7a98e7df59
git-svn-id: https://yap.svn.sf.net/svnroot/yap/trunk@1905 b08c6af1-5177-4d33-ba66-4b1c6b8b522a
1395 lines
66 KiB
TeX
1395 lines
66 KiB
TeX
%==============================================================================
|
||
\documentclass{llncs}
|
||
%------------------------------------------------------------------------------
|
||
\usepackage[latin1]{inputenc}
|
||
\usepackage{float}
|
||
\usepackage{alltt}
|
||
\usepackage{xspace}
|
||
\usepackage{epsfig}
|
||
\usepackage{wrapfig}
|
||
\usepackage{subfigure}
|
||
|
||
\renewcommand{\rmdefault}{ptm}
|
||
%------------------------------------------------------------------------------
|
||
\newcommand{\Paragraph}[1]{\vspace*{-.5em}\paragraph{#1}}
|
||
%------------------------------------------------------------------------------
|
||
\floatstyle{ruled}
|
||
\newfloat{Algorithm}{ht}{lop}
|
||
%------------------------------------------------------------------------------
|
||
\newcommand{\wamcodesize}{scriptsize}
|
||
\newcommand{\code}[1]{\texttt{#1}}
|
||
\newcommand{\instr}[1]{\textsf{#1}}
|
||
\newcommand{\try}{\instr{try}\xspace}
|
||
\newcommand{\retry}{\mbox{\instr{retry}}\xspace}
|
||
\newcommand{\trust}{\instr{trust}\xspace}
|
||
\newcommand{\TryRetryTrust}{\mbox{\instr{try-retry-trust}}\xspace}
|
||
\newcommand{\fail}{\instr{fail}\xspace}
|
||
\newcommand{\jump}{\instr{jump}\xspace}
|
||
\newcommand{\jitiSTAR}{\mbox{\instr{dindex\_on\_*}}\xspace}
|
||
\newcommand{\switchSTAR}{\mbox{\instr{switch\_on\_*}}\xspace}
|
||
\newcommand{\jitiONterm}{\mbox{\instr{dindex\_on\_term}}\xspace}
|
||
\newcommand{\jitiONconstant}{\mbox{\instr{dindex\_on\_constant}}\xspace}
|
||
\newcommand{\jitiONstructure}{\mbox{\instr{dindex\_on\_structure}}\xspace}
|
||
\newcommand{\switchONterm}{\mbox{\instr{switch\_on\_term}}\xspace}
|
||
\newcommand{\switchONconstant}{\mbox{\instr{switch\_on\_constant}}\xspace}
|
||
\newcommand{\switchONstructure}{\mbox{\instr{switch\_on\_structure}}\xspace}
|
||
\newcommand{\getcon}{\mbox{\instr{get\_constant}}\xspace}
|
||
\newcommand{\proceed}{\instr{proceed}\xspace}
|
||
\newcommand{\Cline}{\cline{2-3}}
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||
\newcommand{\JITI}{demand-driven indexing\xspace}
|
||
%------------------------------------------------------------------------------
|
||
\newcommand{\bench}[1]{\textbf{\textsf{#1}}}
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||
\newcommand{\tcLio}{\bench{tc\_l\_io}\xspace}
|
||
\newcommand{\tcRio}{\bench{tc\_r\_io}\xspace}
|
||
\newcommand{\tcDio}{\bench{tc\_d\_io}\xspace}
|
||
\newcommand{\tcLoo}{\bench{tc\_l\_oo}\xspace}
|
||
\newcommand{\tcRoo}{\bench{tc\_r\_oo}\xspace}
|
||
\newcommand{\tcDoo}{\bench{tc\_d\_oo}\xspace}
|
||
\newcommand{\compress}{\bench{compress}\xspace}
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||
\newcommand{\sgCyl}{\bench{sg\_cyl}\xspace}
|
||
\newcommand{\muta}{\bench{muta}\xspace}
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||
\newcommand{\pta}{\bench{pta}\xspace}
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||
\newcommand{\tea}{\bench{tea}\xspace}
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||
%------------------------------------------------------------------------------
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||
\newcommand{\BreastCancer}{\bench{BreastCancer}\xspace}
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||
\newcommand{\Carcino}{\bench{Carcinogenesis}\xspace}
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||
\newcommand{\Choline}{\bench{Choline}\xspace}
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||
\newcommand{\GeneExpr}{\bench{GeneExpression}\xspace}
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||
\newcommand{\IEProtein}{\bench{IE-Protein\_Extraction}\xspace}
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||
%\newcommand{\Krki}{\bench{Krki}\xspace}
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||
%\newcommand{\KrkiII}{\bench{Krki~II}\xspace}
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||
\newcommand{\Mesh}{\bench{Mesh}\xspace}
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||
\newcommand{\Pyrimidines}{\bench{Pyrimidines}\xspace}
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||
\newcommand{\Susi}{\bench{Susi}\xspace}
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||
\newcommand{\Thermolysin}{\bench{Thermolysin}\xspace}
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||
%------------------------------------------------------------------------------
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||
\newenvironment{SmallProg}{\begin{tt}\begin{small}\begin{tabular}[b]{l}}{\end{tabular}\end{small}\end{tt}}
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\newenvironment{ScriptProg}{\begin{tt}\begin{scriptsize}\begin{tabular}[b]{l}}{\end{tabular}\end{scriptsize}\end{tt}}
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||
\newenvironment{FootProg}{\begin{tt}\begin{footnotesize}\begin{tabular}[c]{l}}{\end{tabular}\end{footnotesize}\end{tt}}
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||
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||
\newcommand{\TODOcomment}[2]{%
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||
\stepcounter{TODOcounter#1}%
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||
{\scriptsize\bf$^{(\arabic{TODOcounter#1})}$}%
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\marginpar[\fbox{
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||
\parbox{2cm}{\raggedleft
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||
\scriptsize$^{({\bf{\arabic{TODOcounter#1}{#1}}})}$%
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||
\scriptsize #2}}]%
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||
{\fbox{\parbox{2cm}{\raggedright
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||
\scriptsize$^{({\bf{\arabic{TODOcounter#1}{#1}}})}$%
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||
\scriptsize #2}}}
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||
}%
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\newcounter{TODOcounter}
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\newcommand{\TODO}[1]{\TODOcomment{}{#1}}
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%------------------------------------------------------------------------------
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\title{Demand-Driven Indexing of Prolog Clauses\thanks{Dedicated to
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the memory of our friend, colleague and co-author Ricardo Lopes.
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We miss you!}}
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\titlerunning{Demand-Driven Indexing of Prolog Clauses}
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\author{V\'{\i}tor Santos Costa\inst{1} \and Konstantinos
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Sagonas\inst{2} \and Ricardo Lopes}
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\authorrunning{V. Santos Costa, K. Sagonas and R. Lopes}
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||
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\institute{
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LIACC- DCC/FCUP, University of Porto, Portugal
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||
\and
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National Technical University of Athens, Greece
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||
}
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||
|
||
\begin{document}
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||
\maketitle
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||
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||
\begin{abstract}
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||
As logic programming applications grow in size, Prolog systems need
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to efficiently access larger and larger data sets and the need for
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any- and multi-argument indexing becomes more and more profound.
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||
Static generation of multi-argument indexing is one alternative, but
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||
applications often rely on features that are inherently dynamic
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||
which makes static techniques inapplicable or inaccurate. Another
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alternative is to employ dynamic schemes for flexible demand-driven
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indexing of Prolog clauses. We propose such schemes and discuss
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||
issues that need to be addressed for their efficient implementation
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in the context of WAM-based Prolog systems. We have implemented
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demand-driven indexing in two different Prolog systems and have been
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||
able to obtain non-negligible performance speedups: from a few
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||
percent up to orders of magnitude. Given these results, we see very
|
||
little reason for Prolog systems not to incorporate some form of
|
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dynamic indexing based on actual demand. In fact, we see
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||
demand-driven indexing as only the first step towards effective
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runtime optimization of Prolog programs.
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||
\end{abstract}
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||
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||
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\section{Introduction}
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||
%=====================
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||
The WAM~\cite{Warren83} has mostly been a blessing but occasionally
|
||
also a curse for Prolog systems. Its ingenious design has allowed
|
||
implementors to get byte code compilers with decent performance --- it
|
||
is not a fluke that most Prolog systems are still based on the WAM. On
|
||
the other hand, \emph{because} the WAM gives good performance in many
|
||
cases, implementors have not incorporated in their systems many
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||
features that drastically depart from WAM's basic characteristics.
