
864is transformed into:
865\begin{lstlisting}
866// generated before the first 2-tuple
867forall(dtype T0, dtype T1 | sized(T0) | sized(T1)) struct _tuple2 {
868        T0 field_0;
869        T1 field_1;
870};
871_tuple2(int, int) f() {
872        _tuple2(double, double) x;
873        // generated before the first 3-tuple
874        forall(dtype T0, dtype T1, dtype T2 | sized(T0) | sized(T1) | sized(T2)) struct _tuple3 {
875                T0 field_0;
876                T1 field_1;
877                T2 field_2;
878        };
879        _tuple3(int, double, int) y;
880}
881\end{lstlisting}
882Tuple expressions are then simply converted directly into compound literals:
883\begin{lstlisting}
884[5, 'x', 1.24];
885\end{lstlisting}
886becomes:
887\begin{lstlisting}
888(_tuple3(int, char, double)){ 5, 'x', 1.24 };
889\end{lstlisting}
890
891\begin{comment}
892Since tuples are essentially structures, tuple indexing expressions are just field accesses:
893\begin{lstlisting}
894void f(int, [double, char]);
895[int, double] x;
896
897x.0+x.1;
898printf("%d %g\n", x);
899f(x, 'z');
900\end{lstlisting}
901Is transformed into:
902\begin{lstlisting}
903void f(int, _tuple2(double, char));
904_tuple2(int, double) x;
905
906x.field_0+x.field_1;
907printf("%d %g\n", x.field_0, x.field_1);
908f(x.field_0, (_tuple2){ x.field_1, 'z' });
909\end{lstlisting}
910Note that due to flattening, @x@ used in the argument position is converted into the list of its fields.
911In the call to @f@, the second and third argument components are structured into a tuple argument.
912Similarly, tuple member expressions are recursively expanded into a list of member access expressions.
913
914Expressions that may contain side effects are made into \emph{unique expressions} before being expanded by the flattening conversion.
915Each unique expression is assigned an identifier and is guaranteed to be executed exactly once:
916\begin{lstlisting}
917void g(int, double);
918[int, double] h();
919g(h());
920\end{lstlisting}
921Internally, this expression is converted to two variables and an expression:
922\begin{lstlisting}
923void g(int, double);
924[int, double] h();
925
926_Bool _unq0_finished_ = 0;
927[int, double] _unq0;
928g(
929        (_unq0_finished_ ? _unq0 : (_unq0 = f(), _unq0_finished_ = 1, _unq0)).0,
930        (_unq0_finished_ ? _unq0 : (_unq0 = f(), _unq0_finished_ = 1, _unq0)).1,
931);
932\end{lstlisting}
933Since argument evaluation order is not specified by the C programming language, this scheme is built to work regardless of evaluation order.
934The first time a unique expression is executed, the actual expression is evaluated and the accompanying boolean is set to true.
935Every subsequent evaluation of the unique expression then results in an access to the stored result of the actual expression.
936Tuple member expressions also take advantage of unique expressions in the case of possible impurity.
937
938Currently, the \CFA translator has a very broad, imprecise definition of impurity, where any function call is assumed to be impure.
939This notion could be made more precise for certain intrinsic, auto-generated, and builtin functions, and could analyze function bodies when they are available to recursively detect impurity, to eliminate some unique expressions.
940
941The various kinds of tuple assignment, constructors, and destructors generate GNU C statement expressions.
942A variable is generated to store the value produced by a statement expression, since its fields may need to be constructed with a non-trivial constructor and it may need to be referred to multiple time, \eg in a unique expression.
943The use of statement expressions allows the translator to arbitrarily generate additional temporary variables as needed, but binds the implementation to a non-standard extension of the C language.
944However, there are other places where the \CFA translator makes use of GNU C extensions, such as its use of nested functions, so this restriction is not new.
945\end{comment}
946
947
948\section{Evaluation}
949
950Though \CFA provides significant added functionality over C, these added features have a low runtime penalty.
951In fact, \CFA's features for generic programming can enable faster runtime execution than idiomatic @void *@-based C code.
