Changeset c1f3d1a8
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 Apr 20, 2019, 6:20:13 PM (5 years ago)
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 ADT, aaronthesis, armeh, astexperimental, cleanupdtors, enum, forallpointerdecay, jacob/cs343translation, jenkinssandbox, master, newast, newastuniqueexpr, pthreademulation, qualifiedEnum
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 2834e99
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 e71272a
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 doc/theses/aaron_moss_PhD/phd
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doc/theses/aaron_moss_PhD/phd/background.tex
re71272a rc1f3d1a8 12 12 As this development has been proceeding concurrently with the work described in this thesis, the state of \CFA{} has been somewhat of a moving target; however, Moss~\etal~\cite{Moss18} provides a reasonable summary of the current design. 13 13 Notable features added during this period include generic types (Chapter~\ref{genericchap}), constructors and destructors \cite{Schluntz17}, improved support for tuples \cite{Schluntz17}, reference types \cite{Moss18}, firstclass concurrent and parallel programming support \cite{Delisle18}, as well as numerous pieces of syntactic sugar and the start of an idiomatic standard library \cite{Moss18}. 14 15 This thesis is primarily concerned with the \emph{expression resolution} portion of \CFA{} typechecking; resolution is discussed in more detail in Chapter~\ref{resolutionchap}, but is essentially determining which declarations the identifiers in each expression correspond to. 16 Given that no simultaneouslyvisible C declarations share identifiers, expression resolution in C is not difficult, but the added features of \CFA{} make its resolution algorithms significantly more complex. 17 Due to this complexity, the expressionresolution pass in \CFACC{} requires 95\% of compiler runtime on some source files, making an efficient procedure for expression resolution a requirement for a performant \CFA{} compiler. 14 18 15 19 The selection of features presented in this chapter are chosen to elucidate the design constraints of the work presented in this thesis. … … 141 145 However, !twice!$_2$ also works for any type !S! that has an addition operator defined for it. 142 146 143 Finding 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 .147 Finding 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\footnote{\CFACC{} actually performs a typeunification computation for assertion satisfaction rather than an expression resolution computation; see Section~\ref{assnsatsec} for details.}. 144 148 If a polymorphic function can be used to satisfy one of its own type assertions, this recursion may not terminate, as it is possible that that function is examined as a candidate for its own assertion unboundedly repeatedly. 145 149 To avoid such infinite loops, \CFACC{} 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 otherwise semantically welltyped expressions that cannot be resolved by \CFACC{}. … … 225 229 One contribution of this thesis, discussed in Section~\ref{convcostsec}, is a number of refinements to this cost model to more efficiently resolve polymorphic function calls. 226 230 227 The expression resolution problem, which is the focus of Chapter~\ref{resolutionchap}, is to findthe unique minimalcost 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.231 In the context of these implicit conversions, the expression resolution problem can be restated as finding the unique minimalcost 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. 228 232 While semantically valid \CFA{} code always has such a unique minimalcost interpretation, \CFACC{} must also be able to detect and report as errors expressions that have either no interpretation or multiple ambiguous minimalcost interpretations. 229 233 Note that which subexpression interpretation is minimalcost may require contextual information to disambiguate. 
doc/theses/aaron_moss_PhD/phd/resolutionheuristics.tex
re71272a rc1f3d1a8 2 2 \label{resolutionchap} 3 3 4 % consider using "satisfaction" throughout when talking about assertions5 % "valid" instead of "feasible" interpretations6 7 4 The main task of the \CFACC{} typechecker is \emph{expression resolution}: determining which declarations the identifiers in each expression correspond to. 8 Resolution is a straightforward task in C, as no declarations share identifiers, but in \CFA{}, the name overloading features discussed in Section~\ref{overloadingsec} generate multiple candidate declarations for each identifier.5 Resolution is a straightforward task in C, as no simultaneouslyvisible declarations share identifiers, but in \CFA{}, the name overloading features discussed in Section~\ref{overloadingsec} generate multiple candidate declarations for each identifier. 9 6 A given matching between identifiers and declarations in an expression is an \emph{interpretation}; an interpretation also includes information about polymorphic type bindings and implicit casts to support the \CFA{} features discussed in Sections~\ref{polyfuncsec} and~\ref{implicitconvsec}, each of which increase the number of valid candidate interpretations. 10 7 To choose among valid interpretations, a \emph{conversion cost} is used to rank interpretations. … … 12 9 13 10 \section{Expression Resolution} 11 12 The expression resolution pass in \CFACC{} must traverse the input expression, match identifiers to available declarations, rank candidate interpretations according to their conversion cost, and check type assertion satisfaction for these candidates. 13 Once the set of valid interpretations for the toplevel expression has been found, the expression resolver selects the unique minimalcost candidate or reports an error. 14 15 The expression resolution problem in \CFA{} is more difficult than the analogous problems in C or \CC{}. 16 As mentioned above, the lack of name overloading in C makes its resolution problem substantially easier, but a comparison of the richer type systems in \CFA{} and \CC{} highlights some of the challenges in \CFA{} expression resolution. 17 The key distinction between \CFA{} and \CC{} resolution is that \CC{} uses a greedy algorithm for selection of candidate functions given their argument interpretations, whereas \CFA{} allows contextual information from superexpressions to influence the choice among candidate functions. 