952 resultados para Run-Time Code Generation, Programming Languages, Object-Oriented Programming


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Knowing the size of the terms to which program variables are bound at run-time in logic programs is required in a class of optimizations which includes granularity control and recursion elimination. Such size is difficult to even approximate at compile time and is thus generally computed at run-time by using (possibly predeñned) predicates which traverse the terms involved. We propose a technique which has the potential of performing this computation much more efficiently. The technique is based on ñnding program procedures which are called before those in which knowledge regarding term sizes is needed and which traverse the terms whose size is to be determined, and transforming such procedures so that they compute term sizes "on the fly". We present a systematic way of determining whether a given program can be transformed in order to compute a given term size at a given program point without additional term traversal. Also, if several such transformations are possible our approach allows ñnding minimal transformations under certain criteria. We also discuss the advantages and applications of our technique (specifically in the task of granularity control) and present some performance results.

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Logic programming systems which exploit and-parallelism among non-deterministic goals rely on notions of independence among those goals in order to ensure certain efficiency properties. "Non-strict" independence (NSI) is a more relaxed notion than the traditional notion of "strict" independence (SI) which still ensures the relevant efficiency properties and can allow considerable more parallelism than SI. However, all compilation technology developed to date has been based on SI, because of the intrinsic complexity of exploiting NSI. This is related to the fact that NSI cannot be determined "a priori" as SI. This paper filis this gap by developing a technique for compile-time detection and annotation of NSI. It also proposes algorithms for combined compiletime/ run-time detection, presenting novel run-time checks for this type of parallelism. Also, a transformation procedure to eliminate shared variables among parallel goals is presented, aimed at performing as much work as possible at compile-time. The approach is based on the knowledge of certain properties regarding the run-time instantiations of program variables —sharing and freeness— for which compile-time technology is available, with new approaches being currently proposed. Thus, the paper does not deal with the analysis itself, but rather with how the analysis results can be used to parallelize programs.

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Studying independence of literals, variables, and substitutions has proven very useful in the context of logic programming (LP). Here we study independence in the broader context of constraint logic programming (CLP). We show that a naive extrapolation of the LP definitions of independence to CLP is unsatisfactory (in fact, wrong) for two reasons. First, because interaction between variables through constraints is more complex than in the case of logic programming. Second, in order to ensure the efUciency of several optimizations not only must independence of the search space be considered, but also an orthogonal issue - "independence of constraint solving." We clarify these issues by proposing various types of search independence and constraint solver independence, and show how they can be combined to allow different independence-related optimizations, from parallelism to intelligent backtracking. Sufficient conditions for independence which can be evaluated "a-priori" at run-time are also proposed. Our results suggest that independence, provided a suitable definition is chosen, is even more useful in CLP than in LP.

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This paper presents a technique for achieving a class of optimizations related to the reduction of checks within cycles. The technique uses both Program Transformation and Abstract Interpretation. After a ñrst pass of an abstract interpreter which detects simple invariants, program transformation is used to build a hypothetical situation that simpliñes some predicates that should be executed within the cycle. This transformation implements the heuristic hypothesis that once conditional tests hold they may continué doing so recursively. Specialized versions of predicates are generated to detect and exploit those cases in which the invariance may hold. Abstract interpretation is then used again to verify the truth of such hypotheses and conñrm the proposed simpliñcation. This allows optimizations that go beyond those possible with only one pass of the abstract interpreter over the original program, as is normally the case. It also allows selective program specialization using a standard abstract interpreter not speciñcally designed for this purpose, thus simplifying the design of this already complex module of the compiler. In the paper, a class of programs amenable to such optimization is presented, along with some examples and an evaluation of the proposed techniques in some application áreas such as floundering detection and reducing run-time tests in automatic logic program parallelization. The analysis of the examples presented has been performed automatically by an implementation of the technique using existing abstract interpretation and program transformation tools.

