source: doc/aaron_comp_II/comp_II.tex @ 752dc70

aaron-thesisarm-ehcleanup-dtorsctordeferred_resndemanglerjacob/cs343-translationjenkins-sandboxmemorynew-astnew-ast-unique-exprnew-envno_listpersistent-indexerresolv-newwith_gc
Last change on this file since 752dc70 was 752dc70, checked in by Aaron Moss <a3moss@…>, 5 years ago

Incorporate Peter's suggestions for Comp II section 2

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45\title{
46\Huge \vspace*{1in} Efficient Type Resolution in \CFA \\
47\huge \vspace*{0.25in} PhD Comprehensive II Research Proposal
48\vspace*{1in}
49}
50
51\author{
52\huge Aaron Moss \\
53\Large \vspace*{0.1in} \texttt{a3moss@uwaterloo.ca} \\
54\Large Cheriton School of Computer Science \\
55\Large University of Waterloo
56}
57
58\date{
59\today
60}
61
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64\begin{document}
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69\pagenumbering{roman}
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72\maketitle
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75\clearpage
76\thispagestyle{plain}
77\pdfbookmark[1]{Contents}{section}
78\tableofcontents
79
80\clearpage
81\thispagestyle{plain}
82\pagenumbering{arabic}
83
84\section{Introduction}
85
86\CFA\footnote{Pronounced ``C-for-all'', and written \CFA or \CFL.} is an evolutionary modernization of the C programming language currently being designed and built at the University of Waterloo by a team led by Peter Buhr.
87\CFA both fixes existing design problems and adds multiple new features to C, including name overloading, user-defined operators, parametric-polymorphic routines, and type constructors and destructors, among others.
88The new features make \CFA significantly more powerful and expressive than C, but impose a significant compile-time cost, particularly in the expression resolver, which must evaluate the typing rules of a much more complex type-system.
89
90The primary goal of this research project is to develop a sufficiently performant expression resolution algorithm, experimentally validate its performance, and integrate it into CFA, the \CFA reference compiler.
91Secondary goals of this project include the development of various new language features for \CFA: parametric-polymorphic (``generic'') types have already been designed and implemented, and reference types and user-defined conversions are under design consideration.
92An experimental performance-testing architecture for resolution algorithms is under development to determine the relative performance of different expression resolution algorithms, as well as the compile-time cost of adding various new features to the \CFA type-system.
93More broadly, this research should provide valuable data for implementers of compilers for other programming languages with similarly powerful static type-systems.
94
95\section{\CFA}
96
97To make the scope of the proposed expression resolution problem more explicit, it is necessary to define the features of both C and \CFA (both current and proposed) that affect this algorithm.
98In some cases the interactions of multiple features make expression resolution a significantly more complex problem than any individual feature would; in other cases a feature that does not by itself add any complexity to expression resolution triggers previously rare edge cases more frequently.
99
100It is important to note that \CFA is not an object-oriented language.
101\CFA does have a system of (possibly implicit) type conversions derived from C's type conversions; while these conversions may be thought of as something like an inheritance hierarchy the underlying semantics are significantly different and such an analogy is loose at best.
102Particularly, \CFA has no concept of ``subclass'', and thus no need to integrate an inheritance-based form of polymorphism with its parametric and overloading-based polymorphism.
103The graph structure of the \CFA type conversions is also markedly different than an inheritance graph; it has neither a top nor a bottom type, and does not satisfy the lattice properties typical of inheritance graphs.
104
105\subsection{Polymorphic Functions}
106The most significant feature \CFA adds is parametric-polymorphic functions.
107Such functions are written using a ©forall© clause (which gives the language its name):
108\begin{lstlisting}
109®forall(otype T)®
110T identity(T x) {
111    return x;
112}
113
114int forty_two = identity(42); // T is bound to int, forty_two == 42
115\end{lstlisting}
116The ©identity© function above can be applied to any complete object type (or ``©otype©'').
117The type variable ©T© is transformed into a set of additional implicit parameters to ©identity© which encode sufficient information about ©T© to create and return a variable of that type.
118The current \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.
119Here, the runtime cost of polymorphism is spread over each polymorphic call, due to passing more arguments to polymorphic functions; preliminary experiments have shown this overhead to be similar to \CC virtual function calls.
120Determining if packaging all polymorphic arguments to a function into a virtual function table would reduce the runtime overhead of polymorphic calls is an open research question.
121
122Since bare polymorphic types do not provide a great range of available operations, \CFA also provides a \emph{type assertion} mechanism to provide further information about a type:
123\begin{lstlisting}
124forall(otype T ®| { T twice(T); }®)
125T four_times(T x) {
126    return twice( twice(x) );
127}
128
129double twice(double d) { return d * 2.0; } // (1)
130
131double magic = four_times(10.5); // T is bound to double, uses (1) to satisfy type assertion
132\end{lstlisting}
133These type assertions may be either variable or function declarations that depend on a polymorphic type variable.
134©four_times© can only be called with an argument for which there exists a function named ©twice© that can take that argument and return another value of the same type; a pointer to the appropriate ©twice© function is passed as an additional implicit parameter to the call to ©four_times©.
