Added Related Work section.

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The WAM~\cite{Warren83}
\section{State of the Art and Related Work} \label{sec:related}
%==============================================================
% Indexing in Prolog systems:
Even nowadays, some Prolog systems are still influenced by the WAM
design and only support indexing on the main functor symbol of the
first argument. Some others, like YAP~\cite{YAP}, can look inside
compound terms. SICStus Prolog supports \emph{shallow
backtracking}~\cite{ShallowBacktracking@ICLP-89}; choice points are
fully populated only when it is certain execution will enter the
clause body. While shallow backtracking avoids some of the performance
problems of unnecessary choice point creation, it does not offer the
full benefits that indexing can provide. Other systems like
BIM-Prolog~\cite{IndexingProlog@NACLP-89}, ilProlog,
SWI-Prolog~\cite{SWI}, and XSB~\cite{XSB} allow for user-controlled
multi-argument indexing (via an \code{:-~index} directive). Typically,
this support comes with various implementation restrictions. For
example, in SWI-Prolog at most four arguments can be indexed; in XSB
the compiler does not offer multi-argument indexing support and the
predicates need to be asserted instead; we know of no system where
multi-argument indexing looks inside compound terms. More importantly,
requiring users to specify arguments to index on is neither
user-friendly nor guarantees good performance results. Our thesis is
that it is much better if the abstract machine is able to
automatically adapt to the runtime indexing requirements of Prolog
applications.
% Trees, tries and unification factoring:
Recognizing the need for better indexing, researchers have proposed
more flexible index mechanisms for Prolog. For example, Hickey and
Mudambi proposed \emph{switching trees}~\cite{HickeyMudambi@JLP-89},
which rely on the presence of mode information. Similar proposals were
followed by Van Roy, Demoen and Willems who perform indexing on
several arguments to form a \emph{selection tree}~\cite{VRDW87}, and
by Zhou et al.\ who implemented a \emph{matching tree} oriented
abstract machine for Prolog~\cite{TOAM@ICLP-90}. For static
predicates, the XSB compiler offers support for \emph{unification
factoring}~\cite{UnifFact@POPL-95}; for asserted code, XSB can
represent databases of facts using \emph{tries}~\cite{Tries@JLP-99}
which provide left-to-right multi-argument indexing. However, none of
these mechanisms is used automatically; instead the user has to
specify appropriate directives.
% Comparison with static analysis techniques and Mercury:
Long ago, Kliger and Shapiro argued that such tree-based indexing
schemes are not cost effective for the compilation of Prolog
programs~\cite{KligerShapiro@ICLP-88}. We disagree with their
conclusion. On the other hand it is true that unless the modes of
predicates are known there is a risk of doing indexing on output
arguments, whose only effect will be an unnecessary increase in
compilation times and, more importantly, code size. In a programming
language like Mercury~\cite{Mercury@JLP-96} where modes are known the
compiler can of course avoid this risk; in Mercury modes are in fact
used to guide the compiler in generating indexing tables. However, the
situation is different for a language Prolog. Getting accurate
information about the set of all possible modes of predicates requires
a global static analyzer in the compiler --- and most Prolog systems
do not come with one --- but more importantly, it requires a lot of
discipline from the programmer (e.g., that applications use the module
system religiously and never bypass it). As a result, most Prolog
systems currently do not provide the type of indexing that
applications require. Even in systems like Ciao~\cite{Ciao@SCP-05},
which do come with built-in static analysis and more or less force
such a discipline to the programmer, mode information is not used for
multi-argument index construction.
\begin{itemize}
% \item Alternative: interface with a DB system?
\item Just-In-Time and dynamic compilation techniques (VITOR, IS THERE
ANYTHING FOR PROLOG?)
\end{itemize}
\section{Demand-Driven Indexing of Static Predicates} \label{sec:static}
%=======================================================================
For static predicates the compiler has complete information about all
@ -115,7 +187,7 @@ consist only of Datalog facts. This is commonly the case for all
extensional database predicates where indexing is most effective and
called for. One such code example is shown in
Fig.~\ref{fig:carc:facts}. It is a fragment of the well-known machine
learning dataset \textit{Carcinogenesis}~\cite{SriKinMugSte97-ILP97}.
learning dataset \textit{Carcinogenesis}~\cite{Carcinogenesis@ILP-97}.