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%
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||
For example, first argument indexing is sufficient for many Prolog
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applications. However, it is clearly sub-optimal for applications
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||
accessing large data sets; for a long time now, the database community
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||
has recognized that good indexing is the basis for fast query
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||
processing.
|
||
|
||
As logic programming applications grow in size, Prolog systems need to
|
||
efficiently access larger and larger data sets and the need for any-
|
||
and multi-argument indexing becomes more and more profound. Static
|
||
generation of multi-argument indexing is one alternative. The problem
|
||
is that this alternative is often unattractive because it may
|
||
drastically increase the size of the generated byte code and do so
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unnecessarily. Static analysis can partly address this concern, but in
|
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applications that rely on features which are inherently dynamic (e.g.,
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generating hypotheses for inductive logic programming data sets during
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runtime) static analysis is inapplicable or grossly inaccurate.
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Another alternative, which has not been investigated so far, is to do
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flexible indexing on demand during program execution.
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This is precisely what we advocate with this paper. More specifically,
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we present a small extension to the WAM that allows for flexible
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indexing of Prolog clauses during runtime based on actual demand. For
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static predicates, the scheme we propose is partly guided by the
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compiler; for dynamic code, besides being demand-driven by queries,
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the method needs to cater for code updates during runtime. Where our
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schemes radically depart from current practice is that they generate
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new byte code during runtime, in effect doing a form of just-in-time
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compilation. In our experience these schemes pay off. We have
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implemented \JITI in two different Prolog systems (YAP and XXX) and
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have obtained non-trivial speedups, ranging from a few percent to
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orders of magnitude, across a wide range of applications. Given these
|
||
results, we see very little reason for Prolog systems not to
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incorporate some form of indexing based on actual demand from queries.
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In fact, we see \JITI as only the first step towards effective runtime
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optimization of Prolog programs.
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\Paragraph{Organization.}
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%------------------------
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After commenting on the state of the art and related work concerning
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indexing in Prolog systems (Sect.~\ref{sec:related}) we briefly review
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indexing in the WAM (Sect.~\ref{sec:prelims}). We then present \JITI
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schemes for static (Sect.~\ref{sec:static}) and dynamic
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||
(Sect.~\ref{sec:dynamic}) predicates, their implementation in two
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Prolog systems (Sect.~\ref{sec:impl}) and the performance benefits
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they bring (Sect.~\ref{sec:perf}). The paper ends with some concluding
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||
remarks.
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\section{State of the Art and Related Work} \label{sec:related}
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%==============================================================
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% Indexing in Prolog systems:
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Many Prolog systems still only support
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indexing on the main functor symbol of the first argument. Some
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others, such as YAP version 4, can look inside some compound
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terms~\cite{YAP}. SICStus Prolog supports \emph{shallow
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backtracking}~\cite{ShallowBacktracking@ICLP-89}; choice points are
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fully populated only when it is certain that execution will enter the
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clause body. While shallow backtracking avoids some of the performance
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problems of unnecessary choice point creation, it does not offer the
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full benefits that indexing can provide. Other systems such as
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BIM-Prolog~\cite{IndexingProlog@NACLP-89}, SWI-Prolog~\cite{SWI} and
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XSB~\cite{XSB} allow for user-controlled multi-argument indexing.
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Notably, ilProlog~\cite{ilProlog} uses compile-time heuristics and
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generates code for multi-argument indexing automatically. In all these
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systems, this support comes with various implementation restrictions.
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For example, in SWI-Prolog at most four arguments can be indexed; in
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XSB the compiler does not offer multi-argument indexing and the
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predicates need to be asserted instead; we know of no system where
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multi-argument indexing looks inside compound terms. More importantly,
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requiring users to specify arguments to index on is neither
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user-friendly nor guarantees good performance results.
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||
% Trees, tries and unification factoring:
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||
Recognizing the need for better indexing, researchers have proposed
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more flexible indexing mechanisms for Prolog. For example, Hickey and
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Mudambi proposed \emph{switching trees}~\cite{HickeyMudambi@JLP-89},
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||
which rely on the presence of mode information. Similar proposals were
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||
put forward by Van Roy, Demoen and Willems who investigated indexing
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||
on several arguments in the form of a \emph{selection tree}~\cite{VRDW87}
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||
and by Zhou et al.\ who implemented a \emph{matching tree} oriented
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||
abstract machine for Prolog~\cite{TOAM@ICLP-90}. For static
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||
predicates, the XSB compiler offers support for \emph{unification
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||
factoring}~\cite{UnifFact@POPL-95}; for asserted code, XSB can
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||
represent databases of facts using \emph{tries}~\cite{Tries@JLP-99}
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||
which provide left-to-right multi-argument indexing. However, in XSB
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||
none of these mechanisms is used automatically; instead the user has
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to specify appropriate directives.
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||
% Comparison with static analysis techniques and Mercury:
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Long ago, Kliger and Shapiro argued that such tree-based indexing
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schemes are not cost effective for the compilation of Prolog
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programs~\cite{KligerShapiro@ICLP-88}. Some of their arguments make
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sense for certain applications, but, as we shall show, in general
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they underestimate the benefits of indexing on EDB predicates.
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Nevertheless, it is true that unless the modes of
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predicates are known we run the risk of doing indexing on output
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arguments, whose only effect is an unnecessary increase in compilation
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times and, more importantly, in code size. In a programming language
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such as Mercury~\cite{Mercury@JLP-96} where modes are known the compiler
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can of course avoid this risk; indeed in Mercury modes (and types) are
|
||
used to guide the compiler generate good indexing tables. However, the
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||
situation is different for a language like Prolog. Getting accurate
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||
information about the set of all possible modes of predicates requires
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||
a global static analyzer in the compiler --- and most Prolog systems
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||
do not come with one. More importantly, it requires a lot of
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||
discipline from the programmer (e.g., that applications use the module
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||
system religiously and never bypass it). As a result, most Prolog
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||
systems currently do not provide the type of indexing that
|
||
applications require. Even in systems such as Ciao~\cite{Ciao@SCP-05},
|
||
which do come with a built-in static analyzer and more or less force
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such a discipline on the programmer, mode information is not used for
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multi-argument indexing.
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% The grand finale:
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||
The situation is actually worse for certain types of Prolog
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applications. For example, consider applications in the area of
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inductive logic programming. These applications on the one hand have
|
||
high demands for effective indexing since they need to efficiently
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access big datasets and on the other they are unfit for static
|
||
analysis since queries are often ad hoc and generated only during
|
||
runtime as new hypotheses are formed or refined.
|
||
%
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||
Our thesis is that the abstract machine should be able to adapt
|
||
automatically to the runtime requirements of such or, even better, of
|
||
all applications by employing increasingly aggressive forms of dynamic
|
||
compilation. As a concrete example of what this means in practice, in
|
||
this paper we will attack the problem of satisfying the indexing needs
|
||
of applications during runtime. Naturally, we will base our technique
|
||
on the existing support for indexing that the WAM provides, but we
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will extend this support with the technique of \JITI that we describe
|
||
in the next sections.
|
||
|
||
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\section{Indexing in the WAM} \label{sec:prelims}
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||
%================================================
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To make the paper relatively self-contained we review the indexing
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||
instructions of the WAM and their use. In the WAM, the first level of
|
||
dispatching involves a test on the type of the argument. The
|
||
\switchONterm instruction checks the tag of the dereferenced value in
|
||
the first argument register and implements a four-way branch where one
|
||
branch is for the dereferenced register being an unbound variable, one
|
||
for being atomic, one for (non-empty) list, and one for structure. In
|
||
any case, control goes to a bucket of clauses. In the buckets for
|
||
constants and structures the second level of dispatching involves the
|
||
value of the register. The \switchONconstant and \switchONstructure
|
||
instructions implement this dispatching: typically with a \fail
|
||
instruction when the bucket is empty, with a \jump instruction for
|
||
only one clause, with a sequential scan when the number of clauses is
|
||
small, and with a hash table lookup when the number of clauses exceeds
|
||
a threshold. For this reason the \switchONconstant and
|
||
\switchONstructure instructions take as arguments the hash table
|
||
\instr{T} and the number of clauses \instr{N} the table contains. In
|
||
each bucket of this hash table and also in the bucket for the variable
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||
case of \switchONterm the code sequentially backtracks through the
|
||
clauses using a \TryRetryTrust chain of instructions. The \try
|
||
instruction sets up a choice point, the \retry instructions (if~any)
|
||
update certain fields of this choice point, and the \trust instruction
|
||
removes it.
|
||
|
||
The WAM has additional indexing instructions (\instr{try\_me\_else}
|
||
and friends) that allow indexing to be interspersed with the code of
|
||
clauses. We will not consider them here. This is not a problem since
|
||
the above scheme handles all programs. Also, we will feel free to do
|
||
some minor modifications and optimizations when this simplifies
|
||
things.
|
||
|
||
Let's see an example. Consider the Prolog code shown in
|
||
Fig.~\ref{fig:carc:facts}, a fragment of the machine learning dataset
|
||
\textit{Carcinogenesis}.