135
136Monomorphic specializations of polymorphic functions can themselves be used to satisfy type assertions.
137For instance, ©twice© could have been defined using the \CFA syntax for operator overloading as:
138\begin{lstlisting}
139forall(otype S | { ®S ?+?(S, S);® })
140S twice(S x) { return x + x; }  // (2)
141\end{lstlisting} 
142This version of ©twice© works for any type ©S© that has an addition operator defined for it, and it could have been used to satisfy the type assertion on ©four_times©.
143The compiler accomplishes this by creating a wrapper function calling ©twice // (2)© with ©S© bound to ©double©, then providing this wrapper function to ©four_times©\footnote{©twice // (2)© could also have had a type parameter named ©T©; \CFA specifies renaming of the type parameters, which would avoid the name conflict with the type variable ©T© of ©four_times©.}.
144
145Finding appropriate functions to satisfy type assertions is essentially a recursive case of expression resolution, as it takes a name (that of the type assertion) and attempts to match it to a suitable declaration in the current scope.
146If a polymorphic function can be used to satisfy one of its own type assertions, this recursion may not terminate, as it is possible that function is examined as a candidate for its own type assertion unboundedly repeatedly.
147To avoid infinite loops, the current CFA compiler imposes a fixed limit on the possible depth of recursion, similar to that employed by most \CC compilers for template expansion; this restriction means that there are some semantically well-typed expressions that cannot be resolved by CFA.
148One area of potential improvement this project proposes to investigate is the possibility of using the compiler's knowledge of the current set of declarations to more precicely determine when further type assertion satisfaction recursion does not produce a well-typed expression.
149
150\subsubsection{Traits}
151\CFA provides \emph{traits} as a means to name a group of type assertions, as in the example below:
152\begin{lstlisting}
153®trait has_magnitude(otype T)® {
154    bool ?<?(T, T);        // comparison operator for T
155    T -?(T);               // negation operator for T
156    void ?{}(T*, zero_t);  // constructor from 0 literal
157};
158
159forall(otype M | has_magnitude(M))
160M abs( M m ) {
161    M zero = 0;  // uses zero_t constructor from trait
162    return m < zero ? -m : m;
163}
164
165forall(otype M | has_magnitude(M))
166M max_magnitude( M a, M b ) {
167    M aa = abs(a), ab = abs(b);
168    return aa < ab ? b : a;
169}
170\end{lstlisting}
171
172Semantically, traits are simply a named lists of type assertions, but they may be used for many of the same purposes that interfaces in Java or abstract base classes in \CC are used for.
173Unlike Java interfaces or \CC base classes, \CFA types do not explicitly state any inheritance relationship to traits they satisfy; this can be considered a form of structural inheritance, similar to interface implementation in Go, as opposed to the nominal inheritance model of Java and \CC.
174Nominal inheritance can be simulated with traits using marker variables or functions:
175\begin{lstlisting}
176trait nominal(otype T) {
177    ®T is_nominal;®
178};
179
180int is_nominal;  // int now satisfies the nominal trait
181{
182    char is_nominal; // char satisfies the nominal trait
183}
184// char no longer satisfies the nominal trait here 
185\end{lstlisting}
186
187Traits, however, are significantly more powerful than nominal-inheritance interfaces; firstly, due to the scoping rules of the declarations which satisfy a trait's type assertions, a type may not satisfy a trait everywhere that the type is declared, as with ©char© and the ©nominal© trait above.
188Secondly, traits may be used to declare a relationship between multiple types, a property which may be difficult or impossible to represent in nominal-inheritance type systems:
189\begin{lstlisting}
190trait pointer_like(®otype Ptr, otype El®) {
191    lvalue El *?(Ptr); // Ptr can be dereferenced into a modifiable value of type El
192}
193
194struct list {
195    int value;
196    list *next;  // may omit "struct" on type names
197};
198
199typedef list* list_iterator;
200
201lvalue int *?( list_iterator it ) {
202    return it->value;
203}
204\end{lstlisting}
205
206In the example above, ©(list_iterator, int)© satisfies ©pointer_like© by the given function, and ©(list_iterator, list)© also satisfies ©pointer_like© by the built-in pointer dereference operator.
207While 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.
208
209The flexibility of \CFA's implicit trait satisfaction mechanism provides user programmers with a great deal of power, but also blocks some optimization approaches for expression resolution.
210The ability of types to begin to or cease to satisfy traits when declarations go into or out of scope makes caching of trait satisfaction judgements difficult, and the ability of traits to take multiple type parameters could lead to a combinatorial explosion of work in any attempt to pre-compute trait satisfaction relationships.
211On the other hand, the addition of a nominal inheritance mechanism to \CFA's type system or replacement of \CFA's trait satisfaction system with a more object-oriented inheritance model and investigation of possible expression resolution optimizations for such a system may be an interesting avenue of further research.