952This claim is demonstrated through a set of generic-code-based micro-benchmarks in C, \CFA, and \CC (see source code in Appendix~\ref{sec:BenchMarks}).
953Since all these languages share a subset comprising most of standard C, maximal-performance benchmarks would show little runtime variance, other than in length and clarity of source code.
954Instead, the presented benchmarks show the costs of idiomatic use of each language's features to examine common usage.
955Figure~\ref{fig:MicroBenchmark} shows the \CFA benchmark tests for a generic stack based on a singly linked-list, a generic pair-data-structure, and a variadic @print@ routine similar to that in Section~\ref{sec:variadic-tuples}.
956The tests are similar for C and \CC.
957The first two experiments use element types @int@ and @pair( _Bool, char)@, and push $N=40M$ elements on a generic stack, copy the stack, clear one of the stacks, and find the maximum value in the other stack.
958The last experiment creates a file and prints $N=40M$ elements of type @int@ and @pair( _Bool, char)@ using a variadic print.
959
960The structure of each implemented is: C with @void *@-based polymorphism, \CFA with the different presented features, \CC with templates, and \CC using only class inheritance for polymorphism, called \CCV.
961The \CCV variant illustrates an alternative object-oriented idiom where all objects inherit from a base @object@ class, mimicking a Java-like interface;
962hence runtime checks are necessary to safely down-cast objects.
963The most notable difference among the implementations is in optimizations: \CFA and \CC inline the stack and pair elements into corresponding list and pair nodes, while the C and \CCV lack generic-type capability {\color{red}(AWKWARD) to store generic objects via pointers to separately-allocated objects}.
964For the print benchmark, idiomatic printing is used: the C and \CFA variants used @cstdio.h@, while the \CC and \CCV variants used @iostream@.
965Preliminary tests show the difference has little runtime effect.
966Finally, the C @rand@ function is used generate random numbers.
967
968\begin{figure}
969\begin{lstlisting}[xleftmargin=3\parindentlnth,aboveskip=0pt,belowskip=0pt,numbers=left,numberstyle=\tt\small,numberblanklines=false]
970int main( int argc, char *argv[] ) {
971        int max = 0;
972        stack(int) s, t;
973        REPEAT_TIMED( "push_int", push( &s, 42 ); )
974        TIMED( "copy_int", t = s; )
975        TIMED( "clear_int", clear( &s ); )
976        REPEAT_TIMED( "pop_int", max = max( max, pop( &t ) ); )
977
978        stack(pair(_Bool, char)) s1, t1;
979        pair(_Bool, char) max = { (_Bool)0, '\0' };
980        REPEAT_TIMED( "push_pair", push( &s1, (pair(_Bool, char)){ (_Bool)0, 'a' } ); )
981        TIMED( "copy_pair", t1 = s1; )
982        TIMED( "clear_pair", clear( &s1 ); )
983        REPEAT_TIMED( "pop_pair", max = max( max, pop( &t1 ) ); )
984
985        FILE * out = fopen( "cfa-out.txt", "w" );
986        REPEAT_TIMED( "print_int", print( out, 42, ":", 42, "\n" ); )
987        REPEAT_TIMED( "print_pair", print( out, (pair(_Bool, char)){ (_Bool)0, 'a' }, ":",
988                                                         (pair(_Bool, char)){ (_Bool)0, 'a' }, "\n" ); )
989        fclose(out);
990}
991\end{lstlisting}
992\caption{Micro-Benchmark}
993\label{fig:MicroBenchmark}
994\end{figure}
995
996\begin{figure}
997\centering
998\input{timing}
999\caption{Benchmark Timing Results (smaller is better)}
1000\label{fig:eval}
1001\end{figure}
1002
1003\begin{table}
1004\caption{Properties of benchmark code}
1005\label{tab:eval}
1006\newcommand{\CT}[1]{\multicolumn{1}{c}{#1}}
1007\begin{tabular}{r|rrrr}
1008                                                        & \CT{C}        & \CT{\CFA}     & \CT{\CC}      &       \CT{\CCV}       \\ \hline
1009maximum memory usage (MB)       & 10001         & 2501          & 2503          &       11253           \\
1010source code size (lines)        & 301           & 224           & 188           &       437                     \\
1011redundant type annotations (lines)      & 46            & 3                     & 2                     &       15                      \\
1012binary size (KB)                        & 18            & 234           & 18            &       42                      \\
1013\end{tabular}
1014\end{table}
1015
1016Figure~\ref{fig:eval} and Table~\ref{tab:eval} show the benchmark results.
