4,347 research outputs found
Compiling Prolog to Idiomatic Java
Today, Prolog is often used to solve well-defined, domain-specific problems that are part of larger applications. In such cases, a tight integration of the Prolog program and the rest of the application, which is commonly written in a different language, is necessary. One common approach is to compile the Prolog code to (native) code in the target language. In this case, the effort necessary to build, test and deploy the final application is reduced. However, most of the approaches that achieve reasonable performance compile Prolog to
object-oriented code that relies on some kind of virtual machine (VM). These VMs are libraries implemented in the target language and implement Prolog\u27s execution semantics. This adds a significant layer to the object-oriented program and results in code that does not look and feel native to developers of object-oriented programs. Further, if Prolog\u27s execution semantics is implemented as a library the potential of modern runtime environments for object-oriented
programs, such as the Java Virtual Machine, to effectively optimize the program is more limited. In this paper, we report on our approach to compile Prolog to high-level, idiomatic object-oriented
Java code. The generated Java code closely resembles code written by Java developers and is effectively optimized by the Java Virtual Machine
SICStus MT - A Multithreaded Execution Environment for SICStus Prolog
The development of intelligent software agents and other
complex applications which continuously interact with their
environments has been one of the reasons why explicit concurrency has
become a necessity in a modern Prolog system today. Such applications
need to perform several tasks which may be very different with respect
to how they are implemented in Prolog. Performing these tasks
simultaneously is very tedious without language support.
This paper describes the design, implementation and evaluation of a
prototype multithreaded execution environment for SICStus Prolog. The
threads are dynamically managed using a small and compact set of
Prolog primitives implemented in a portable way, requiring almost no
support from the underlying operating system
Optimizing the SICStus Prolog virtual machine instruction set
The Swedish Institute of Computer Science (SICS) is the vendor of SICStus Prolog.
To decrease execution time and reduce space requirements, variants of SICStus
Prolog's virtual instruction set were investigated. Semi-automatic ways of finding
candidate sets of instructions to combine or specialize were developed and used.
Several virtual machines were implemented and the relationship between improvements
by combinations and by specializations were investigated. The benefits of specializations
and combinations of instructions to the performance of the emulator is on the
average of the order of 10%. The code size reduction is 15%
Description and Optimization of Abstract Machines in a Dialect of Prolog
In order to achieve competitive performance, abstract machines for Prolog and
related languages end up being large and intricate, and incorporate
sophisticated optimizations, both at the design and at the implementation
levels. At the same time, efficiency considerations make it necessary to use
low-level languages in their implementation. This makes them laborious to code,
optimize, and, especially, maintain and extend. Writing the abstract machine
(and ancillary code) in a higher-level language can help tame this inherent
complexity. We show how the semantics of most basic components of an efficient
virtual machine for Prolog can be described using (a variant of) Prolog. These
descriptions are then compiled to C and assembled to build a complete bytecode
emulator. Thanks to the high level of the language used and its closeness to
Prolog, the abstract machine description can be manipulated using standard
Prolog compilation and optimization techniques with relative ease. We also show
how, by applying program transformations selectively, we obtain abstract
machine implementations whose performance can match and even exceed that of
state-of-the-art, highly-tuned, hand-crafted emulators.Comment: 56 pages, 46 figures, 5 tables, To appear in Theory and Practice of
Logic Programming (TPLP
Comparing Tag Scheme Variations Using an Abstract Machine Generator
In this paper we study, in the context of a WAM-based abstract machine for Prolog, how variations in the encoding of type information in tagged words and in their associated basic operations impact performance and memory usage. We use a high-level language to specify encodings and the associated operations. An automatic generator constructs both the abstract machine using this encoding and the associated Prolog-to-byte code compiler. Annotations in this language make it possible to impose constraints on the final representation of tagged words, such as the effectively addressable space (fixing, for example, the word size of the target processor /architecture), the layout of the tag and value bits inside the tagged word, and how the basic operations are implemented. We evaluate large number of combinations of the different parameters in two scenarios: a) trying to obtain an optimal general-purpose abstract machine and b) automatically generating a specially-tuned abstract machine for a particular program. We conclude that we are able to automatically generate code featuring all the optimizations present in a hand-written, highly-optimized abstract machine and we canal so obtain emulators with larger addressable space and better performance
Approaches to Interpreter Composition
In this paper, we compose six different Python and Prolog VMs into 4 pairwise
compositions: one using C interpreters; one running on the JVM; one using
meta-tracing interpreters; and one using a C interpreter and a meta-tracing
interpreter. We show that programs that cross the language barrier frequently
execute faster in a meta-tracing composition, and that meta-tracing imposes a
significantly lower overhead on composed programs relative to mono-language
programs.Comment: 33 pages, 1 figure, 9 table
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