212
213\subsection{Name Overloading}
214In C, no more than one variable or function in the same scope may share the same name\footnote{Technically, C has multiple separated namespaces, one holding ©struct©, ©union©, and ©enum© tags, one holding labels, one holding typedef names, variable, function, and enumerator identifiers, and one for each ©struct© or ©union© type holding the field names.}, and variable or function declarations in inner scopes with the same name as a declaration in an outer scope hide the outer declaration.
215This makes finding the proper declaration to match to a variable expression or function application a simple matter of symbol table lookup, which can be easily and efficiently implemented.
216\CFA, on the other hand, allows overloading of variable and function names, so long as the overloaded declarations do not have the same type, avoiding the multiplication of variable and function names for different types common in the C standard library, as in the following example:
217\begin{lstlisting}
218#include <limits.h>
219
220int max(int a, int b) { return a < b ? b : a; }  // (1)
221double max(double a, double b) { return a < b ? b : a; }  // (2)
222
223int max = INT_MAX;     // (3)
224double max = DBL_MAX;  // (4)
225
226max(7, -max);   // uses (1) and (3), by matching int type of 7
227max(max, 3.14); // uses (2) and (4), by matching double type of 3.14
228
229max(max, -max);  // ERROR: ambiguous
230int m = max(max, -max); // uses (1) and (3) twice, by return type
231\end{lstlisting}
232
233The presence of name overloading in \CFA means that simple table lookup is insufficient to match identifiers to declarations, and a type matching algorithm must be part of expression resolution.
234
235\subsection{Implicit Conversions}
236In addition to the multiple interpretations of an expression produced by name overloading, \CFA also supports all of the implicit conversions present in C, producing further candidate interpretations for expressions.
237C does not have a traditionally-defined inheritance hierarchy of types, but the C standard's rules for the ``usual arithmetic conversions'' define which of the built-in types are implicitly convertable to which other types, and the relative cost of any pair of such conversions from a single source type.
238\CFA adds to the usual arithmetic conversions rules for determining the cost of binding a polymorphic type variable in a function call; such bindings are cheaper than any \emph{unsafe} (narrowing) conversion, \eg ©int© to ©char©, but more expensive than any \emph{safe} (widening) conversion, \eg ©int© to ©double©.
239
240The expression resolution problem, then, is to find the unique minimal-cost interpretation of each expression in the program, where all identifiers must be matched to a declaration and implicit conversions or polymorphic bindings of the result of an expression may increase the cost of the expression.
241Note that which subexpression interpretation is minimal-cost may require contextual information to disambiguate.
242For instance, in the example in the previous subsection, ©max(max, -max)© cannot be unambiguously resolved, but ©int m = max(max, -max);© has a single minimal-cost resolution.
243©int m = (int)max((double)max, -(double)max)© is also be a valid interpretation, but is not minimal-cost due to the unsafe cast from the ©double© result of ©max© to ©int© (the two ©double© casts function as type ascriptions selecting ©double max© rather than casts from ©int max© to ©double©, and as such are zero-cost).
244
245\subsubsection{User-generated Implicit Conversions}
246One possible additional feature to \CFA included in this research proposal is \emph{user-generated implicit conversions}.
247Such a conversion system should be simple for user programmers to utilize, and fit naturally with the existing design of implicit conversions in C; ideally it would also be sufficiently powerful to encode C's usual arithmetic conversions itself, so that \CFA only has one set of rules for conversions.
248
249Ditchfield~\cite{Ditchfield:conversions} has laid out a framework for using polymorphic-conversion-constructor functions to create a directed acyclic graph (DAG) of conversions.
250A monomorphic variant of these functions can be used to mark a conversion arc in the DAG as only usable as the final step in a conversion.
251With these two types of conversion arcs, separate DAGs can be created for the safe and the unsafe conversions, and conversion cost can be represented as path length through the DAG.
252\begin{figure}[h]
253\centering
254\includegraphics{conversion_dag}
255\caption{A portion of the implicit conversion DAG for built-in types.}
256\end{figure}
257As can be seen in the example DAG above, there are either safe or unsafe paths between each of the arithmetic types listed; the ``final'' arcs are important both to avoid creating cycles in the signed-unsigned conversions, and to disambiguate potential diamond conversions (\eg, if the ©int© to ©unsigned int© conversion was not marked final there would be two length-two paths from ©int© to ©unsigned long©, and it would be impossible to choose which one; however, since the ©unsigned int© to ©unsigned long© arc can not be traversed after the final ©int© to ©unsigned int© arc, there is a single unambiguous conversion path from ©int© to ©unsigned long©).
258
259Open research questions on this topic include:
260\begin{itemize}
261\item Can a conversion graph be generated that represents each allowable conversion in C with a unique minimal-length path such that the path lengths accurately represent the relative costs of the conversions?
262\item Can such a graph representation can be usefully augmented to include user-defined types as well as built-in types?
263\item Can the graph can be efficiently represented and used in the expression resolver?
264\end{itemize}
265
266\subsection{Constructors and Destructors}
267Rob Shluntz, a current member of the \CFA research team, has added constructors and destructors to \CFA.
268Each type has an overridable default-generated zero-argument constructor, copy constructor, assignment operator, and destructor; for ©struct© types these functions each call their equivalents on each field of the ©struct©.