|
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%
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||
These clauses get compiled to the WAM code shown in
|
||
Fig.~\ref{fig:carc:clauses}. The first argument indexing code that a
|
||
Prolog compiler generates is shown in Fig.~\ref{fig:carc:index}. This
|
||
code is typically placed before the code for the clauses and the
|
||
\switchONconstant is the entry point of the predicate. Note that compared
|
||
with vanilla WAM this instruction has an extra argument: the register
|
||
on the value of which we index ($r_1$). This extra argument will allow
|
||
us to go beyond first argument indexing. Another departure from the
|
||
WAM is that if this argument register contains an unbound variable
|
||
instead of a constant then execution will continue with the next
|
||
instruction; in effect we have merged part of the functionality of
|
||
\switchONterm into the \switchONconstant instruction. This small
|
||
change in the behavior of \switchONconstant will allow us to get
|
||
\JITI. Let's see how.
|
||
|
||
%------------------------------------------------------------------------------
|
||
\begin{figure}[t]
|
||
\centering
|
||
\begin{tabular}[b]{c}
|
||
\subfigure[Some Prolog clauses\label{fig:carc:facts}]{%
|
||
\begin{ScriptProg}
|
||
has\_property(d1,salmonella,p).\\
|
||
has\_property(d1,salmonella\_n,p).\\
|
||
has\_property(d2,salmonella,p). \\
|
||
has\_property(d2,cytogen\_ca,n).\\
|
||
has\_property(d3,cytogen\_ca,p).\\[5pt]
|
||
\end{ScriptProg}
|
||
}\\ \hline\hline%
|
||
\subfigure[WAM indexing\label{fig:carc:index}]{%
|
||
\begin{sf}
|
||
\begin{\wamcodesize}
|
||
\begin{tabular}[b]{l}
|
||
\switchONconstant $r_1$ 5 $T_1$ \\
|
||
\try $L_1$ \\
|
||
\retry $L_2$ \\
|
||
\retry $L_3$ \\
|
||
\retry $L_4$ \\
|
||
\trust $L_5$ \\
|
||
\\
|
||
\begin{tabular}[b]{r|c@{\ }|l|}
|
||
\Cline
|
||
$T_1$: & \multicolumn{2}{c|}{Hash Table Info}\\ \Cline\Cline
|
||
\ & d1 & \try $L_1$ \\
|
||
\ & & \trust $L_2$ \\ \Cline
|
||
\ & d2 & \try $L_3$ \\
|
||
\ & & \trust $L_4$ \\ \Cline
|
||
\ & d3 & \jump $L_5$ \\
|
||
\Cline
|
||
\end{tabular}\\[3pt]
|
||
\end{tabular}
|
||
\end{\wamcodesize}
|
||
\end{sf}
|
||
}%
|
||
\end{tabular}%
|
||
\subfigure[Code for the clauses\label{fig:carc:clauses}]{%
|
||
\begin{sf}
|
||
\begin{\wamcodesize}
|
||
\begin{tabular}[b]{rl}
|
||
$L_1$: & \getcon $r_1$ d1 \\
|
||
\ & \getcon $r_2$ salmonella \\
|
||
\ & \getcon $r_3$ p \\
|
||
\ & \proceed \\
|
||
$L_2$: & \getcon $r_1$ d1 \\
|
||
\ & \getcon $r_2$ salmonella\_n \\
|
||
\ & \getcon $r_3$ p \\
|
||
\ & \proceed \\
|
||
$L_3$: & \getcon $r_1$ d2 \\
|
||
\ & \getcon $r_2$ salmonella \\
|
||
\ & \getcon $r_3$ p \\
|
||
\ & \proceed \\
|
||
$L_4$: & \getcon $r_1$ d2 \\
|
||
\ & \getcon $r_2$ cytogen\_ca \\
|
||
\ & \getcon $r_3$ n \\
|
||
\ & \proceed \\
|
||
$L_5$: & \getcon $r_1$ d3 \\
|
||
\ & \getcon $r_2$ cytogen\_ca \\
|
||
\ & \getcon $r_3$ p \\
|
||
\ & \proceed
|
||
\end{tabular}
|
||
\end{\wamcodesize}
|
||
\end{sf}
|
||
}%
|
||
\subfigure[Any arg indexing\label{fig:carc:jiti_single:before}]{%
|
||
\begin{sf}
|
||
\begin{\wamcodesize}
|
||
\begin{tabular}[b]{l}
|
||
\switchONconstant $r_1$ 5 $T_1$ \\
|
||
\jitiONconstant $r_2$ 5 3 \\
|
||
\jitiONconstant $r_3$ 5 3 \\
|
||
\try $L_1$ \\
|
||
\retry $L_2$ \\
|
||
\retry $L_3$ \\
|
||
\retry $L_4$ \\
|
||
\trust $L_5$ \\
|
||
\\
|
||
\begin{tabular}[b]{r|c@{\ }|l|}
|
||
\Cline
|
||
$T_1$: & \multicolumn{2}{c|}{Hash Table Info}\\ \Cline\Cline
|
||
\ & \code{d1} & \try $L_1$ \\
|
||
\ & & \trust $L_2$ \\ \Cline
|
||
\ & \code{d2} & \try $L_3$ \\
|
||
\ & & \trust $L_4$ \\ \Cline
|
||
\ & \code{d3} & \jump $L_5$ \\
|
||
\Cline
|
||
\end{tabular}
|
||
\end{tabular}
|
||
\end{\wamcodesize}
|
||
\end{sf}
|
||
}%
|
||
\caption{Part of the Carcinogenesis dataset and WAM code that a byte
|
||
code compiler generates}
|
||
\label{fig:carc}
|
||
\vspace*{-1em}
|
||
\end{figure}
|
||
%------------------------------------------------------------------------------
|
||
|
||
|
||
\section{Demand-Driven Indexing of Static Predicates} \label{sec:static}
|
||
%=======================================================================
|
||
For static predicates the compiler has complete information about all
|
||
clauses and shapes of their head arguments. It is both desirable and
|
||
possible to take advantage of this information at compile time and so
|
||
we treat the case of static predicates separately.
|
||
%
|
||
We will do so with schemes of increasing effectiveness and
|
||
implementation complexity.
|
||
|
||
\subsection{A simple WAM extension for any argument indexing}
|
||
%------------------------------------------------------------
|
||
Let us initially consider the case where the predicates to index
|
||
consist only of Datalog facts. This is commonly the case for all
|
||
extensional database predicates where indexing is most effective and
|
||
called for.
|
||
|
||
Refer to the example in Fig.~\ref{fig:carc}.
|
||
%
|
||
The indexing code of Fig.~\ref{fig:carc:index} incurs a small cost for
|
||
a call where the first argument is a variable (namely, executing the
|
||
\switchONconstant instruction) but the instruction pays off for calls
|
||
where the first argument is bound. On the other hand, for calls where
|
||
the first argument is a free variable and some other argument is
|
||
bound, a choice point will be created, the \TryRetryTrust chain will
|
||
be used, and execution will go through the code of all clauses. This
|
||
is clearly inefficient, more so for larger data sets.
|
||
%
|
||
We can do much better with the relatively simple scheme shown in
|
||
Fig.~\ref{fig:carc:jiti_single:before}. Immediately after the
|
||
\switchONconstant instruction, we can statically generate
|
||
\jitiONconstant (demand indexing) instructions, one for each remaining
|
||
argument. Recall that the entry point of the predicate is the
|
||
\switchONconstant instruction. The \jitiONconstant $r_i$ \instr{N A}
|
||
instruction works as follows:
|
||
\begin{itemize}
|
||
\item if the argument $r_i$ is a free variable,
|
||
execution continues with the next instruction;
|
||
\item otherwise, \JITI kicks in as follows. The abstract machine
|
||
scans the WAM code of the clauses and creates an index table for the
|
||
values of the corresponding argument. It can do so because the
|
||
instruction takes as arguments the number of clauses \instr{N} to
|
||
index and the arity \instr{A} of the predicate. (In our example, the
|
||
numbers 5 and 3.) For Datalog facts, this information is sufficient.
|
||
Because the WAM byte code for the clauses has a very regular
|
||
structure, the index table can be created very quickly. Upon its
|
||
creation, the \jitiONconstant instruction gets transformed to a
|
||
\switchONconstant. Again this is straightforward because of the two
|
||
instructions have similar layouts in memory. Execution of the
|
||
abstract machine then continues with the \switchONconstant
|
||
instruction.
|
||
\end{itemize}
|
||
Figure~\ref{fig:carg:jiti_single:after} shows the index table $T_2$
|
||
which is created for our example and how the indexing code looks after
|
||
the execution of a call with mode \code{(out,in,?)}. Note that the
|
||
\jitiONconstant instruction for argument register $r_2$ has been
|
||
appropriately patched. The call that triggered \JITI and subsequent
|
||
calls of the same mode will use table $T_2$. The index for the second
|
||
argument has been created.
