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@@ -4650,7 +4650,7 @@ A straightforward way to compute the interference graph is to look at
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the set of live locations between each instruction and add an edge to
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the graph for every pair of variables in the same set. This approach
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is less than ideal for two reasons. First, it can be expensive because
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-it takes $O(n^2)$ time to consider at every pair in a set of $n$ live
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+it takes $O(n^2)$ time to consider every pair in a set of $n$ live
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locations. Second, in the special case where two locations hold the
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same value (because one was assigned to the other), they can be live
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at the same time without interfering with each other.
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@@ -12949,6 +12949,9 @@ from the set.
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\section{Further Reading}
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+\citet{Appel90} describes many data representation approaches,
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+including the ones used in the compilation of Standard ML.
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+
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There are many alternatives to copying collectors (and their bigger
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siblings, the generational collectors) when its comes to garbage
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collection, such as mark-and-sweep~\citep{McCarthy:1960dz} and
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@@ -12971,6 +12974,7 @@ meet every year at the International Symposium on Memory Management to
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present these findings.
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+
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\chapter{Functions}
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\label{ch:Lfun}
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@@ -14771,7 +14775,8 @@ the point where the \code{lambda} is applied. An efficient solution to
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the problem, due to \citet{Cardelli:1983aa}, is to bundle the values
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of the free variables together with a function pointer into a tuple,
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an arrangement called a \emph{flat closure} (which we shorten to just
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-``closure''). \index{subject}{closure}\index{subject}{flat closure}
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+``closure'').\index{subject}{closure}\index{subject}{flat closure}
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+%
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Fortunately, we have all the ingredients to make closures:
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Chapter~\ref{ch:Lvec} gave us tuples and Chapter~\ref{ch:Lfun} gave us
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function pointers. The function pointer resides at index $0$ and the
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@@ -16307,16 +16312,18 @@ dialect of LISP adopted lexical scoping and
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\citet{Guy-L.-Steele:1978yq} demonstrated how to efficiently compile
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Scheme programs. However, environments were represented as linked
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lists, so variable lookup was linear in the size of the
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-environment. In this chapter we represent environments using flat
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-closures, which were invented by
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+environment. \citet{Appel91} gives a detailed description of several
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+closure representations. In this chapter we represent environments
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+using flat closures, which were invented by
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\citet{Cardelli:1983aa,Cardelli:1984aa} for the purposes of compiling
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-the ML language~\citep{Gordon:1978aa,Milner:1990fk}. With flat
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+the ML language~\citep{Gordon:1978aa,Milner:1990fk}. With flat
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closures, variable lookup is constant time but the time to create a
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closure is proportional to the number of its free variables. Flat
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closures were reinvented by \citet{Dybvig:1987ab} in his Ph.D. thesis
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and used in Chez Scheme version 1~\citep{Dybvig:2006aa}.
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+
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\chapter{Dynamic Typing}
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\label{ch:Ldyn}
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Jeremy Siek