269This affects expression resolution because an ©otype© type variable ©T© implicitly adds four type assertions, one for each of these four functions, so assertion resolution is pervasive in \CFA polymorphic functions, even those without any explicit type assertions.
270The following example shows the implicitly-generated code in green:
271\begin{lstlisting}
272struct kv {
273    int key;
274    char *value;
275};
276
277¢void ?{}(kv *this) {
278    ?{}(&this->key);
279    ?{}(&this->value);
280}
281void ?{}(kv *this, kv that) {
282    ?{}(&this->key, that.key);
283    ?{}(&this->value, that.value);
284}
285kv ?=?(kv *this, kv that) {
286    ?=?(&this->key, that.key);
287    ?=?(&this->value, that.value);
288    return *this;
289}
290void ^?{}(kv *this) {
291    ^?{}(&this->key);
292    ^?{}(&this->value);
293}¢
294
295forall(otype T ¢| { void ?{}(T*); void ?{}(T*, T); T ?=?(T*, T); void ^?{}(T*); }¢)
296void foo(T);
297\end{lstlisting}
298
299\subsection{Generic Types}
300I have already added a generic type capability to \CFA, designed to efficiently and naturally integrate with \CFA's existing polymorphic functions.
301A 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:
302\begin{lstlisting}
303forall(otype R, otype S) struct pair {
304    R first;
305    S second;
306};
307
308forall(otype T)
309T value( pair(const char*, T) *p ) { return p->second; }
310
311pair(const char*, int) p = { "magic", 42 };
312int magic = value( &p );
313\end{lstlisting}
314For \emph{concrete} generic types, that is, those where none of the type parameters depend on polymorphic type variables (like ©pair(const char*, int)© above), the struct is essentially template expanded to a new struct type; for \emph{polymorphic} generic types (such as ©pair(const char*, T)© above), member access is handled by a runtime calculation of the field offset, based on the size and alignment information of the polymorphic parameter type.
315The default-generated constructors, destructor and assignment operator for a generic type are polymorphic functions with the same list of type parameters as the generic type definition.
316
317Aside from giving users the ability to create more parameterized types than just the built-in pointer, array and function types, the combination of generic types with polymorphic functions and implicit conversions makes the edge case where the resolver may enter an infinite loop much more common, as in the following code example:
318\begin{lstlisting}
319forall(otype T) struct box { T x; };
320
321void f(void*); // (1)
322
323forall(otype S)
324void f(box(S)* b) { // (2)
325        f(®(void*)0®);
326}
327\end{lstlisting}
328
329The loop in the resolver happens as follows:
330\begin{itemize} 
331\item Since there is an implicit conversion from ©void*© to any pointer type, the highlighted expression can be interpreted as either a ©void*©, matching ©f // (1)©, or a ©box(S)*© for some type ©S©, matching ©f // (2)©.
332\item To determine the cost of the ©box(S)© interpretation, a type must be found for ©S© which satisfies the ©otype© implicit type assertions (assignment operator, default and copy constructors, and destructor); one option is ©box(S2)© for some type ©S2©.
333\item The assignment operator, default and copy constructors, and destructor of ©box(T)© are also polymorphic functions, each of which require the type parameter ©T© to have an assignment operator, default and copy constructors, and destructor. When choosing an interpretation for ©S2©, one option is ©box(S3)©, for some type ©S3©.
334\item The previous step repeats until stopped, with four times as much work performed at each step.
335\end{itemize}
336This problem can occur in any resolution context where a polymorphic function that can satisfy its own type assertions is required for a possible interpretation of an expression with no constraints on its type, and is thus not limited to combinations of generic types with ©void*© conversions, though constructors for generic types often satisfy their own assertions and a polymorphic conversion such as the ©void*© conversion to a polymorphic variable can create an expression with no constraints on its type.
337As discussed above, the \CFA expression resolver must handle this possible infinite recursion somehow, and it occurs fairly naturally in code like the above that uses generic types.
338
339\subsection{Tuple Types}
340\CFA adds \emph{tuple types} to C, a syntactic facility for referring to lists of values anonymously or with a single identifier.
341An identifier may name a tuple, and a function may return one.
342Particularly relevantly for resolution, a tuple may be implicitly \emph{destructured} into a list of values, as in the call to ©swap© below:
343\begin{lstlisting}
344[char, char] x = [ '!', '?' ];
345int x = 42;
346
347forall(otype T) [T, T] swap( T a, T b ) { return [b, a]; }
348
349x = swap( x ); // destructure [char, char] x into two elements of parameter list
350// cannot use int x for parameter, not enough arguments to swap
351\end{lstlisting}
352Tuple destructuring means that the mapping from the position of a subexpression in the argument list to the position of a paramter in the function declaration is not straightforward, as some arguments may be expandable to different numbers of parameters, like ©x© above.
353
354\subsection{Reference Types}
355I have been designing \emph{reference types} for \CFA, in collaboration with the rest of the \CFA research team.