These clauses get compiled to the WAM code shown in
Fig.~\ref{fig:carc:clauses}. Assuming WAM-style, first argument
indexing, the indexing code that a Prolog compiler generates is shown
@ -458,9 +530,9 @@ requires the following extensions:
for this argument (one for the variables and one per each group of
clauses). However, this issue and how to deal with it is well-known
by now. Possible solutions to it are described in a 1987 paper by
Carlsson~\cite{Carlsson} and can be readily adapted to \JITI.
Alternatively, in a simple implementation, we can skip \JITI for
arguments with variables in some clauses.
Carlsson~\cite{FreezeIndexing@ICLP-87} and can be readily adapted to
\JITI. Alternatively, in a simple implementation, we can skip \JITI
for arguments with variables in some clauses.
\end{enumerate}
Before describing \JITI more formally, we remark on the following
design decisions whose rationale may not be immediately obvious:
@ -593,7 +665,7 @@ to a \switchSTAR WAM instruction.
\end{itemize}
\end{itemize}
\item[2.2.4] transform the \jitiSTAR $r, l, k$ instruction to
a \switchSTAR $r, l, \cal T$ instruction; and
a \switchSTAR $r, l, \&{\cal T}$ instruction; and
\item[2.2.5] continue execution with this instruction.
\end{itemize}
\end{enumerate}
@ -629,7 +701,7 @@ Algorithm~\ref{alg:construction} provides multi-argument indexing but
only for the outermost symbols of arguments. For clauses with
structured terms that require indexing in their subterms we can either
employ a compile-time program transformation like \emph{unification
factoring}~\cite{Dawson:1995:UFE} or modify the algorithm to consider
factoring}~\cite{UnifFact@POPL-95} or modify the algorithm to consider
index positions inside structure symbols. This is relatively easy to
do but requires support from the register allocator (passing the
subterms of structures in appropriate argument registers) and/or a new
@ -666,44 +738,6 @@ If the indices are needed again, they can simply be regenerated.
%================================================
\section{Related Work} \label{sec:related}
%=========================================
Some Prolog systems are influenced by the WAM design and only support
indexing on the functor symbol of the first argument. Some others,
like YAP~\cite{costa88}, can look inside compound terms. SICStus
Prolog supports \emph{shallow backtracking}~\cite{Carls88}; choice
points are fully populated only when it is certain execution will
enter the clause body. Other systems like
BIM-Prolog~\cite{Bart89:NACLP}, SWI-Prolog~\cite{SWI}, and
hProlog~\cite{} that can do multi-argument indexing.
Hickey and Mudambi~\cite{HicMud} proposed \emph{switching trees}.
These trees assume mode information to organise the different clauses
in a tree. Similar proposals were followed by Van Roy, Demoen and
Willems~\cite{VRDW87} who perform indexing on several arguments to
form a {\it selection tree}, and, more recently, Zhou et al.\
introduced \emph{matching trees} in B-Prolog~\cite{ZhTaUs-small}.
Long ago, Kliger and Shapiro argued that such schemes are not cost
effective for the compilation of Prolog programs~\cite{KS88}. Firstly,
in many cases choice points are really necessary, and a sophisticated
indexing scheme will not help. Second, unless the mode declarations
are known, there is a risk of doing indexing on output arguments,
which will never be instantiated. Some of the advanced indexing
systems we discussed claim to overcome the last difficulty by using
global analysis, in the form of abstract interpretation, to provide
the modes of use for the program.
\begin{itemize}
\item Indexing in Prolog systems.
\item Trees and tries. Unification factoring.
\item Comparison with static analysis techniques and Mercury.
\item Alternative: interface with a DB system?
\item Just-In-Time and dynamic compilation techniques (VITOR, IS THERE
ANYTHING FOR PROLOG?)
\end{itemize}
\section{Concluding Remarks}
%===========================
\begin{itemize}