%============================================================================== \documentclass{llncs} %------------------------------------------------------------------------------ \usepackage{a4wide} \usepackage{float} \usepackage{alltt} \usepackage{xspace} \usepackage{epsfig} \usepackage{wrapfig} \usepackage{subfigure} \renewcommand{\rmdefault}{ptm} %------------------------------------------------------------------------------ \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}} \newcommand{\JITI}{demand-driven indexing\xspace} %------------------------------------------------------------------------------ \newcommand{\bench}[1]{\textbf{\textsf{#1}}} \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} \newcommand{\sgCyl}{\bench{sg\_cyl}\xspace} \newcommand{\muta}{\bench{mutagenesis}\xspace} \newcommand{\pta}{\bench{pta}\xspace} \newcommand{\tea}{\bench{tea}\xspace} %------------------------------------------------------------------------------ \newcommand{\BreastCancer}{\bench{BreastCancer}\xspace} \newcommand{\Carcino}{\bench{Carcinogenesis}\xspace} \newcommand{\Choline}{\bench{Choline}\xspace} \newcommand{\GeneExpr}{\bench{GeneExpression}\xspace} \newcommand{\IEProtein}{\bench{IE-Protein\_Extraction}\xspace} %\newcommand{\Krki}{\bench{Krki}\xspace} %\newcommand{\KrkiII}{\bench{Krki~II}\xspace} \newcommand{\Mesh}{\bench{Mesh}\xspace} \newcommand{\Pyrimidines}{\bench{Pyrimidines}\xspace} \newcommand{\Susi}{\bench{Susi}\xspace} \newcommand{\Thermolysin}{\bench{Thermolysin}\xspace} %------------------------------------------------------------------------------ \newenvironment{SmallProg}{\begin{tt}\begin{small}\begin{tabular}[b]{l}}{\end{tabular}\end{small}\end{tt}} \newenvironment{ScriptProg}{\begin{tt}\begin{scriptsize}\begin{tabular}[b]{l}}{\end{tabular}\end{scriptsize}\end{tt}} \newenvironment{FootProg}{\begin{tt}\begin{footnotesize}\begin{tabular}[c]{l}}{\end{tabular}\end{footnotesize}\end{tt}} \newcommand{\TODOcomment}[2]{% \stepcounter{TODOcounter#1}% {\scriptsize\bf$^{(\arabic{TODOcounter#1})}$}% \marginpar[\fbox{ \parbox{2cm}{\raggedleft \scriptsize$^{({\bf{\arabic{TODOcounter#1}{#1}}})}$% \scriptsize #2}}]% {\fbox{\parbox{2cm}{\raggedright \scriptsize$^{({\bf{\arabic{TODOcounter#1}{#1}}})}$% \scriptsize #2}}} }% \newcounter{TODOcounter} \newcommand{\TODO}[1]{\TODOcomment{}{#1}} %------------------------------------------------------------------------------ \title{Demand-Driven Indexing of Prolog Clauses} \titlerunning{Demand-Driven Indexing of Prolog Clauses} \author{V\'{\i}tor Santos Costa\inst{1} \and Konstantinos Sagonas\inst{2} \and Ricardo Lopes\inst{1}} \authorrunning{V. Santos Costa, K. Sagonas and R. Lopes} \institute{ University of Porto, Portugal \and National Technical University of Athens, Greece } \begin{document} \maketitle \begin{abstract} 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, but applications often rely on features that are inherently dynamic (e.g., generating hypotheses for ILP data sets during runtime) which makes static techniques inapplicable or inaccurate. Another alternative, which has not been investigated so far, is to employ dynamic schemes for flexible demand-driven indexing of Prolog clauses. We propose such schemes and discuss issues that need to be addressed for their efficient implementation in the context of WAM-based Prolog systems. We have implemented demand-driven indexing in two different Prolog systems and have been able to obtain non-negligible performance speedups: from a few percent up to orders of magnitude. Given these results, we see very little reason for Prolog systems not to incorporate some form of dynamic indexing based on actual demand. In fact, we see demand-driven indexing as the first step towards effective runtime optimization of Prolog programs. \end{abstract} \section{Introduction} %===================== 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 features that drastically depart from WAM's basic characteristics. % For example, first argument indexing is sufficient for many Prolog applications. However, it is clearly sub-optimal for applications accessing large databases; for a long time now, the database community has recognized that good indexing is the basis for fast query 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 unnecessarily. Static analysis can partly address this concern, but in applications that rely on features which are inherently dynamic (e.g., generating hypotheses for inductive logic programming data sets during runtime) static analysis is inapplicable or grossly inaccurate. Another alternative, which has not been investigated so far, is to do flexible indexing on demand during program execution. This is precisely what we advocate with this paper. More specifically, we present a small extension to the WAM that allows for flexible indexing of Prolog clauses during runtime based on actual demand. For static predicates, the scheme we propose is partly guided by the compiler; for dynamic code, besides being demand-driven by queries, the method needs to cater for code updates during runtime. Where our schemes radically depart from current practice is that they generate new byte code during runtime, in effect doing a form of just-in-time compilation. In our experience these schemes pay off. We have implemented \JITI in two different Prolog systems (YAP and XXX) and have obtained non-trivial speedups, ranging from a few percent to orders of magnitude, across a wide range of applications. Given these results, we see very little reason for Prolog systems not to incorporate some form of indexing based on actual demand from queries. In fact, we see \JITI as only the first step towards effective runtime optimization of Prolog programs. This paper is structured as follows. After commenting on the state of the art and related work concerning indexing in Prolog systems (Sect.