|
||
%------------------------------------------------------------------------------
|
||
\begin{figure}[t]
|
||
\centering
|
||
\begin{sf}
|
||
\begin{\wamcodesize}
|
||
\begin{tabular}{c@{\hspace*{2em}}c@{\hspace*{2em}}c}
|
||
\begin{tabular}{l}
|
||
\switchONconstant $r_1$ 5 $T_1$ \\
|
||
\switchONconstant $r_2$ 5 $T_2$ \\
|
||
\jitiONconstant $r_3$ 5 3 \\
|
||
\try $L_1$ \\
|
||
\retry $L_2$ \\
|
||
\retry $L_3$ \\
|
||
\retry $L_4$ \\
|
||
\trust $L_5$ \\
|
||
\end{tabular}
|
||
&
|
||
\begin{tabular}{r|c@{\ }|l|}
|
||
\Cline
|
||
$T_1$: & \multicolumn{2}{c|}{Hash Table Info}\\ \Cline\Cline
|
||
\ & \code{d1} & \try $L_1$ \\
|
||
\ & & \trust $L_2$ \\ \Cline
|
||
\ & \code{d2} & \try $L_3$ \\
|
||
\ & & \trust $L_4$ \\ \Cline
|
||
\ & \code{d3} & \jump $L_5$ \\
|
||
\Cline
|
||
\end{tabular}
|
||
&
|
||
\begin{tabular}{r|c@{\ }|l|}
|
||
\Cline
|
||
$T_2$: & \multicolumn{2}{|c|}{Hash Table Info}\\ \Cline\Cline
|
||
\ & \code{salmonella} & \try $L_1$ \\
|
||
\ & & \trust $L_3$ \\ \Cline
|
||
\ & \code{salmonella\_n} & \jump $L_2$ \\ \Cline
|
||
\ & \code{cytrogen\_ca} & \try $L_4$ \\
|
||
\ & & \trust $L_5$ \\
|
||
\Cline
|
||
\end{tabular}
|
||
\end{tabular}
|
||
\end{\wamcodesize}
|
||
\end{sf}
|
||
\caption{WAM code after demand-driven indexing for argument 2;
|
||
$T_2$ is generated dynamically}
|
||
\label{fig:carg:jiti_single:after}
|
||
\end{figure}
|
||
%------------------------------------------------------------------------------
|
||
|
||
The main advantage of this scheme is its simplicity. The compiled code
|
||
(Fig.~\ref{fig:carc:jiti_single:before}) is not significantly bigger
|
||
than the code which a WAM-based compiler would generate
|
||
(Fig.~\ref{fig:carc:index}) and, if \JITI turns out unnecessary
|
||
during runtime (e.g. execution encounters only open calls or with only
|
||
the first argument bound), the extra overhead is minimal: the
|
||
execution of some \jitiONconstant instructions for the open call only.
|
||
%
|
||
In short, this is a simple scheme that allows for indexing on \emph{any
|
||
single} argument. At least for big sets of Datalog facts, we see
|
||
little reason not to use it.
|
||
|
||
\Paragraph{Optimizations.}
|
||
Because we are dealing with static code, there are opportunities for
|
||
some easy optimizations. Suppose we statically determine that there
|
||
will never be any calls with \code{in} mode for some arguments or that
|
||
these arguments are not discriminating enough.\footnote{In our
|
||
example, suppose the third argument of \code{has\_property/3} was the
|
||
atom \code{p} throughout.} Then we can avoid generating
|
||
\jitiONconstant instructions for them. Also, suppose we know that some
|
||
arguments are most likely than others to be used in the \code{in}
|
||
mode. Then we can simply place the \jitiONconstant instructions for
|
||
them before the instructions for other arguments. This is possible
|
||
since all indexing instructions take the argument register number as
|
||
an argument; their order does not matter.
|
||
|
||
\subsection{From any argument indexing to multi-argument indexing}
|
||
%-----------------------------------------------------------------
|
||
The scheme of the previous section gives us only single argument
|
||
indexing. However, all the infrastructure we need is already in place.
|
||
We can use it to obtain any fixed-order multi-argument \JITI in a
|
||
straightforward way.
|
||
|
||
Note that the compiler knows exactly the set of clauses that need to
|
||
be tried for each query with a specific symbol in the first argument.
|
||
% This information is needed in order to construct, at compile time, the
|
||
% hash table $T_1$ of Fig.~\ref{fig:carc:index}.
|
||
For multi-argument \JITI, instead of generating for each hash bucket
|
||
only \TryRetryTrust instructions, the compiler can prepend appropriate
|
||
demand indexing instructions. We illustrate this on our running
|
||
example. The table $T_1$ contains four \jitiONconstant instructions:
|
||
two for each of the remaining two arguments of hash buckets with more
|
||
than one alternative. For hash buckets with none or only one
|
||
alternative (e.g., for \code{d3}'s bucket) there is obviously no need
|
||
to resort to \JITI for the remaining arguments.
|
||
Figure~\ref{fig:carc:jiti_multi} shows the state of the hash tables
|
||
after the execution of queries \code{has\_property(C,salmonella,T)},
|
||
which creates $T_2$, and \code{has\_property(d2,P,n)} which creates
|
||
the $T_3$ table and transforms the \jitiONconstant instruction for
|
||
\code{d2} and register $r_3$ to the appropriate \switchONconstant
|
||
instruction.
|
||
|
||
%------------------------------------------------------------------------------
|
||
\begin{figure}[t]
|
||
\centering
|
||
\begin{sf}
|
||
\begin{\wamcodesize}
|
||
\begin{tabular}{@{}c@{}c@{}c@{}}
|
||
\begin{tabular}{l}
|
||
\switchONconstant $r_1$ 5 $T_1$ \\
|
||
\switchONconstant $r_2$ 5 $T_2$ \\
|
||
\jitiONconstant $r_3$ 5 3 \\
|
||
\try $L_1$ \\
|
||
\retry $L_2$ \\
|
||
\retry $L_3$ \\
|
||
\retry $L_4$ \\
|
||
\trust $L_5$ \\
|
||
\end{tabular}
|
||
&
|
||
\begin{tabular}{r|c@{\ }|l|}
|
||
\Cline
|
||
$T_1$: & \multicolumn{2}{c|}{Hash Table Info}\\ \Cline\Cline
|
||
\ & \code{d1} & \jitiONconstant $r_2$ 2 3 \\
|
||
\ & & \jitiONconstant $r_3$ 2 3 \\
|
||
\ & & \try $L_1$ \\
|
||
\ & & \trust $L_2$ \\ \Cline
|
||
\ & \code{d2} & \jitiONconstant $r_2$ 2 3 \\
|
||
\ & & \switchONconstant $r_3$ 2 $T_3$ \\
|
||
\ & & \try $L_3$ \\
|
||
\ & & \trust $L_4$ \\ \Cline
|
||
\ & \code{d3} & \jump $L_5$ \\
|
||
\Cline
|
||
\end{tabular}
|
||
&
|
||
\begin{tabular}{c}
|
||
\begin{tabular}{r|c@{\ }|l|}
|
||
\Cline
|
||
$T_2$: & \multicolumn{2}{|c|}{Hash Table Info}\\ \Cline\Cline
|
||
\ & \code{salmonella} & \jitiONconstant $r_3$ 2 3 \\
|
||
\ & & \try $L_1$ \\
|
||
\ & & \trust $L_3$ \\ \Cline
|
||
\ & \code{salmonella\_n} & \jump $L_2$ \\ \Cline
|
||
\ & \code{cytrogen\_ca} & \jitiONconstant $r_3$ 2 3 \\
|
||
\ & & \try $L_4$ \\
|
||
\ & & \trust $L_5$ \\
|
||
\Cline
|
||
\end{tabular}
|
||
\\
|
||
\ \\
|
||
\begin{tabular}{r|c@{\ }|l|}
|
||
\Cline
|
||
$T_3$: & \multicolumn{2}{|c|}{Hash Table Info}\\ \Cline\Cline
|
||
\ & \code{p} & \jump $L_3$ \\ \Cline
|
||
\ & \code{n} & \jump $L_4$ \\
|
||
\Cline
|
||
\end{tabular}
|
||
\end{tabular}
|
||
\end{tabular}
|
||
\end{\wamcodesize}
|
||
\end{sf}
|
||
\caption{\JITI for all arguments;
|
||
$T_1$ is static; $T_2$ and $T_3$ are created dynamically}
|
||
\label{fig:carc:jiti_multi}
|
||
\end{figure}
|
||
%------------------------------------------------------------------------------
|
||
|
||
\Paragraph{Implementation issues.}
|
||
In the \jitiONconstant instructions of Fig.~\ref{fig:carc:jiti_multi}
|
||
notice the integer 2 which denotes the number of clauses that the
|
||
instruction will index. Using this number an index table of
|
||
appropriate size will be created, such as $T_3$. To fill this table we
|
||
need information about the clauses to index and the symbols to hash
|
||
on. The clauses can be obtained by scanning the labels of the
|
||
\TryRetryTrust instructions following \jitiONconstant; the symbols by
|
||
looking at appropriate byte code offsets (based on the argument
|
||
register number) from these labels. In our running example, the
|
||
symbols can be obtained by looking at the second argument of the
|
||
\getcon instruction whose argument register is $r_2$. In the loaded
|
||
bytecode, assuming the argument register is represented in one byte,
|
||
these symbols are found $sizeof(\getcon) + sizeof(opcode) + 1$ bytes
|
||
away from the clause label; see Fig.~\ref{fig:carc:clauses}. Thus,
|
||
multi-argument \JITI is easy to get and the creation of index tables
|
||
can be extremely fast when indexing Datalog facts.