1017Each data point is the time for $N = 40M$ function calls or loop iterations, as appropriate.
1018The five functions are $N$ stack pushes of randomly generated elements, deep copy of an $N$ element stack, clearing all nodes of an $N$ element stack, $N$ stack pops (keeping a running record of the maximum element to ensure that the object copies are not optimized out), and $N/2$ variadic @print@ calls each containing two constant strings and two stack elements.
1019These five functions are run first for a stack of integers, and second for a stack of generic pairs of a boolean and a @char@.
1020\TODO{} The data shown is the median of 5 consecutive runs of each program, with an initial warm-up run omitted.
1021All code was compiled at \texttt{-O2} by GCC or G++ 6.2.0, with all \CC code compiled as \CCfourteen.
1022The benchmarks were run on an Ubuntu 16.04 workstation with 16 GB of RAM and a 6-core AMD FX-6300 CPU with 3.5 GHz maximum clock frequency.
1023The C and \CCV variants are generally the slowest and most memory-hungry, due to their less-efficient memory layout and the pointer-indirection necessary to implement generic types in these languages; this problem is exacerbated by the second level of generic types in the pair-based benchmarks.
1024By contrast, the \CFA and \CC variants run in roughly equivalent time for both the integer and pair of boolean and char tests, which makes sense given that an integer is actually larger than the pair in both languages.
1025
1026\CC performs best because it uses header-only inlined libraries (i.e., no separate compilation).
1027\CFA and \CC have the advantage of a pre-written generic @pair@ type to reduce line count, while C and \CCV require it to written by the programmer, as C does not have a generic collections library in its standard distribution and \CCV does not use the \CC standard template library by construction.
1028The definition of @object@ and wrapper classes for @bool@, @char@, @int@, and @const char *@ are included in the line count for \CCV, which somewhat inflates its line count, as an actual object-oriented language would include these in the standard library; with their omission the \CCV line count is similar to C.
1029We justify the given line count by noting that many object-oriented languages do not allow implementing new interfaces on library types without subclassing or boilerplate-filled wrapper types, which may be similarly verbose.
1030
1031Raw line-count, however, is a fairly rough measure of code complexity;
1032another important factor is how much type information the programmer must manually specify, especially where that information is not checked by the compiler.
1033Such un-checked type information produces a heavier documentation burden and increased potential for runtime bugs, and is much less common in \CFA than C, with its manually specified function pointers arguments and format codes, or \CCV, with its extensive use of un-type-checked downcasts (\eg @object@ to @integer@ when popping a stack, or @object@ to @printable@ when printing the elements of a @pair@). To quantify this, the ``redundant type annotations'' line in Table~\ref{tab:eval} counts the number of lines on which the type of a known variable is re-specified, either as a format specifier, explicit downcast, type-specific function, or by name in a @sizeof@, struct literal, or @new@ expression. The \CC benchmark uses two redundant type annotations to create a new stack nodes, while the C and \CCV benchmarks have several such annotations spread throughout their code. The three instances in which the \CFA benchmark still uses redundant type specifiers are to cast the result of a polymorphic @malloc@ call (the @sizeof@ argument is inferred by the compiler). These uses are similar to the @new@ expressions in \CC, though ongoing work on the \CFA compiler's type resolver should shortly render even these type casts superfluous.
1034
1035\section{Related Work}
1036
1037
1038\subsection{Polymorphism}
1039
1040\CC is closest language to \CFA;
1041both are extensions to C with source and runtime backwards compatibility, and incremental extensions to C.
1042The fundamental difference is in their engineering approach to C compatibility and programmer expectation.