356Given some type ©T©, a ©T&© (``reference to ©T©'') is essentially an automatically dereferenced pointer; with these semantics most of the C standard's discussions of lvalues can be expressed in terms of references instead, with the benefit of being able to express the difference between the reference and non-reference version of a type in user code.
357References preserve C's existing qualifier-dropping lvalue-to-rvalue conversion (\eg a ©const volatile int&© can be implicitly converted to a bare ©int©); the reference proposal also adds a rvalue-to-lvalue conversion to \CFA, implemented by storing the value in a new compiler-generated temporary and passing a reference to the temporary.
358These two conversions can chain, producing a qualifier-dropping conversion for references, for instance converting a reference to a ©const int© into a reference to a non-©const int© by copying the originally refered to value into a fresh temporary and taking a reference to this temporary, as below:
359\begin{lstlisting} 
360const int magic = 42;
361
362void inc_print( int& x ) { printf("%d\n", ++x); }
363
364print_inc( magic ); // legal; implicitly generated code in green below:
365
366¢int tmp = magic;¢ // copies to safely strip const-qualifier
367¢print_inc( tmp );¢ // tmp is incremented, magic is unchanged
368\end{lstlisting}
369These reference conversions may also chain with the other implicit type conversions.
370The main implication of this for expression resolution is the multiplication of available implicit conversions, though in a restricted context that may be able to be treated efficiently as a special case.
371
372\subsection{Special Literal Types}
373Another proposal currently under consideration for the \CFA type-system is assigning special types to the literal values ©0© and ©1©.
374Implicit conversions from these types allow ©0© and ©1© to be considered as values of many different types, depending on context, allowing expression desugarings like ©if ( x ) {}© $\Rightarrow$ ©if ( x != 0 ) {}© to be implemented efficiently and precicely.
375This approach is a generalization of C's existing behaviour of treating ©0© as either an integer zero or a null pointer constant, and treating either of those values as boolean false.
376The main implication for expression resolution is that the frequently encountered expressions ©0© and ©1© may have a large number of valid interpretations.
377
378\subsection{Deleted Function Declarations}
379One final proposal for \CFA with an impact on the expression resolver is \emph{deleted function declarations}; in \CCeleven, a function declaration can be deleted as below:
380\begin{lstlisting}
381int somefn(char) = delete;
382\end{lstlisting}
383This feature is typically used in \CCeleven to make a type non-copyable by deleting its copy constructor and assignment operator, or forbidding some interpretations of a polymorphic function by specifically deleting the forbidden overloads.
384To add a similar feature to \CFA would involve including the deleted function declarations in expression resolution along with the normal declarations, but producing a compiler error if the deleted function was the best resolution.
385How conflicts should be handled between resolution of an expression to both a deleted and a non-deleted function is a small but open research question.
386
387\section{Expression Resolution}
388The expression resolution problem is essentially to determine an optimal matching between some combination of argument interpretations and the parameter list of some overloaded instance of a function; the argument interpretations are produced by recursive invocations of expression resolution, where the base case is zero-argument functions (which are, for purposes of this discussion, semantically equivalent to named variables or constant literal expressions).
389Assuming that the matching between a function's parameter list and a combination of argument interpretations can be done in $O(p^k)$ time, where $p$ is the number of parameters and $k$ is some positive number, if there are $O(i)$ valid interpretations for each subexpression, there will be $O(i)$ candidate functions and $O(i^p)$ possible argument combinations for each expression, so a single recursive call to expression resolution will take $O(i^{p+1} \cdot p^k)$ time if it compares all combinations.
390Given this bound, resolution of a single top-level expression tree of depth $d$ takes $O(i^{p+1} \cdot p^{k \cdot d})$ time\footnote{The call tree will have leaves at depth $O(d)$, and each internal node will have $O(p)$ fan-out, producing $O(p^d)$ total recursive calls.}.
391Expression resolution is somewhat unavoidably exponential in $p$, the number of function parameters, and $d$, the depth of the expression tree, but these values are fixed by the user programmer, and generally bounded by reasonably small constants.
392$k$, on the other hand, is mostly dependent on the representation of types in the system and the efficiency of type assertion checking; if a candidate argument combination can be compared to a function parameter list in linear time in the length of the list (\ie $k = 1$), then the $p^{k \cdot d}$ term is linear in the input size of the source code for the expression, otherwise the resolution algorithm will exibit sub-linear performance scaling on code containing more-deeply nested expressions.
393The number of valid interpretations of any subexpression, $i$, is bounded by the number of types in the system, which is possibly infinite, though practical resolution algorithms for \CFA must be able to place some finite bound on $i$, possibly at the expense of type-system completeness.
394
395The research goal of this project is to develop a performant expression resolver for \CFA; this analysis suggests two primary areas of investigation to accomplish that end.
396The first is efficient argument-parameter matching; Bilson\cite{Bilson03} mentions significant optimization opportunities available in the current literature to improve on the existing CFA compiler.
397%TODO: look up and lit review
398The second, and likely more fruitful, area of investigation is heuristics and algorithmic approaches to reduce the number of argument interpretations considered in the common case; given the large ($p+1$) exponent on number of interpretations considered in the runtime analysis, even small reductions here could have a significant effect on overall resolver runtime.