|
||
|
||
\subsection{Beyond Datalog and other implementation issues}
|
||
%----------------------------------------------------------
|
||
Indexing on demand clauses with function symbols is not significantly
|
||
more difficult. The scheme we have described is applicable but
|
||
requires the following extensions:
|
||
\begin{enumerate}
|
||
\item Besides \jitiONconstant we also need \jitiONterm and
|
||
\jitiONstructure instructions. These are the \JITI counterparts of
|
||
the WAM's \switchONterm and \switchONstructure.
|
||
\item Because the byte code for the clause heads does not necessarily
|
||
have a regular structure, the abstract machine needs to be able to
|
||
``walk'' the byte code instructions and recover the symbols on which
|
||
indexing will be based. Writing such a code walking procedure is not
|
||
hard.
|
||
\item Indexing on a position that contains unconstrained variables
|
||
for some clauses is tricky. The WAM needs to group clauses in this
|
||
case and without special treatment creates two choice points 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 paper by
|
||
Carlsson~\cite{FreezeIndexing@ICLP-87} and can be readily adapted to
|
||
\JITI. Alternatively, in a simple implementation, we can skip \JITI
|
||
for positions 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:
|
||
\begin{itemize}
|
||
\item By default, only table $T_1$ is generated at compile time (as in
|
||
the WAM) and the additional index tables $T_2, T_3, \ldots$ are
|
||
generated dynamically. This is because we do not want to increase
|
||
compiled code size unnecessarily (i.e., when there is no demand for
|
||
these indices).
|
||
\item On the other hand, we generate \jitiSTAR instructions at compile
|
||
time for the head arguments.\footnote{The \jitiSTAR instructions for
|
||
$T_1$ can be generated either by the compiler or the loader.} This
|
||
does not noticeably increase the generated byte code but it greatly
|
||
simplifies code loading. Notice that a nice property of the scheme
|
||
we have described is that the loaded byte code can be patched
|
||
\emph{without} the need to move any instructions.
|
||
% The indexing tables are typically not intersperced with the byte code.
|
||
\item Finally, one may wonder why the \jitiSTAR instructions create
|
||
the dynamic index tables with an additional code walking pass
|
||
instead of piggy-backing on the pass which examines all clauses via
|
||
the main \TryRetryTrust chain. Main reasons are: 1) in many cases
|
||
the code walking can be selective and guided by offsets and 2) by
|
||
first creating the index table and then using it we speed up the
|
||
execution of the queries and often avoid unnecessary choice point
|
||
creations.
|
||
\end{itemize}
|
||
Note that all these decisions are orthogonal to the main idea and are
|
||
under compiler control. For example, if analysis determines that some
|
||
argument sequences will never demand indexing we can simply avoid
|
||
generation of \jitiSTAR instructions for them. Similarly, if some
|
||
argument sequences will definitely demand indexing we can speed up
|
||
execution by generating the appropriate tables at compile time instead
|
||
of dynamically.
|
||
|
||
\subsection{Demand-driven index construction and its properties}
|
||
%---------------------------------------------------------------
|
||
The idea behind \JITI can be captured in a single sentence: \emph{we
|
||
can generate every index we need during program execution when this
|
||
index is demanded}. Subsequent uses of these indices can speed up
|
||
execution considerably more than the time it takes to construct them
|
||
(more on this below) so this runtime action makes sense.%\footnote{In
|
||
%fact, because choice points are expensive in the WAM, \JITI can speed
|
||
%up even the execution of the query that triggers the process, not only
|
||
%subsequent queries.}
|
||
%
|
||
%We describe the process of demand-driven index construction.
|
||
|
||
% \subsubsection{Demand-driven index construction}
|
||
%-------------------------------------------------
|
||
Let $p/k$ be a predicate with $n$ clauses.
|
||
%
|
||
At a high level, its indices form a tree whose root is the entry point
|
||
of the predicate. For simplicity, assume that the root node of the
|
||
tree and the interior nodes corresponding to the index table for the
|
||
first argument have been constructed at compile time. Leaves of this
|
||
tree are the nodes containing the code for the clauses of the
|
||
predicate and each clause is identified by a unique label \mbox{$L_i,
|
||
1 \leq i \leq n$}. Execution always starts at the first instruction of
|
||
the root node and follows Algorithm~\ref{alg:construction}. The
|
||
algorithm might look complicated but is actually quite simple.
|
||
%
|
||
Each non-leaf node contains a sequence of byte code instructions with
|
||
groups of the form \mbox{$\langle I_1, \ldots, I_m, T_1, \ldots, T_l
|
||
\rangle, 0 \leq m \leq k, 1 \leq l \leq n$} where each of the $I$
|
||
instructions, if any, is either a \switchSTAR or a \jitiSTAR
|
||
instruction and each of the $T$ instructions either forms a sequence
|
||
of \TryRetryTrust instructions (if $l > 1$) or is a \jump instruction
|
||
(if \mbox{$l = 1$}). Step~2.2 dynamically constructs an index table
|
||
$\cal T$ whose buckets are the newly created interior nodes in the
|
||
tree. Each bucket associated with a single clause contains a \jump to
|
||
the label of that clause. Each bucket associated with many clauses
|
||
starts with the $I$ instructions which are yet to be visited and
|
||
continues with a \TryRetryTrust chain pointing to the clauses. When
|
||
the index construction is done, the instruction mutates to a
|
||
\switchSTAR WAM instruction.
|
||
%-------------------------------------------------------------------------
|
||
\begin{Algorithm}[t]
|
||
\caption{Actions of the abstract machine with \JITI}
|
||
\label{alg:construction}
|
||
\begin{enumerate}
|
||
\item if the current instruction $I$ is a \switchSTAR, \try, \retry,
|
||
\trust or \jump, act as in the WAM;
|
||
\item if the current instruction $I$ is a \jitiSTAR with arguments $r,
|
||
l$, and $k$ ($r$ is a register) then
|
||
\begin{enumerate}
|
||
\item[2.1] if register $r$ contains a variable, the action is a
|
||
\instr{goto} the next instruction in the node;
|
||
\item[2.2] if register $r$ contains a value $v$, the action is to
|
||
dynamically construct the index:
|
||
\begin{itemize}
|
||
\item[2.2.1] collect the subsequent instructions in a list $\cal I$
|
||
until the next instruction is a \try;
|
||
\item[2.2.2] for each label $L$ in the \TryRetryTrust chain
|
||
inspect the code of the clause with label $L$ to find the
|
||
symbol~$c$ associated with register $r$ in the clause; (This
|
||
step creates a list of $\langle c, L \rangle$ pairs.)
|
||
\item[2.2.3] create an index table $\cal T$ out of these pairs as
|
||
follows:
|
||
\begin{itemize}
|
||
\item if $I$ is a \jitiONconstant or a \jitiONstructure then
|
||
create an index table for the symbols in the list of pairs;
|
||
each entry of the table is identified by a symbol $c$ and
|
||
contains:
|
||
\begin{itemize}
|
||
\item the instruction \jump $L_c$ if $L_c$ is the only label
|
||
associated with $c$;
|
||
\item the sequence of instructions obtained by appending to
|
||
$\cal I$ a \TryRetryTrust chain for the sequence of labels
|
||
$L'_1, \ldots, L'_l$ that are associated with $c$
|
||
\end{itemize}
|
||
\item if $I$ is a \jitiONterm then
|
||
\begin{itemize}
|
||
\item partition the sequence of labels $\cal L$ in the list
|
||
of pairs into sequences of labels ${\cal L}_c, {\cal L}_l$
|
||
and ${\cal L}_s$ for constants, lists and structures,
|
||
respectively;
|
||
\item for each of the four sequences ${\cal L}, {\cal L}_c,
|
||
{\cal L}_l, {\cal L}_s$ of labels create code:
|
||
\begin{itemize}
|
||
\item the instruction \fail if the sequence is empty;
|
||
\item the instruction \jump $L$ if $L$ is the only label in
|
||
the sequence;
|
||
\item the sequence of instructions obtained by appending to
|
||
$\cal I$ a \TryRetryTrust chain for the current sequence
|
||
of labels;
|
||
\end{itemize}
|
||
\end{itemize}
|
||
\end{itemize}
|
||
\item[2.2.4] transform the \jitiSTAR $r, l, k$ instruction to
|
||
a \switchSTAR $r, l, {\cal T}$ instruction;
|
||
\item[2.2.5] continue execution with this instruction.
