14 research outputs found
Improving PARMA Trailing
Taylor introduced a variable binding scheme for logic variables in his PARMA
system, that uses cycles of bindings rather than the linear chains of bindings
used in the standard WAM representation. Both the HAL and dProlog languages
make use of the PARMA representation in their Herbrand constraint solvers.
Unfortunately, PARMA's trailing scheme is considerably more expensive in both
time and space consumption. The aim of this paper is to present several
techniques that lower the cost.
First, we introduce a trailing analysis for HAL using the classic PARMA
trailing scheme that detects and eliminates unnecessary trailings. The
analysis, whose accuracy comes from HAL's determinism and mode declarations,
has been integrated in the HAL compiler and is shown to produce space
improvements as well as speed improvements. Second, we explain how to modify
the classic PARMA trailing scheme to halve its trailing cost. This technique is
illustrated and evaluated both in the context of dProlog and HAL. Finally, we
explain the modifications needed by the trailing analysis in order to be
combined with our modified PARMA trailing scheme. Empirical evidence shows that
the combination is more effective than any of the techniques when used in
isolation.
To appear in Theory and Practice of Logic Programming.Comment: 36 pages, 7 figures, 8 table
Incremental copying garbage collection for WAM-based Prolog systems
The design and implementation of an incremental copying heap garbage
collector for WAM-based Prolog systems is presented. Its heap layout consists
of a number of equal-sized blocks. Other changes to the standard WAM allow
these blocks to be garbage collected independently. The independent collection
of heap blocks forms the basis of an incremental collecting algorithm which
employs copying without marking (contrary to the more frequently used mark©
or mark&slide algorithms in the context of Prolog). Compared to standard
semi-space copying collectors, this approach to heap garbage collection lowers
in many cases the memory usage and reduces pause times. The algorithm also
allows for a wide variety of garbage collection policies including generational
ones. The algorithm is implemented and evaluated in the context of hProlog.Comment: 33 pages, 22 figures, 5 tables. To appear in Theory and Practice of
Logic Programming (TPLP
Determinacy analysis for logic programs using mode and type information
We propose an analysis for detecting procedures and goals
that are deterministic (i.e. that produce at most one solution), or predicates whose clause tests are mutually exclusive (which implies that at most one of their clauses will succeed) even if they are not deterministic (because they cali other predicates that can produce more than one solution). Applications of such determinacy information include detecting programming errors, performing certain high-level program transformations for improving search efñciency, optimizing low level code generation and parallel execution, and estimating tighter upper bounds on the computational costs of goals and data sizes, which can be used for program debugging, resource consumption and granularity control, etc. We have implemented the analysis and integrated it in the CiaoPP system, which also infers automatically the mode and type information that our analysis takes as input. Experiments performed on this implementation show that the analysis is fairly accurate and efncient
Automatic Inference of Determinacy and Mutual Exclusion for Logic Programs Using Mode and Type Analyses
We propose an analysis for detecting procedures and goals
that are deterministic (i.e., that produce at most one solution at most once),or predicates whose clause tests are mutually exclusive (which implies that at most one of their clauses will succeed) even if they are not deterministic.
The analysis takes advantage of the pruning operator in order to improve the detection of mutual exclusion and determinacy. It also supports arithmetic equations and disequations, as well as equations and disequations on terms,for which we give a complete satisfiability testing algorithm, w.r.t. available type information. Information about determinacy can be used for program debugging and optimization, resource consumption and granularity control,
abstraction carrying code, etc. We have implemented the analysis and integrated it in the CiaoPP system, which also infers automatically the mode and type information that our analysis takes as input. Experiments performed on this implementation show that the analysis is fairly accurate and efficient
Inferring determinacy and mutual exclusion in logic programs using mode and type analysis.
We propose an analysis for detecting procedures and goals that are deterministic (i.e., that produce at most one solution at most once), or predicates whose clause tests are mutually exclusive (which implies that at most one of their clauses will succeed) even if they are not deterministic. The analysis takes advantage of the pruning operator in order to improve the detection of mutual exclusion and determinacy. It also supports arithmetic equations and disequations, as well as equations and disequations on terms, for which we give a complete satisfiability testing algorithm, w.r.t. available type information. We have implemented the analysis and integrated it in the CiaoPP system, which also infers automatically the mode and type information that our analysis takes as input. Experiments performed on this implementation show that the analysis is fairly accurate and efficient
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Probabilistic Finite Domains
This thesis presents a set of programming constructs that are capable of modelling probabilistic concepts and of computing with such concepts. The main objectives are to provide: a theoretically sound, practically achievable and notationally intuitive formalism. The probabilistic programming constructs are presented in the form of a system called probabilistic finite domains, which enhances the Logic Programming paradigm with a novel constraint solver. In doing so, we are able to take advantage of the knowledge representation power of probability. In particular we investigate: first, the duality of the two interpretations of probability to the problems researchers face when wishing to create a probabilistic formalism and second, the use of probability as a unifying model for computational derivations. Some programming examples and a simple implementation are also described
An overview of HAL
Experience using constraint programming to solve real-life problems has shown that finding an efficient solution to the problem often requires experimentation with different constraint solvers or even building a problem-specific constraint solver. HAL is a new constraint logic programming language expressly designed to facilitate this process. It provides a well-defined solver interface, mutable global variables for implementing a constraint stare, and dynamic scheduling for combining, extending and writing new constraint solvers. Equally importantly, HAL supports semi-optional type, mode and determinism declarations. These allow natural constraint specification by means of type overloading, better compile-time error checking and generation of more efficient run-time code.status: publishe