399The discussion below presents a number of largely orthagonal axes for expression resolution algorithm design to be investigated, noting prior work where applicable.
400
401\subsection{Argument-Parameter Matching}
402The first axis we consider is argument-parameter matching --- whether the type matching for a candidate function to a set of candidate arguments is directed by the argument types or the parameter types.
403
404\subsubsection{Argument-directed (``Bottom-up'')}
405Baker's algorithm for expression resolution\cite{Baker82} pre-computes argument candidates, from the leaves of the expression tree up.
406For each candidate function, Baker attempts to match argument types to parameter types in sequence, failing if any parameter cannot be matched.
407
408Bilson\cite{Bilson03} similarly pre-computes argument candidates in the original \CFA compiler, but then explicitly enumerates all possible argument combinations for a multi-parameter function; these argument combinations are matched to the parameter types of the candidate function as a unit rather than individual arguments.
409This is less efficient than Baker's approach, as the same argument may be compared to the same parameter many times, but allows a more straightforward handling of polymorphic type binding and multiple return types.
410It is possible the efficiency losses here relative to Baker could be significantly reduced by application of memoization to the argument-parameter type comparisons.
411
412\subsubsection{Parameter-directed (``Top-down'')}
413Unlike Baker and Bilson, Cormack's algorithm\cite{Cormack81} requests argument candidates which match the type of each parameter of each candidate function, from the top-level expression down; memoization of these requests is presented as an optimization.
414As presented, this algorithm requires the result of the expression to have a known type, though an algorithm based on Cormack's could reasonably request a candidate set of any return type, though such a set may be quite large.
415
416\subsubsection{Hybrid}
417This proposal includes the investigation of hybrid top-down/bottom-up argument-parameter matching.
418A reasonable hybrid approach might be to take a top-down approach when the expression to be matched is known to have a fixed type, and a bottom-up approach in untyped contexts.
419This may include switches from one type to another at different levels of the expression tree, for instance:
420\begin{lstlisting}
421forall(otype T)
422int f(T x);  // (1)
423
424void* f(char y);  // (2)
425
426int x = f( f( '!' ) );
427\end{lstlisting}
428Here, the outer call to ©f© must have a return type that is (implicitly convertable to) ©int©, so a top-down approach could be used to select \textit{(1)} as the proper interpretation of ©f©. \textit{(1)}'s parameter ©x© here, however, is an unbound type variable, and can thus take a value of any complete type, providing no guidance for the choice of candidate for the inner ©f©. The leaf expression ©'!'©, however, gives us a zero-cost interpretation of the inner ©f© as \textit{(2)}, providing a minimal-cost expression resolution where ©T© is bound to ©void*©.
429
430Deciding when to switch between bottom-up and top-down resolution in a hybrid algorithm is a necessarily heuristic process, and though finding good heuristics for it is an open question, one reasonable approach might be to switch from top-down to bottom-up when the number of candidate functions exceeds some threshold.
431
432\subsection{Implicit Conversion Application}
433Baker's\cite{Baker82} and Cormack's\cite{Cormack81} algorithms do not account for implicit conversions\footnote{Baker does briefly comment on an approach for handling implicit conversions.}; both assume that there is at most one valid interpretation of a given expression for each distinct type.
434Integrating implicit conversion handling into their algorithms provides some choice of implementation approach.
435
436\subsubsection{On Parameters}
437Bilson\cite{Bilson03} did account for implicit conversions in his algorithm, but it is not clear his approach is optimal.
438His algorithm integrates checking for valid implicit conversions into the argument-parameter matching step, essentially trading more expensive matching for a smaller number of argument interpretations.
439This approach may result in the same subexpression being checked for a type match with the same type multiple times, though again memoization may mitigate this cost, and this approach will not generate implicit conversions that are not useful to match the containing function.
440
441\subsubsection{On Arguments}
442Another approach would be to generate a set of possible implicit conversions for each set of interpretations of a given argument.
443This would have the benefit of detecting ambiguous interpretations of arguments at the level of the argument rather than its containing call, would also never find more than one interpretation of the argument with a given type, and would re-use calculation of implicit conversions between function candidates.
444On the other hand, this approach may unncessarily generate argument interpretations that will never match a parameter, wasting work.
445Further, in the presence of tuple types this approach may lead to a combinatorial explosion of argument interpretations considered, unless the tuple can be considered as a sequence of elements rather than a unified whole.
446
447\subsection{Candidate Set Generation}
448Cormack\cite{Cormack81}, Baker\cite{Baker82} and Bilson\cite{Bilson03} all generate the complete set of candidate argument interpretations before attempting to match the containing function call expression.
449However, given that the top-level expression interpretation that is ultimately chosen will be the minimal-cost valid interpretation, any consideration of non-minimal-cost interpretations is in some sense wasted work.
450If we assume that user programmers will generally write function calls with relatively low-cost interpretations, a possible work-saving heuristic is to generate only the lowest-cost argument interpretations first, attempt to find a valid top-level interpretation using them, and only if that fails generate the higher-cost argument interpretations.