|
||
\end{itemize}
|
||
\end{enumerate}
|
||
\end{enumerate}
|
||
\vspace*{-.7em}
|
||
\end{Algorithm}
|
||
%-------------------------------------------------------------------------
|
||
|
||
\Paragraph{Complexity properties.}
|
||
Index construction during runtime does not change the complexity of
|
||
query execution. First, note that each demanded index table will be
|
||
constructed at most once. Also, a \jitiSTAR instruction will be
|
||
encountered only in cases where execution would examine all clauses in
|
||
the \TryRetryTrust chain.\footnote{This statement is possibly not
|
||
valid in the presence of Prolog cuts.} The construction visits these
|
||
clauses \emph{once} and then creates the index table in time linear in
|
||
the number of clauses as one pass over the list of $\langle c, L
|
||
\rangle$ pairs suffices. After index construction, execution will
|
||
visit a subset of these clauses as the index table will be consulted.
|
||
%% Finally, note that the maximum number of \jitiSTAR instructions
|
||
%% that will be visited for each query is bounded by the maximum
|
||
%% number of index positions (symbols) in the clause heads of the
|
||
%% predicate.
|
||
Thus, in cases where \JITI is not effective, execution of a query will
|
||
at most double due to dynamic index construction. In fact, this worst
|
||
case is pessimistic and unlikely in practice. On the other hand, \JITI
|
||
can change the complexity of query evaluation from $O(n)$ to $O(1)$
|
||
where $n$ is the number of clauses.
|
||
|
||
\subsection{More implementation choices}
|
||
%---------------------------------------
|
||
The observant reader has no doubt noticed that
|
||
Algorithm~\ref{alg:construction} provides multi-argument indexing but
|
||
only for the main functor symbol. For clauses with compound terms that
|
||
require indexing in their sub-terms we can either employ a program
|
||
transformation such as \emph{unification
|
||
factoring}~\cite{UnifFact@POPL-95} at compile time or modify the
|
||
algorithm to consider index positions inside compound terms. This is
|
||
relatively easy to do but requires support from the register allocator
|
||
(passing the sub-terms of compound terms in appropriate registers)
|
||
and/or a new set of instructions. Due to space limitations we omit
|
||
further details.
|
||
|
||
Algorithm~\ref{alg:construction} relies on a procedure that inspects
|
||
the code of a clause and collects the symbols associated with some
|
||
particular index position (step~2.2.2). If we are satisfied with
|
||
looking only at clause heads, this procedure needs to understand only
|
||
the structure of \instr{get} and \instr{unify} instructions. Thus, it
|
||
is easy to write. At the cost of increased implementation complexity,
|
||
this step can of course take into account other information that may
|
||
exist in the body of the clause (e.g., type tests such as
|
||
\code{var(X)}, \code{atom(X)}, aliasing constraints such as \code{X =
|
||
Y}, numeric constraints such as \code{X > 0}, etc.).
|
||
|
||
A reasonable concern for \JITI is increased memory consumption. In our
|
||
experience, this does not seem to be a problem in practice since most
|
||
applications do not have demand for indexing on many argument
|
||
combinations. In applications where it does become a problem or when
|
||
running in an environment with limited memory, we can easily put a
|
||
bound on the size of index tables, either globally or for each
|
||
predicate separately. For example, the \jitiSTAR instructions can
|
||
either become inactive when this limit is reached, or better yet we
|
||
can recover the space of some tables. To do so, we can employ any
|
||
standard recycling algorithm (e.g., LRU) and reclaim the memory of
|
||
index tables that are no longer in use. This is easy to do by
|
||
reverting the corresponding \switchSTAR instructions back to \jitiSTAR
|
||
instructions. If the indices are demanded again at a time when memory
|
||
is available, they can simply be regenerated.
|
||
|
||
|
||
\section{Demand-Driven Indexing of Dynamic Predicates} \label{sec:dynamic}
|
||
%=========================================================================
|
||
We have so far lived in the comfortable world of static predicates,
|
||
where the set of clauses to index is fixed and the compiler can take
|
||
advantage of this knowledge. Dynamic code introduces several
|
||
complications:
|
||
\begin{itemize}
|
||
\item We need mechanisms to update multiple indices when new clauses
|
||
are asserted or retracted. In particular, we need the ability to
|
||
expand and possibly shrink multiple code chunks after code updates.
|
||
\item We do not know a priori which are the best index positions and
|
||
cannot determine whether indexing on some arguments is avoidable.
|
||
\item Supporting the logical update (LU) semantics of ISO Prolog
|
||
becomes harder.
|
||
\end{itemize}
|
||
We briefly discuss possible ways of addressing these issues.
|
||
However, note that Prolog systems typically provide indexing for
|
||
dynamic predicates and thus already deal in some way or another with
|
||
these issues; \JITI makes the problems more involved but not
|
||
fundamentally different than with only first argument indexing.
|
||
|
||
The first complication suggests that we should allocate memory for
|
||
dynamic indices in separate chunks, so that these can be expanded and
|
||
deallocated independently. Indeed, this is what we do.
|
||
%
|
||
Regarding the second complication, in the absence of any other
|
||
information, the only alternative is to generate indices for all
|
||
arguments. As optimizations, we can avoid indexing predicates with
|
||
only one clause and exclude arguments where some clause has a
|
||
variable.
|
||
|
||
Under LU semantics, calls to dynamic predicates execute in a
|
||
``snapshot'' of the corresponding predicate. Each call sees the
|
||
clauses that existed at the time when the call was made, even if some
|
||
of the clauses were later retracted or new clauses were asserted. If
|
||
several calls are alive in the stack, several snapshots will be alive
|
||
at the same time. The standard solution to this problem is to use time
|
||
stamps to tell which clauses are \emph{live} for which calls.
|
||
%
|
||
This solution complicates freeing index tables because: (1) an index
|
||
table holds references to clauses, and (2) the table may be in use
|
||
(i.e., may be accessible from the execution stacks). An index
|
||
table thus is killed in several steps:
|
||
\begin{enumerate}
|
||
\item Detach the index table from the indexing tree.
|
||
\item Recursively \emph{kill} every child of the current table; if a
|
||
table is killed so are its children.
|
||
\item Wait until the table is not in use, that is, it is not pointed
|
||
to from anywhere.
|
||
\item Walk the table and release any references it may hold.
|
||
\item Physically recover space.
|
||
\end{enumerate}
|
||
|
||
|
||
\section{Implementation in XXX and in YAP} \label{sec:impl}
|
||
%==========================================================
|
||
The implementation of \JITI in XXX follows a variant of the scheme
|
||
presented in Sect.~\ref{sec:static}. The compiler uses heuristics to
|
||
determine the best argument to index on (i.e., this argument is not
|
||
necessarily the first) and employs \switchSTAR instructions for this
|
||
task. It also statically generates \jitiONconstant instructions for
|
||
other arguments that are good candidates for \JITI. Currently, an
|
||
argument is considered a good candidate if it has only constants or
|
||
only structure symbols in all clauses. Thus, XXX uses only
|
||
\jitiONconstant and \jitiONstructure instructions, never a
|
||
\jitiONterm. Also, XXX does not perform \JITI inside structure
|
||
symbols. For dynamic predicates, \JITI is employed only if they
|
||
consist of Datalog facts; if a clause which is not a Datalog fact is
|
||
asserted, all dynamically created index tables for the predicate are
|
||
simply removed and the \jitiONconstant instruction becomes a
|
||
\instr{noop}. All this is done automatically, but the user can disable
|
||
\JITI in compiled code using an option.
|
||
|
||
YAP implements \JITI since version 5. The current implementation
|
||
supports static code, dynamic code, and the internal database. It
|
||
differs from the algorithm presented in Sect.~\ref{sec:static} in that
|
||
\emph{all indexing code is generated on demand}. Thus, YAP cannot
|
||
assume that a \jitiSTAR instruction is followed by a \TryRetryTrust
|
||
chain. Instead, by default YAP has to search the whole predicate for
|
||
clauses that match the current position in the indexing code. Doing so
|
||
for every index expansion was found to be very inefficient for larger
|
||
relations: in such cases YAP will maintain a list of matching clauses
|
||
at each \jitiSTAR node. Indexing dynamic predicates in YAP follows
|
||
very much the same algorithm as static indexing: the key idea is that
|
||
most nodes in the index tree must be allocated separately so that they
|
||
can grow or shrink independently. YAP can index arguments where some
|
||
clauses have unconstrained variables, but only for static predicates,
|
||
as in dynamic code this would complicate support for LU semantics.
|
||
|
||
YAP uses the term JITI (Just-In-Time Indexing) to refer to \JITI. In
|
||
the next section we will take the liberty to use this term as a
|
||
convenient abbreviation.