~\ref{sec:related}) we briefly review indexing in the WAM (Sect.~\ref{sec:prelims}). We then present \JITI schemes for static (Sect.~\ref{sec:static}) and dynamic (Sect.~\ref{sec:dynamic}) predicates, their implementation in two Prolog systems (Sect.~\ref{sec:impl}) and the performance benefits they bring (Sect.~\ref{sec:perf}). The paper ends with some concluding remarks. \section{State of the Art and Related Work} \label{sec:related} %============================================================== % Indexing in Prolog systems: To the best of our knowledge, many Prolog systems still only support indexing on the main functor symbol of the first argument. Some others, like YAP version 4, can look inside some compound terms~\cite{YAP}. SICStus Prolog supports \emph{shallow backtracking}~\cite{ShallowBacktracking@ICLP-89}; choice points are fully populated only when it is certain that 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}, SWI-Prolog~\cite{SWI} and XSB~\cite{XSB} allow for user-controlled multi-argument indexing (via an \code{:-~index} directive). Notably, ilProlog~\cite{ilProlog} uses compile-time heuristics and generates code for multi-argument indexing automatically. In all these systems, 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 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. % 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 put forward by Van Roy, Demoen and Willems who investigated indexing on several arguments in the form of 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, in XSB 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}. Some of their arguments make sense for certain applications, but, as we shall show, in general they underestimate the benefits of indexing on EDB predicates. Nevertheless, it is true that unless the modes of predicates are known we run the risk of doing indexing on output arguments, whose only effect is an unnecessary increase in compilation times and, more importantly, in code size. In a programming language like Mercury~\cite{Mercury@JLP-96} where modes are known the compiler can of course avoid this risk; indeed in Mercury modes (and types) are used to guide the compiler generate good indexing tables. However, the situation is different for a language like 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. 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 on the programmer, mode information is not used for multi-argument indexing. % The grand finale: The situation is actually worse for certain types of Prolog applications. For example, consider applications in the area of inductive logic programming. These applications on the one hand have high demands for effective indexing since they need to efficiently 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. % Our thesis is that the Prolog 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 will extend this support with the technique of \JITI that we describe in the next sections. \section{Indexing in the WAM} \label{sec:prelims} %================================================ To make the paper relatively self-contained we briefly review the indexing 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 (possibly empty) 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 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 (or equivalently, \instr{N} is the size of the hash table). In each bucket of this hash table and also in the bucket for the variable case of \switchONterm the code performs a sequential backtracking search of 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. For simplicity of presentation we will not consider them here. This is not a problem since the above scheme handles all cases. Also, we will feel free to do some minor modifications and optimizations when this simplifies things. We present an example. Consider the Prolog code shown in Fig.~\ref{fig:carc:facts}. It is a fragment of the well-known machine learning dataset \textit{Carcinogenesis}~\cite{Carcinogenesis@ILP-97}. The five clauses get compiled to the WAM code shown in Fig.~\ref{fig:carc:clauses}. The first argument indexing 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 instruction is the entry point of predicate. Note that compared with vanilla WAM this instruction has an extra argument: the register on the value of which we will 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 \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). \end{ScriptProg} }% \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} \end{tabular} \end{\wamcodesize} \end{sf} }% \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} \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 register $r_i$ is a free variable, then execution continues with the next instruction; \item otherwise, \JITI kicks in as follows. The abstract machine will scan the WAM code of the clauses and create 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. Also, 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 will get transformed to a \switchONconstant. Again this is straightforward because of the two instructions have similar layouts in memory. Execution of the abstract machine will continue 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} \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; table $T_2$ 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, even 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 \JITI on \emph{any single} argument. At least for big sets of Datalog facts, we see little reason not to use this indexing scheme. \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} had the atom \code{p} as value throughout.