451
452\subsubsection{Eager}
453Within the eager approach taken by Cormack, Baker and Bilson, there are still variants to explore.
454Cormack and Baker do not account for implict conversions, and thus do not account for the possibility of multiple valid interpretations with distinct costs; Bilson, on the other hand, sorts the list of interpretations to aid in finding minimal-cost interpretations.
455Sorting the lists of argument or function call interpretations by cost at some point during resolution may provide useful opportunities to short-circuit expression evaluation when a minimal-cost interpretation is found, though it is not clear if this short-circuiting behaviour would justify the cost of the sort.
456
457\subsubsection{Lazy}
458In the presence of implicit conversions, many argument interpretations may match a given parameter by application of an appropriate implicit conversion.
459However, if user programmers actually use relatively few implicit conversions, then the ``on arguments'' approach to implicit conversions will generate a large number of high-cost interpretations which may never be used.
460The essence of the lazy approach to candidate set generation is to wrap the matching algorithm into the element generator of a lazy list type, only generating as few elements at a time as possible to ensure that the next-smallest-cost interpretation has been generated.
461Assuming that argument interpretations are provided to the parameter matching algorithm in sorted order, a sorted list of function call interpretations can be produced by generating combinations of arguments sorted by total cost\footnote{I have already developed a lazy $n$-way combination generation algorithm to perform this task.}, then generating function call interpretations in the order suggested by this list.
462Note that the function call interpretation chosen may have costs of its own, for instance polymorphic type binding, so in some cases a number of argument combinations (any combination whose marginal cost does not exceed the cost of the function call interpretation itself) may need to be considered to determine the next-smallest-cost function call interpretation.
463Ideally, this candidate generation approach will lead to very few unused candidates being generated (in the expected case where the user programmer has, in fact, provided a validly-typable program), but this research project will need to determine whether or not the overheads of lazy generation exceed the benefit produced from considering fewer interpretations.
464
465\subsubsection{Stepwise Lazy}
466As a compromise between the trade-offs of the eager and lazy approaches, it would also be interesting to investigate a ``stepwise lazy'' approach, where all the interpretations for some ``step'' are eagerly generated, then the interpretations in the later steps are only generated on demand.
467Under this approach the \CFA resolver could, for instance, try expression interpretations in the following order:
468\begin{enumerate}
469\item Interpretations with no polymorphic type binding or implicit conversions.
470\item Interpretations containing no polymorphic type binding and at least one safe implicit conversion.
471\item Interpretations containing polymorphic type binding, but only safe implicit conversions.
472\item Interpretations containing at least one unsafe implicit conversion.
473\end{enumerate} 
474If a valid expression interpretation is found in one step, it is guaranteed to be lower-cost than any interpretation in a later step (by the structure of \CFA interpretation costs), so no step after the first one where a valid interpretation can be found need be considered.
475This may save significant amounts of work, especially given that the first steps avoid potentially expensive handling of implicit conversions and type assertion satisfaction entirely.
476
477%\subsection{Parameter-Directed}
478%\textbf{TODO: Richard's algorithm isn't Baker (Cormack?), disentangle from this section \ldots}.
479%The expression resolution algorithm used by the existing iteration of CFA is based on Baker's\cite{Baker82} algorithm for overload resolution in Ada.
480%The essential idea of this algorithm is to first find the possible interpretations of the most deeply nested subexpressions, then to use these interpretations to recursively generate valid interpretations of their superexpressions.
481%To simplify matters, the only expressions considered in this discussion of the algorithm are function application and literal expressions; other expression types can generally be considered to be variants of one of these for the purposes of the resolver, \eg variables are essentially zero-argument functions.
482%If we consider expressions as graph nodes with arcs connecting them to their subexpressions, these expressions form a DAG, generated by the algorithm from the bottom up.
483%Literal expressions are represented by leaf nodes, annotated with the type of the expression, while a function application will have a reference to the function declaration chosen, as well as arcs to the interpretation nodes for its argument expressions; functions are annotated with their return type (or types, in the case of multiple return values).
484%
485%\textbf{TODO: Figure}
486%
487%Baker's algorithm was designed to account for name overloading; Richard Bilson\cite{Bilson03} extended this algorithm to also handle polymorphic functions, implicit conversions and multiple return types when designing the original \CFA compiler.
488%The core of the algorithm is a function which Baker refers to as $gen\_calls$.
489%$gen\_calls$ takes as arguments the name of a function $f$ and a list containing the set of possible subexpression interpretations $S_j$ for each argument of the function and returns a set of possible interpretations of calling that function on those arguments.
490%The subexpression interpretations are generally either singleton sets generated by the single valid interpretation of a literal expression, or the results of a previous call to $gen\_calls$.
491%If there are no valid interpretations of an expression, the set returned by $gen\_calls$ will be empty, at which point resolution can cease, since each subexpression must have at least one valid interpretation to produce an interpretation of the whole expression.
492%On the other hand, if for some type $T$ there is more than one valid interpretation of an expression with type $T$, all interpretations of that expression with type $T$ can be collapsed into a single \emph{ambiguous expression} of type $T$, since the only way to disambiguate expressions is by their return types.