18 One key use of this contextual information is for type inference of polymorphic return types; \CC{} requires explicit specification of template type parameters which only occur in a function's return type, while \CFA{} allows the instantiation of these type parameters to be inferred from context (and in fact does not allow explicit specification of type parameters to a function). 19 Similarly, while both \CFA{} and \CC{} rank candidate functions based on a cost metric for implicit conversions, \CFA{} allows a suboptimal subexpression interpretation to be selected if it allows a lowercost overall interpretation, while \CC{} requires that each subexpression interpretation have minimal cost. 20 Because of this use of contextual information, the \CFA{} expression resolver must consider multiple interpretations of each function argument, while the \CC{} compiler has only a single interpretation for each argument\footnote{With the exception of addressof operations on functions.}. 21 Additionally, until the introduction of concepts in \CCtwenty{} \cite{C++Concepts}, \CC{} expression resolution has no analogue to \CFA{} assertion satisfaction checking, a further complication for a \CFA{} compiler. 22 The precise definition of \CFA{} expression resolution in this section further expands on the challenges of this problem. 14 23 15 24 \subsection{Type Unification} … … 23 32 \subsection{Conversion Cost} \label{convcostsec} 24 33 25 C does not have an explicit cost model for implicit conversions, but the ``usual arithmetic conversions'' \cite[\S{}6.3.1.8]{C11} used to decide which arithmetic operators to use define one implicitly. 34 \CFA{}, like C, allows inexact matches between the type of function parameters and function call arguments. 35 Both languages insert \emph{implicit conversions} in these situations to produce an exact type match, and \CFA{} also uses the relative \emph{cost} of different conversions to select among overloaded function candidates. 36 C does not have an explicit cost model for implicit conversions, but the ``usual arithmetic conversions'' \cite[\S{}6.3.1.8]{C11} used to decide which arithmetic operators to apply define one implicitly. 26 37 The only context in which C has name overloading is the arithmetic operators, and the usual arithmetic conversions define a \emph{common type} for mixedtype arguments to binary arithmetic operators. 27 38 Since for backwardcompatibility purposes the conversion costs of \CFA{} must produce an equivalent result to these common type rules, it is appropriate to summarize \cite[\S{}6.3.1.8]{C11} here: … … 39 50 Degree of safe conversion is calculated as path weight in a directed graph of safe conversions between types; Bilson's version and the current version of this graph are in Figures~\ref{bilsonconvfig} and~\ref{extendedconvfig}, respectively. 40 51 The safe conversion graph is designed such that the common type $c$ of two types $u$ and $v$ is compatible with the C standard definitions from \cite[\S{}6.3.1.8]{C11} and can be calculated as the unique type minimizing the sum of the path weights of $\overrightarrow{uc}$ and $\overrightarrow{vc}$. 41 The following example lists the cost in the Bilson model of calling each of the following functions with two !int! parameters :52 The following example lists the cost in the Bilson model of calling each of the following functions with two !int! parameters, where the interpretation with the minimum total cost will be selected: 42 53 43 54 \begin{cfa} … … 73 84 74 85 \begin{cfa} 75 forall(dtype T  { T& ++?(T&); }) T& advance$\( 1\)$(T& i, int n);76 forall(dtype T  { T& ++?(T&); T& ?+=?(T&, int)}) T& advance$\( 2\)$(T& i, int n);86 forall(dtype T  { T& ++?(T&); }) T& advance$\(_1\)$(T& i, int n); 87 forall(dtype T  { T& ++?(T&); T& ?+=?(T&, int)}) T& advance$\(_2\)$(T& i, int n); 77 88 \end{cfa} 78 89 … … 96 107 The new cost model accounts for the fact that functions with more polymorphic variables are less constrained by introducing a $var$ cost element that counts the number of type variables on a candidate function. 97 108 In the example above, !f!$_1$ has $var = 2$, while the others have $var = 1$. 109 98 110 The new cost model also accounts for a nuance unhandled by Ditchfield or Bilson, in that it makes the more specific !f!$_4$ cheaper than the more generic !f!$_3$; !f!$_4$ is presumably somewhat optimized for handling pointers, but the prior \CFA{} cost model could not account for the more specific binding, as it simply counted the number of polymorphic unifications. 99 100 111 In the modified model, each level of constraint on a polymorphic type in the parameter list results in a decrement of the $specialization$ cost element, which is shared with the count of assertions due to their common nature as constraints on polymorphic type bindings. 101 112 Thus, all else equal, if both a binding to !T! and a binding to !T*! are available, the model chooses the more specific !T*! binding with $specialization = 1$. … … 150 161 However, in context, an unsafe conversion is required to downcast the return type of !g!$_1$ to an !int! suitable for !f!, for a total cost of $(1,0,0,0,0,0,0)$ for !f( g!$_1$!(42) )!. 151 162 If !g!$_2$ is chosen, on the other hand, there is a safe upcast from the !int! type of !42! to !long!, but no cast on the return of !g!$_2$, for a total cost of $(0,0,1,0,0,0,0)$ for !f( g!$_2$!(42) )!; as this is cheaper, !g!$_2$ is chosen. 152 Due to this design, all valid interpretations of subexpressions must in general be propagated to the top of the expression tree before any can be eliminated, a lazy form of expression resolution, as opposed to the eager expression resolution allowed by C , where each expression can be resolved given only the resolution of its immediate subexpressions.163 Due to this design, all valid interpretations of subexpressions must in general be propagated to the top of the expression tree before any can be eliminated, a lazy form of expression resolution, as opposed to the eager expression resolution allowed by C or \CC{}, where each expression can be resolved given only the resolution of its immediate subexpressions. 153 164 154 165 If there are no valid interpretations of the toplevel expression, expression resolution fails and must produce an appropriate error message.
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