1043While \CC provides good backwards compatibility with C, it has a steep learning curve for many of its extensions.
1044For example, polymorphism is provided via three disjoint mechanisms: overloading, inheritance, and templates.
1045The overloading is restricted because resolution does not using the return type, inheritance requires learning object-oriented programming and coping with a restricted nominal-inheritance hierarchy, templates cannot be separately compiled resulting in compilation/code bloat and poor error messages, and determining how these mechanisms interact and which to use is confusing.
1046In contrast, \CFA has a single facility for polymorphic code supporting type-safe separate-compilation of polymorphic functions and generic (opaque) types, which uniformly leveraging the C procedural paradigm.
1047The key mechanism to support separate compilation is \CFA's \emph{explicit} use of assumed properties for a type.
1048Until \CC concepts~\citep{C++Concepts} are standardized (anticipated for \CCtwenty), \CC provides no way to specify the requirements of a generic function in code beyond compilation errors during template expansion;
1049furthermore, \CC concepts are restricted to template polymorphism.
1050
1051Cyclone~\citep{Grossman06} also provides capabilities for polymorphic functions and existential types, similar to \CFA's @forall@ functions and generic types.
1052Cyclone existential types can include function pointers in a construct similar to a virtual function-table, but these pointers must be explicitly initialized at some point in the code, a tedious and potentially error-prone process.
1053Furthermore, Cyclone's polymorphic functions and types are restricted to abstraction over types with the same layout and calling convention as @void *@, \ie only pointer types and @int@.
1054In \CFA terms, all Cyclone polymorphism must be dtype-static.
1055While the Cyclone design provides the efficiency benefits discussed in Section~\ref{sec:generic-apps} for dtype-static polymorphism, it is more restrictive than \CFA's general model.
1056
1057Objective-C~\citep{obj-c-book} is an industrially successful extensions to C.
1058However, Objective-C is a radical departure from C, using an object-oriented model with message-passing.
1059Objective-C did not support type-checked generics until recently~\citep{xcode7}, historically using less-efficient and more error-prone runtime checking of object types.
1060The GObject framework~\citep{GObject} also adds object-oriented programming with runtime type-checking and reference-counting garbage-collection to C;
1061these features are more intrusive additions than those provided by \CFA, in addition to the runtime overhead of reference-counting.
1062Vala~\citep{Vala} compiles to GObject-based C, and so adds the burden of learning a separate language syntax to the aforementioned demerits of GObject as a modernization path for the existing C code-bases.
1063Java~\citep{Java8} included generic types in Java~5;
1064Java's generic types are type-checked at compilation and type-erased at runtime, similar to \CFA's.
1065However, in Java, each object carries its own table of method pointers, while \CFA passes the method pointers separately to maintain a C-compatible layout.
1066Java is also a garbage-collected, object-oriented language, with the associated resource usage and C-interoperability burdens.
1067
1068D~\citep{D}, Go~\citep{Go}, and Rust~\citep{Rust} are modern, compiled languages with abstraction features similar to \CFA traits, \emph{interfaces} in D and Go and \emph{traits} in Rust.
1069However, each language represents significant departures from C in terms of language model, and none has the same level of compatibility with C as \CFA.
1070D and Go are garbage-collected languages, imposing the associated runtime overhead.
1071The necessity of accounting for data transfer between managed runtimes and the unmanaged C runtime complicates foreign-function interfaces to C.
1072Furthermore, while generic types and functions are available in Go, they are limited to a small fixed set provided by the compiler, with no language facility to define more.
1073D restricts garbage collection to its own heap by default, while Rust is not garbage-collected, and thus has a lighter-weight runtime more interoperable with C.
1074Rust also possesses much more powerful abstraction capabilities for writing generic code than Go.
1075On the other hand, Rust's borrow-checker, provides strong safety guarantees but is complex and difficult to learn, and imposes a distinctly idiomatic programming style.
1076\CFA, with its more modest safety features, ports directly to C code, while maintaining the idiomatic style of the original source.