493%If a subexpression interpretation is ambiguous, than any expression interpretation containing it will also be ambiguous.
494%In the variant of this algorithm including implicit conversions, the interpretation of an expression as type $T$ is ambiguous only if there is more than one \emph{minimal-cost} interpretation of the expression as type $T$, as cheaper expressions are always chosen in preference to more expensive ones.
495%
496%Given this description of the behaviour of $gen\_calls$, its implementation is quite straightforward: for each function declaration $f_i$ matching the name of the function, consider each of the parameter types $p_j$ of $f_i$, attempting to match the type of an element of $S_j$ to $p_j$ (this may include checking of implicit conversions).
497%If no such element can be found, there is no valid interpretation of the expression using $f_i$, while if more than one such (minimal-cost) element is found than an ambiguous interpretation with the result type of $f_i$ is produced.
498%In the \CFA variant, which includes polymorphic functions, it is possible that a single polymorphic function definition $f_i$ can produce multiple valid interpretations by different choices of type variable bindings; these interpretations are unambiguous so long as the return type of $f_i$ is different for each type binding.
499%If all the parameters $p_j$ of $f_i$ can be uniquely matched to a candidate interpretation, then a valid interpretation based on $f_i$ and those $p_j$ is produced.
500%$gen\_calls$ collects the produced interpretations for each $f_i$ and returns them; a top level expression is invalid if this list is empty, ambiguous if there is more than one (minimal-cost) result, or if this single result is ambiguous, and valid otherwise.
501%
502%In this implementation, resolution of a single top-level expression takes time $O(\ldots)$, where \ldots. \textbf{TODO:} \textit{Look at 2.3.1 in Richard's thesis when working out complexity; I think he does get the Baker algorithm wrong on combinations though, maybe\ldots}
503%
504%\textbf{TODO: Basic Lit Review} \textit{Look at 2.4 in Richard's thesis for any possible more-recent citations of Baker\ldots} \textit{Look back at Baker's related work for other papers that look similar to what you're doing, then check their citations as well\ldots} \textit{Look at Richard's citations in 2.3.2 w.r.t. type data structures\ldots}
505%\textit{CormackWright90 seems to describe a solution for the same problem, mostly focused on how to find the implicit parameters}
506
507\section{Proposal}
508Baker\cite{Baker82} discussed various expression resolution algorithms that could handle name overloading, but left experimental comparison of those algorithms to future work; Bilson\cite{Bilson03} described one extension of Baker's algorithm to handle implicit conversions, but did not fully explore the space of algorithmic approaches to handle both overloaded names and implicit conversions.
509This project is intended to experimentally test a number of expression resolution algorithms which are powerful enough to handle the \CFA type-system, including both name overloading and implicit conversions.
510This comparison will close Baker's open research question, as well as potentially improving on Bilson's \CFA compiler.
511
512Rather than testing all of these algorithms in-place in the \CFA compiler, a resolver prototype will be developed which acts on a simplified input language encapsulating the essential details of the \CFA type-system\footnote{Note that this simplified input language is not required to be a usable programming language.}.
513Multiple variants of this resolver prototype will be implemented, each encapsulating a different expression resolution variant, sharing as much code as feasible.
514These variants will be instrumented to test runtime performance, and run on a variety of input files; the input files may be generated programmatically or from exisiting code in \CFA or similar languages.
515These experimental results will allow the research team to determine the algorithm likely to be most performant in practical use, and replace CFA's existing expression resolver with that code.
516The experimental results will also provide some empirical sense of the compile-time cost of various language features by comparing the results of the most performant resolver variant that supports the feature with the most performant resolver variant that doesn't, a useful capability to guide language design.
517
518This proposed project should provide valuable data on how to implement a performant compiler for modern programming languages such as \CFA with powerful static type-systems, specifically targeting the feature interaction between name overloading and implicit conversions.
519
520\appendix
521\section{Completion Timeline}
522The following is a preliminary estimate of the time necessary to complete the major components of this research project:
523\begin{center}
524\begin{tabular}{ | r @{--} l | p{4in} | }
525\hline       May 2015 & April 2016   & Project familiarization and generic types design and implementation. \\
526\hline       May 2016 & April 2017   & Design and implement resolver prototype and run performance experiments. \\
527\hline       May 2017 & August 2017  & Integrate new language features and best-performing resolver prototype into CFA. \\
528\hline September 2017 & January 2018 & Thesis writing and defense. \\
529\hline
530\end{tabular}
531\end{center}
532
533\addcontentsline{toc}{section}{\refname}
534\bibliographystyle{plain}
535\bibliography{cfa}
536
537%\addcontentsline{toc}{section}{\indexname} % add index name to table of contents
538%\begin{theindex}
539%Italic page numbers give the location of the main entry for the referenced term.
540%Plain page numbers denote uses of the indexed term.
541%Entries for grammar non-terminals are italicized.
542%A typewriter font is used for grammar terminals and program identifiers.
543%\indexspace
544%\input{comp_II.ind}
545%\end{theindex}
546
547\end{document}
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