} Then we can avoid generating \jitiONconstant instructions for them. Also, suppose we detect or heuristically decide 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 these arguments \emph{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 table $T_2$, 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}{@{}cccc@{}} \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}{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{\wamcodesize} \end{sf} \caption{\JITI for all argument combinations; table $T_1$ is static; $T_2$ and $T_3$ are generated 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.\footnote{In many Prolog systems, a procedure with similar functionality often exists for the disassembler, the debugger, etc.} \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 1987 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 the $T_1$ table can be generated either by the compiler or by 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 encountered during runtime and often avoid unnecessary choice point creations. \end{itemize} This is \JITI as we have implemented it. % in one of our Prolog systems. However, we note that these decisions are orthogonal to the main idea and are under compiler control. If, for example, analysis determines that some argument sequences will never demand indexing we can simply avoid generation of \jitiSTAR instructions for these. Similarly, if we determine that some argument sequences will definitely demand indexing we can speed up execution by generating the appropriate index tables at compile time instead of at runtime. \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, we 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 the $T$ instructions are either a sequence of \TryRetryTrust instructions (if $l > 1$) or 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 instruction 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, the action is an in the WAM; \item if the current instruction $I$ is a \jitiSTAR with arguments $r, l$, and $k$ where $r$ is a register then \begin{enumerate} \item[2.1] if register $r$ contains a variable, the action is simply to \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 as follows: \begin{itemize} \item[2.2.1] collect the subsequent instructions in a list $\cal I$ until the next instruction is a \try;\footnote{Note that there will always be a \try following a \jitiSTAR instruction.} \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 as follows: \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; and \item[2.2.5] continue execution with this instruction. \end{itemize} \end{enumerate} \end{enumerate} \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 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 extremely 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 of arguments. For clauses with compound terms that require indexing in their sub-terms we can either employ a program transformation like \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 argument 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 during runtime due to the index tables. 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., least recently used) and reclaim the 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 so-called logical update (LU) semantics of the ISO Prolog standard becomes harder. \end{itemize} We will briefly discuss possible ways of addressing these issues. However, we 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 those 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 for predicates with only one clause (these are often used to simulate global variables) and we can exclude arguments where some clause has a variable. Under logical update semantics calls to dynamic predicates execute in a ``snapshot'' of the corresponding predicate. In other words, each call sees the clauses that existed at the time when the call was made, even if some of the clauses were later deleted 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, that is, it 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 the current table is killed, so will be its children. \item Wait until the table is not in use, that is, it is not pointed to by someone. \item Walk the table and release any references it may hold. \item Physically recover space. \end{enumerate} %% It is interesting to observe that at the end of an \emph{itemset-node} %% the emulator can remove references to the current index, hence freeing %% the code it is currently executing. This happens on the last member of %% the \emph{itemset-node}, so the emulator reads all the instruction's %% arguments before executing the instruction. \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.