|
||
|
||
|
||
\section{Performance Evaluation} \label{sec:perf}
|
||
%================================================
|
||
We evaluate JITI on a set of benchmarks and applications.
|
||
Throughout, we compare performance of JITI with first argument
|
||
indexing. For the benchmarks of Sect.~\ref{sec:perf:ineffective}
|
||
and~\ref{sec:perf:effective} which involve both systems, we used a
|
||
2.4~GHz P4-based laptop with 512~MB of memory.
|
||
% and report times in milliseconds.
|
||
For the benchmarks of Sect.~\ref{sec:perf:ILP} which involve
|
||
YAP~5.1.2 only, we used a 8-node cluster, where each node is a
|
||
dual-core AMD~2600+ machine with 2GB of memory.
|
||
% and report times in seconds.
|
||
|
||
%------------------------------------------------------------------------------
|
||
\begin{table}[t]
|
||
\centering
|
||
\caption{Performance of some benchmarks with 1st vs. \JITI (times in msecs)}
|
||
\vspace*{-1em}
|
||
\subfigure[When JITI is ineffective]{%
|
||
\label{tab:ineffective}
|
||
\begin{tabular}[b]{|l||r|r||r|r|} \hline
|
||
& \multicolumn{2}{|c||}{\bf YAP} & \multicolumn{2}{|c|}{\bf XXX} \\
|
||
\cline{2-5}
|
||
Benchmark & 1st & JITI & 1st & JITI \\
|
||
\hline
|
||
\tcLio (8000) & 13 & 14 & 4 & 4 \\
|
||
\tcRio (2000) & 1445 & 1469 & 614 & 615 \\
|
||
\tcDio ( 400) & 3208 & 3260 & 2338 & 2300 \\
|
||
\tcLoo (2000) & 3935 & 3987 & 2026 & 2105 \\
|
||
\tcRoo (2000) & 2841 & 2952 & 1502 & 1512 \\
|
||
\tcDoo ( 400) & 3735 & 3805 & 4976 & 4978 \\
|
||
\compress & 3614 & 3595 & 2875 & 2848 \\
|
||
\hline
|
||
\end{tabular}
|
||
}%
|
||
\subfigure[When JITI is effective]{
|
||
\label{tab:effective}
|
||
\begin{tabular}[b]{|l||r|r|r||r|r|r|} \hline
|
||
& \multicolumn{3}{|c||}{\bf YAP} & \multicolumn{3}{|c|}{\bf XXX} \\
|
||
\cline{2-7}
|
||
& 1st & JITI &{\bf ratio}& 1st & JITI &{\bf ratio}\\
|
||
\hline
|
||
\sgCyl & 2,864 & 24 & $119\times$& 2,390 & 28 & $85\times$\\
|
||
\muta & 30,057 &16,782 &$1.79\times$&26,314 &21,574 &$1.22\times$\\
|
||
\pta & 5,131 & 188 & $27\times$& 4,442 & 279 & $16\times$\\
|
||
\tea &1,478,813 &54,616 & $27\times$& --- & --- & --- \\
|
||
\hline
|
||
\end{tabular}
|
||
}%
|
||
\vspace*{-1em}
|
||
\end{table}
|
||
%------------------------------------------------------------------------------
|
||
|
||
\subsection{Performance of \JITI when ineffective} \label{sec:perf:ineffective}
|
||
%------------------------------------------------------------------------------
|
||
In some programs, \JITI does not trigger\footnote{In XXX only; even
|
||
1st argument indexing is generated on demand when JITI is used in
|
||
YAP.} or might trigger but have no effect other than an overhead due
|
||
to runtime index construction. We therefore wanted to measure this
|
||
overhead.
|
||
%
|
||
As both systems support tabling, we decided to use tabling benchmarks
|
||
because they are small and easy to understand, and because they are a
|
||
bad case for JITI in the following sense: tabling avoids generating
|
||
repetitive queries and the benchmarks operate over extensional
|
||
database (EDB) predicates of size approximately equal to the size of
|
||
the program. We used \compress, a tabled program that solves a puzzle
|
||
from an ICLP Prolog programming competition. The other benchmarks are
|
||
different variants of tabled left, right and doubly recursive
|
||
transitive closure over an EDB predicate forming a chain of size shown
|
||
in Table~\ref{tab:ineffective} in parentheses. For each variant of
|
||
transitive closure, we issue two queries: one with mode
|
||
\code{(in,out)} and one with mode \code{(out,out)}.
|
||
%
|
||
For YAP, indices on the first argument and \TryRetryTrust chains are
|
||
built on all benchmarks under \JITI.
|
||
%
|
||
For XXX, \JITI triggers on no benchmark but the \jitiONconstant
|
||
instructions are executed for the three \bench{tc\_?\_oo} benchmarks.
|
||
%
|
||
As can be seen in Table~\ref{tab:ineffective}, \JITI, even when
|
||
ineffective, incurs a runtime overhead that is at the level of noise
|
||
and goes mostly unnoticed.
|
||
%
|
||
We also note that our aim here is \emph{not} to compare the two
|
||
systems, so the \textbf{YAP} and \textbf{XXX} columns should be read
|
||
separately.
|
||
|
||
\vspace*{-0.5em}
|
||
\subsection{Performance of \JITI when effective} \label{sec:perf:effective}
|
||
%--------------------------------------------------------------------------
|
||
On the other hand, when \JITI is effective, it can significantly
|
||
improve runtime performance. We use the following programs and
|
||
applications:
|
||
%------------------------------------------------------------------------------
|
||
\begin{small}
|
||
\begin{description}
|
||
\item[\sgCyl] The same generation DB benchmark on a $24 \times 24
|
||
\times 2$ cylinder. We issue the open query.
|
||
\item[\muta] A computationally intensive application where most
|
||
predicates are defined intentionally.
|
||
\item[\pta] A tabled logic program implementing Andersen's points-to
|
||
analysis. A medium-sized imperative program is encoded as a set of
|
||
facts (about 16,000) and properties of interest are encoded using
|
||
rules. Program properties are then determined by the closure of
|
||
these rules.
|
||
\item[\tea] Another implementation of Andersen's points-to analysis.
|
||
The analyzed program, the \texttt{javac} benchmark, is encoded in a
|
||
file of 411,696 facts (62,759,581 bytes in total). Its compilation
|
||
exceeds the limits of the XXX compiler (w/o JITI). So we run this
|
||
benchmark only in YAP.
|
||
\end{description}
|
||
\end{small}
|
||
%------------------------------------------------------------------------------
|
||
|
||
As can be seen in Table~\ref{tab:effective}, \JITI significantly
|
||
improves the performance of these applications. In \muta, which spends
|
||
most of its time in recursive predicates, the speed up is only $79\%$
|
||
in YAP and~$22\%$ in XXX. The remaining benchmarks execute several
|
||
times (from~$16$ up to~$119$) faster. It is important to realize that
|
||
\emph{these speedups are obtained automatically}, i.e., without any
|
||
programmer intervention or by using any compiler directives, in all
|
||
these applications.
|
||
|
||
\subsection{Performance of \JITI on ILP applications} \label{sec:perf:ILP}
|
||
%-------------------------------------------------------------------------
|
||
The need for \JITI was originally noticed in inductive logic
|
||
programming applications. These applications tend to issue ad hoc
|
||
queries during execution and thus their indexing requirements cannot
|
||
be determined at compile time. On the other hand, they operate on lots
|
||
of data, so memory consumption is a reasonable concern. We evaluate
|
||
JITI's time and space performance on some learning tasks using the
|
||
Aleph system~\cite{ALEPH} and the datasets of
|
||
Fig.~\ref{fig:ilp:datasets} which issue simple queries in an
|
||
extensional database. Several of these datasets are standard in the
|
||
ILP literature.
|
||
|
||
\Paragraph{Time performance.}
|
||
We compare times for 10 runs of the saturation/refinement cycle of the
|
||
ILP system; see Table~\ref{tab:ilp:time}.
|
||
%% The \Krki datasets have small search spaces and small databases, so
|
||
%% they achieve the same performance under both versions: there is no
|
||
%% slowdown.
|
||
The \Mesh and \Pyrimidines applications are the only ones that do not
|
||
benefit much from indexing in the database; they do benefit through
|
||
from indexing in the dynamic representation of the search space, as
|
||
their running times improve somewhat with \JITI.
|
||
|
||
The \BreastCancer and \GeneExpr applications use unstructured data.
|
||
The speedup here is mostly from multiple argument indexing.
|
||
\BreastCancer is particularly interesting. It consists of 40 binary
|
||
relations with 65k elements each, where the first argument is the key.
|
||
We know that most calls have the first argument bound, hence indexing
|
||
was not expected to matter much. Instead, the results show \JITI to
|
||
improve running time by more than an order of magnitude. This suggests
|
||
that even a small percentage of badly indexed calls can end up
|
||
dominating runtime.
