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Daniel Hillerström
2021-05-25 19:28:17 +01:00
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thesis.tex
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@@ -304,7 +304,7 @@
\href{http://pervasiveparallelism.inf.ed.ac.uk}{EP/L01503X/1} (EPSRC \href{http://pervasiveparallelism.inf.ed.ac.uk}{EP/L01503X/1} (EPSRC
Centre for Doctoral Training in Pervasive Parallelism), and by ERC Centre for Doctoral Training in Pervasive Parallelism), and by ERC
Consolidator Grant Skye (grant number 682315). I finished this Consolidator Grant Skye (grant number 682315). I finished this
dissertation whilst being employed by the UKRI Future Leaders dissertation whilst being employed on the UKRI Future Leaders
Fellowship ``Effect Handler Oriented Programming'' (reference number Fellowship ``Effect Handler Oriented Programming'' (reference number
MR/T043830/1). MR/T043830/1).
@@ -420,12 +420,12 @@ meaning the programmer can compose any number of complementary
handlers to obtain a full implementation of some handlers to obtain a full implementation of some
interface~\cite{HillerstromL16}. interface~\cite{HillerstromL16}.
% %
The ability to seamless compose handlers gives to a programming The ability to seamless compose handlers gives rise to a new
paradigm which we shall call \emph{effect handler oriented programming paradigm which we shall call \emph{effect handler oriented
programming} in which the meaning of effectful programs may be programming} in which the meaning of effectful programs may be
decomposed into a collection of fine-grained effect handlers. decomposed into a collection of fine-grained effect handlers.
The key enabler for seamless composition is \emph{first-class The key enabler for seamlessly composition is \emph{first-class
control}, which provides a mechanism for reifying the program control}, which provides a mechanism for reifying the program
control state as a first-class data object known as a control state as a first-class data object known as a
continuation~\cite{FriedmanHK84}. continuation~\cite{FriedmanHK84}.
@@ -17109,23 +17109,23 @@ to support repeated queries as follows.
\[ \[
\bl \bl
\ECount'_n : ((\Nat_n \to \Bool) \to \Bool) \to \Nat\\ \ECount'_n : ((\Nat_n \to \Bool) \to \Bool) \to \Nat\\
\ECount'_n\,pred \defas \ECount'_n~pred \defas
\bl \bl
\Let\; h \revto \Handle\; pred\,(\lambda i. \Do\; \Branch~i)\; \With\\ \Let\; h \revto \Handle\; pred\,(\lambda i. \Do\; \Branch~i)\; \With\\
\quad\ba[t]{@{}l@{\hspace{1.5ex}}c@{\hspace{1.5ex}}l@{}} \quad\ba[t]{@{}l@{}c@{}l@{}}
\Return\; x &\mapsto& \lambda s. \If\; x\; \Then\; 1 \;\Else\; 0 \\ \Return\; x &\mapsto& \lambda s. \If\; x\; \Then\; 1 \;\Else\; 0 \\
\OpCase{\Branch}{i}{r} &\mapsto& \OpCase{\Branch}{i}{r} &\mapsto&
\ba[t]{@{}l}\lambda s. \ba[t]{@{}l}\lambda s.
\ba[t]{@{}l} \ba[t]{@{}l}
\Case\; \dec{lookup}_n~i~s\; \{\\ \Case\; \dec{lookup}_n~i~s\; \{\\
\quad\ba[t]{@{~}l@{~}c@{~}l} \quad\ba[t]{@{~}l@{~}c@{~}l}
\Inl\, \Unit &\mapsto& \Inl\,\Unit &\mapsto&
\ba[t]{@{}l} \ba[t]{@{}l}
\Let\;x_\True \revto r~\True~(\dec{add}_n~\Record{i, \True}~s)\; \In\\ \Let\;x_\True \revto r~\True~(\dec{add}_n~\Record{i, \True}~s)\; \In\\
\Let\;x_\False \revto r~\False~(\dec{add}_n~\Record{i, \False}~s)\; \In\\ \Let\;x_\False \revto r~\False~(\dec{add}_n~\Record{i, \False}~s)\; \In\\
(x_\True + x_\False); \\ x_\True + x_\False; \\
\ea\\ \ea\\
\Inr\,x &\mapsto& r~x~s\; \} \\ \Inr~x &\mapsto& r~x~s\; \} \\
\ea \\ \ea \\
\ea \\ \ea \\
\ea \\ \ea \\
@@ -17147,7 +17147,7 @@ Similarly, we can use parameter-passing to support missing queries.
\[ \[
\bl \bl
\ECount''_n : ((\Nat_n \to \Bool) \to \Bool) \to \Nat\\ \ECount''_n : ((\Nat_n \to \Bool) \to \Bool) \to \Nat\\
\ECount''_n\,pred \defas \ECount''_n~pred \defas
\bl \bl
\Let\;h \revto \bl \Let\;h \revto \bl
\Handle\;pred\,(\lambda i. \Do\;\Branch~\Unit)\;\With\\ \Handle\;pred\,(\lambda i. \Do\;\Branch~\Unit)\;\With\\
@@ -17919,6 +17919,8 @@ fast with SML/NJ compared with MLton.
\tablethree \tablethree
\section{Related work}
\part{Conclusions} \part{Conclusions}
\label{p:conclusions} \label{p:conclusions}