\footnote{Instead, it prompts its user to request unification factoring for predicates that look likely to benefit from indexing inside compound terms. The user can then use the appropriate compiler directive for these predicates.} 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 killed and the \jitiONconstant instruction becomes a \instr{noop}. All this is done automatically, but the user can disable \JITI in compiled code using an appropriate compiler 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 contract 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 logical update 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 LP 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 running Linux. % 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. \subsection{Performance of \JITI when ineffective} \label{sec:perf:ineffective} %------------------------------------------------------------------------------ In some programs, \JITI does not trigger\footnote{In XXX only; as mentioned in Sect.~\ref{sec:impl} 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 worst 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 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: %% \TODO{For the journal version we should also add FSA benchmarks %% (\bench{k963}, \bench{dg5} and \bench{tl3})} %------------------------------------------------------------------------------ \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~\cite{anderson-phd}. 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 can then be determined by checking the closure of these rules. \item[\tea] Another analyzer using tabling to implement Andersen's points-to analysis. The analyzed program, the \texttt{javac} SPEC benchmark, is encoded in a file of 411,696 facts (62,759,581 bytes in total). As its compilation exceeds the limits of the XXX compiler (w/o JITI), we run this benchmark only in YAP. \end{description} \end{small} %------------------------------------------------------------------------------ %------------------------------------------------------------------------------ \begin{table}[t] \centering \caption{Performance of some benchmarks with 1st vs. \JITI (times in msecs)} \setlength{\tabcolsep}{3pt} \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} Benchmark & 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 &$179\%$ &26,314 &21,574 &$122\%$ \\ \pta & 5,131 & 188 & $27\times$& 4,442 & 279 &$16\times$\\ \tea &1,478,813 &54,616 & $27\times$& --- & --- & --- \\ \hline \end{tabular} } \end{table} %------------------------------------------------------------------------------ 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. We analyze the \sgCyl program that has the biggest speedup in both systems and is the only one whose code is small enough to be shown. With the open call to \texttt{same\_generation/2}, most work in this benchmark consists of calling \texttt{cyl/2} facts in three different modes: with both arguments unbound, with the first argument bound, or with only the second argument bound. Demand-driven indexing improves performance in the last case only, but this improvement makes a big difference in this benchmark. \begin{alltt}\small same_generation(X,X) :- cyl(X,_). same_generation(X,X) :- cyl(_,X). same_generation(X,Y) :- cyl(X,Z), same_generation(Z,W), cyl(Y,W).\end{alltt} %% Our experience with the indexing algorithm described here shows a %% significant performance improvement over the previous indexing code in %% our system. Quite often, this has allowed us to tackle applications %% which previously would not have been feasible. \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 extentional database. Several of these datasets are standard in the Machine Learning 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 data in 1NF (i.e., 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. Like in \sgCyl, 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} \label{tab:ilp} \setlength{\tabcolsep}{3pt} \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 & $16$ \\ \Carcino & 17,705 & 192 & $92$ \\ \Choline & 14,766 & 1,397 & $11$ \\ \GeneExpr & 193,283 & 7,483 & $26$ \\ \IEProtein & 1,677,146 & 2,909 & $577$ \\ %% \Krki & 0.3 & 0.3 & $1$ \\ %% \KrkiII & 1.3 & 1.3 & $1$ \\ \Mesh & 4 & 3 & $1.3$ \\ \Pyrimidines & 487,545 & 253,235 & $1.9$ \\ \Susi & 105,091 & 307 & $342$ \\ \Thermolysin & 50,279 & 5,213 & $10$ \\ \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}; \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; \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; a point where memory usage should be at or close to 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 tree takes 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 data is shown in the last two columns; note that dynamic data is mostly used to store the search space. One can 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 LP 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 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 times. We hold that this should change. % Indeed, we see \JITI as only the first, albeit very important, 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 use static analysis to be even more selective or go beyond fixed-order indexing. % Finally, note 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. %============================================================================== \bibliographystyle{splncs} \bibliography{lp} %============================================================================== \end{document}