|
||
|
||
\IEProtein and \Thermolysin are example applications that manipulate
|
||
structured data. \IEProtein is the largest dataset we consider, and
|
||
indexing is absolutely critical. The speedup is not just impressive;
|
||
it is simply not possible to run the application in reasonable time
|
||
with only first argument indexing. \Thermolysin is smaller and
|
||
performs some computation per query, but even so, \JITI improves its
|
||
performance by an order of magnitude. The remaining benchmarks improve
|
||
from one to more than two orders of magnitude.
|
||
|
||
%------------------------------------------------------------------------------
|
||
\begin{table}[t]
|
||
\centering
|
||
\caption{Time and space performance of JITI
|
||
on Inductive Logic Programming datasets}
|
||
\vspace*{-1em}
|
||
\label{tab:ilp}
|
||
\setlength{\tabcolsep}{2.5pt}
|
||
\subfigure[Time (in seconds)]{\label{tab:ilp:time}
|
||
\begin{tabular}{|l||r|r|r||} \hline
|
||
& \multicolumn{3}{|c||}{Time} \\
|
||
\cline{2-4}
|
||
Benchmark & 1st & JITI &{\bf ratio} \\
|
||
\hline
|
||
\BreastCancer & 1,450 & 88 & $16\times$ \\
|
||
\Carcino & 17,705 & 192 & $92\times$ \\
|
||
\Choline & 14,766 & 1,397 & $11\times$ \\
|
||
\GeneExpr & 193,283 & 7,483 & $26\times$ \\
|
||
\IEProtein & 1,677,146 & 2,909 & $577\times$ \\
|
||
%% \Krki & 0.3 & 0.3 & $1$ \\
|
||
%% \KrkiII & 1.3 & 1.3 & $1$ \\
|
||
\Mesh & 4 & 3 & $1.3\times$ \\
|
||
\Pyrimidines & 487,545 & 253,235 & $1.9\times$ \\
|
||
\Susi & 105,091 & 307 & $342\times$ \\
|
||
\Thermolysin & 50,279 & 5,213 & $10\times$ \\
|
||
\hline
|
||
\end{tabular}
|
||
}%
|
||
\subfigure[Memory usage (in KB)]{\label{tab:ilp:memory}
|
||
\begin{tabular}{||r|r|r|r||} \hline
|
||
\multicolumn{2}{||c|}{Static code}
|
||
& \multicolumn{2}{|c||}{Dynamic code} \\
|
||
\hline
|
||
\multicolumn{1}{||c|}{Clauses} & \multicolumn{1}{c}{Index}
|
||
& \multicolumn{1}{|c|}{Clauses} & \multicolumn{1}{c||}{Index}\\
|
||
\hline
|
||
60,940 & 46,887 & 630 & 14 \\
|
||
1,801 & 2,678 & 13,512 & 942 \\
|
||
666 & 174 & 3,172 & 174 \\
|
||
46,726 & 22,629 & 116,463 & 9,015 \\
|
||
146,033 & 129,333 & 53,423 & 1,531 \\
|
||
%% 678 & 117 & 2,047 & 24 \\
|
||
%% 1,866 & 715 & 2,055 & 26 \\
|
||
802 & 161 & 2,149 & 109 \\
|
||
774 & 218 & 25,840 & 12,291 \\
|
||
5,007 & 2,509 & 4,497 & 759 \\
|
||
2,317 & 929 & 116,129 & 7,064 \\
|
||
\hline
|
||
\end{tabular}
|
||
}%
|
||
\end{table}
|
||
%------------------------------------------------------------------------------
|
||
|
||
%------------------------------------------------------------------------------
|
||
\begin{figure}
|
||
\hrule \ \\[-2em]
|
||
\begin{description}
|
||
%% \item[\Krki] tries to learn rules from a small database of chess end-games;
|
||
\item[\GeneExpr] learns rules for yeast gene activity given a
|
||
database of genes, their interactions, and micro-array gene
|
||
expression data; %~\cite{Regulatory@ILP-06};
|
||
\item[\BreastCancer] processes real-life patient reports towards
|
||
predicting whether an abnormality may be
|
||
malignant; %~\cite{DavisBDPRCS@IJCAI-05-short};
|
||
\item[\IEProtein] processes information extraction from paper
|
||
abstracts to search proteins;
|
||
\item[\Susi] learns from shopping patterns;
|
||
\item[\Mesh] learns rules for finite-methods mesh design;
|
||
\item[\Carcino, \Choline, \Pyrimidines] try to predict chemical
|
||
properties of compounds and store them as tables, given their
|
||
chemical composition and major properties;
|
||
\item[\Thermolysin] also manipulates chemical compounds but learns
|
||
from the 3D-structure of a molecule's conformations.
|
||
\end{description}
|
||
\hrule
|
||
\caption{Description of the ILP datasets used in the performance
|
||
comparison of Table~\ref{tab:ilp}}
|
||
\label{fig:ilp:datasets}
|
||
\end{figure}
|
||
%------------------------------------------------------------------------------
|
||
|
||
\Paragraph{Space performance.}
|
||
Table~\ref{tab:ilp:memory} shows memory usage when using \JITI. The
|
||
table presents data obtained at a point near the end of execution;
|
||
memory usage should be at the maximum. These applications use a
|
||
mixture of static and dynamic predicates and we show their memory
|
||
usage separately. On static predicates, memory usage varies widely,
|
||
from only 10\% to the worst case, \Carcino, where the index tables
|
||
take more space than the original program. Hash tables dominate usage
|
||
in \IEProtein and \Susi, whereas \TryRetryTrust chains dominate in
|
||
\BreastCancer. In most other cases no single component dominates
|
||
memory usage. Memory usage for dynamic predicates is shown in the last
|
||
two columns; this data is mostly used to store the search space.
|
||
Observe that there is a much lower overhead in this case. A more
|
||
detailed analysis shows that most space is occupied by the hash tables
|
||
and by internal nodes of the tree, and that relatively little space is
|
||
occupied by \TryRetryTrust chains, suggesting that \JITI is behaving
|
||
well in practice.
|
||
|
||
|
||
\section{Concluding Remarks}
|
||
%===========================
|
||
Motivated by the needs of applications in the areas of inductive
|
||
logic programming, program analysis, deductive databases, etc.\ to
|
||
access large datasets efficiently, we have described a novel but also
|
||
simple idea: \emph{indexing Prolog clauses on demand during program
|
||
execution}.
|
||
%
|
||
Given the impressive speedups this idea can provide for many LP
|
||
applications, we are a bit surprised similar techniques have not been
|
||
explored before. In general, Prolog systems have been reluctant to
|
||
perform code optimizations during runtime and our feeling is that LP
|
||
implementation has been left a bit behind. We hold that this
|
||
should change.
|
||
%
|
||
Indeed, we see \JITI as only a first, very successful, step towards
|
||
effective runtime optimization of logic programs.
|
||
|
||
As presented, \JITI is a hybrid technique: index generation occurs
|
||
during runtime but is partly guided by the compiler, because we want
|
||
to combine it with compile-time WAM-style indexing. More flexible
|
||
schemes are of course possible. For example, index generation can be
|
||
fully dynamic (as in YAP), combined with user declarations, or driven
|
||
by static analysis to be even more selective or go beyond fixed-order
|
||
indexing.
|
||
%
|
||
Last, observe that \JITI fully respects Prolog semantics. Better
|
||
performance can be achieved in the context of one solution
|
||
computations, or in the context of tabling where order of clauses and
|
||
solutions does not matter and repeated solutions are discarded.
|
||
|
||
\paragragh{Acknowledgments}
|
||
|
||
This work is dedicated to the memory of our friend and colleague
|
||
Ricardo Lopes. We miss you! V\{\i}tor Santos Costa was partially
|
||
supported by CNPq and would like to acknowledge support received while
|
||
visiting at UW-Madison and the support of the YAP user community.
|
||
This work has been partially supported by MYDDAS (POSC/EIA/59154/2004)
|
||
and by funds granted to LIACC through the Programa de Financiamento
|
||
Plurianual, Funda<64><61>o para a Ci<43>ncia e Tecnologia and Programa POSC.
|
||
|
||
\Paragraph{Acknowledgments}
|
||
%--------------------------
|
||
V\'{\i}tor Santos Costa was partially supported by CNPq and would like
|
||
to acknowledge support received while visiting at UW-Madison and the
|
||
support of the YAP user community. This work has been partially
|
||
supported by MYDDAS (POSC/EIA/59154/2004) and by funds granted to
|
||
LIACC through the Programa de Financiamento Plurianual, Funda<64><61>o para
|
||
a Ci<43>ncia e Tecnologia and Programa POSC.
|
||
|
||
%==============================================================================
|
||
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|
||
%==============================================================================
|
||
|
||
\end{document}
|