1077
1078
1079\subsection{Tuples/Variadics}
1080
1081Many programming languages have some form of tuple construct and/or variadic functions, \eg SETL, C, KW-C, \CC, D, Go, Java, ML, and Scala.
1082SETL~\cite{SETL} is a high-level mathematical programming language, with tuples being one of the primary data types.
1083Tuples in SETL allow subscripting, dynamic expansion, and multiple assignment.
1084C provides variadic functions through @va_list@ objects, but the programmer is responsible for managing the number of arguments and their types, so the mechanism is not type-safe.
1085KW-C~\cite{Buhr94a}, a predecessor of \CFA, introduced tuples to C as an extension of the C syntax, taking much of its inspiration from SETL.
1086The main contributions of that work were adding MRVF, tuple mass and multiple assignment, and record-field access.
1087\CCeleven introduced @std::tuple@ as a library variadic template structure.
1088Tuples are a generalization of @std::pair@, in that they allow for arbitrary length, fixed-size aggregation of heterogeneous values.
1089Operations include @std::get<N>@ to extract vales, @std::tie@ to create a tuple of references used for assignment, and lexicographic comparisons.
1090\CCseventeen proposes \emph{structured bindings}~\cite{Sutter15} to eliminate pre-declaring variables and use of @std::tie@ for binding the results.
1091This extension requires the use of @auto@ to infer the types of the new variables, so complicated expressions with a non-obvious type must be documented with some other mechanism.
1092Furthermore, structured bindings are not a full replacement for @std::tie@, as it always declares new variables.
1093Like \CC, D provides tuples through a library variadic-template structure.
1094Go does not have tuples but supports MRVF.
1095Java's variadic functions appear similar to C's but are type-safe using homogeneous arrays, which are less useful than \CFA's heterogeneously-typed variadic functions.
1096Tuples are a fundamental abstraction in most functional programming languages, such as Standard ML~\cite{sml} and Scala~\cite{Scala}, which decompose tuples using pattern matching.
1097
1098
1099\section{Conclusion \& Future Work}
1100
1101There is ongoing work on a wide range of \CFA feature extensions, including reference types, exceptions, and concurrent programming primitives.
1102In addition to this work, there are some interesting future directions the polymorphism design could take.
1103Notably, \CC template functions trade compile time and code bloat for optimal runtime of individual instantiations of polymorphic functions.
1104\CFA polymorphic functions, by contrast, use an approach that is essentially dynamic virtual dispatch.
1105The runtime overhead of this approach is low, but not as low as \CC template functions, and it may be beneficial to provide a mechanism for particularly performance-sensitive code to close this gap.
1106Further research is needed, but two promising approaches are to allow an annotation on polymorphic function call sites that tells the translator to create a template-specialization of the function (provided the code is visible in the current translation unit) or placing an annotation on polymorphic function definitions that instantiates a version of the polymorphic function specialized to some set of types.
1107These approaches are not mutually exclusive, and would allow these performance optimizations to be applied only where most useful to increase performance, without suffering the code bloat or loss of generality of a template expansion approach where it is unnecessary.
1108
1109In conclusion, the authors' design for generic types and tuples, unlike those available in existing work, is both reusable and type-checked, while still supporting a full range of C features, including separately-compiled modules.
1110We have experimentally validated the performance of our design against both \CC and standard C, showing it is comparable to both, and in some cases better.
1111
1112
1113\begin{acks}
1114The authors would like to thank Magnus Madsen for valuable editorial feedback.
1115
1116This work is supported in part by a corporate partnership with \grantsponsor{Huawei}{Huawei Ltd.}{http://www.huawei.com}\ and the first author's \grantsponsor{NSERC-PGS}{NSERC PGS D}{http://www.nserc-crsng.gc.ca/Students-Etudiants/PG-CS/BellandPostgrad-BelletSuperieures_eng.asp} scholarship.
1117\end{acks}
1118
1119
1120\bibliographystyle{ACM-Reference-Format}
1121\bibliography{cfa}
1122
1123
1124\appendix
1125
1126
1127\section{BenchMarks}
1128\label{sec:BenchMarks}
1129
1130TODO
1131
1132\end{document}
1133
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