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In this paper, abstract interpretation algorithms are described for computing the sharmg as well as the freeness information about the run-time instantiations of program variables. An abstract domain is proposed which accurately and concisely represents combined freeness and sharing information for program variables. Abstract unification and all other domain-specific functions for an abstract interpreter working on this domain are presented. These functions are illustrated with an example. The importance of inferring freeness is stressed by showing (1) the central role it plays in non-strict goal independence, and (2) the improved accuracy it brings to the analysis of sharing information when both are computed together. Conversely, it is shown that keeping accurate track of sharing allows more precise inference of freeness, thus resulting in an overall much more powerful abstract interpreter.

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This paper presents and develops a generalized concept of Non-Strict Independent And Parallelism (NSIAP). NSIAP extends the applicability of Independent And- Parallelism (IAP) by enlarging the class of goals which are eligible for parallel execution. At the same time it maintains IAP's ability to run non-deterministic goals in parallel and to preserve the computational complexity expected in the execution of the program by the programmer. First, a parallel execution framework is defined and some fundamental correctness results, in the sense of equivalence of solutions with the sequential model, are discussed for this framework. The issue of efficiency is then considered. Two new definitions of NSI are given for the cases of puré and impure goals respectively and efficiency results are provided for programs parallelized under these definitions which include treatment of the case of goal failure: not only is reduction of execution time guaranteed (modulo run-time overheads) in the absence of failure but it is also shown that in the worst case of failure no speed-down will occur. In addition to applying to NSI, these results carry over and complete previous results shown in the context of IAP which did not deal with the case of goal failure. Finally, some practical examples of the application of the NSIAP concept to the parallelization of a set of programs are presented and performance results, showing the advantage of using NSI, are given.

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Traditional schemes for abstract interpretation-based global analysis of logic programs generally focus on obtaining procedure argument mode and type information. Variable sharing information is often given only the attention needed to preserve the correctness of the analysis. However, such sharing information can be very useful. In particular, it can be used for predicting run-time goal independence, which can eliminate costly run-time checks in and-parallel execution. In this paper, a new algorithm for doing abstract interpretation in logic programs is described which infers the dependencies of the terms bound to program variables with increased precisión and at all points in the execution of the program, rather than just at a procedure level. Algorithms are presented for computing abstract entry and success substitutions which extensively keep track of variable aliasing and term dependence information. The algorithms are illustrated with examples.

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The technique of Abstract Interpretation has allowed the development of very sophisticated global program analyses which are at the same time provably correct and practical. We present in a tutorial fashion a novel program development framework which uses abstract interpretation as a fundamental tool. The framework uses modular, incremental abstract interpretation to obtain information about the program. This information is used to validate programs, to detect bugs with respect to partial specifications written using assertions (in the program itself and/or in system librarles), to genérate and simplify run-time tests, and to perform high-level program transformations such as múltiple abstract specialization, parallelization, and resource usage control, all in a provably correct way. In the case of validation and debugging, the assertions can refer to a variety of program points such as procedure entry, procedure exit, points within procedures, or global computations. The system can reason with much richer information than, for example, traditional types. This includes data structure shape (including pointer sharing), bounds on data structure sizes, and other operational variable instantiation properties, as well as procedure-level properties such as determinacy, termination, non-failure, and bounds on resource consumption (time or space cost). CiaoPP, the preprocessor of the Ciao multi-paradigm programming system, which implements the described functionality, will be used to illustrate the fundamental ideas.

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We present a generic preprocessor for combined static/dynamic validation and debugging of constraint logic programs. Passing programs through the preprocessor prior to execution allows detecting many bugs automatically. This is achieved by performing a repertoire of tests which range from simple syntactic checks to much more advanced checks based on static analysis of the program. Together with the program, the user may provide a series of assertions which trigger further automatic checking of the program. Such assertions are written using the assertion language presented in Chapter 2, which allows expressing a wide variety of properties. These properties extend beyond the predefined set which may be understandable by the available static analyzers and include properties defined by means of user programs. In addition to user-provided assertions, in each particular CLP system assertions may be available for predefined system predicates. Checking of both user-provided assertions and assertions for system predicates is attempted first at compile-time by comparing them with the results of static analysis. This may allow statically proving that the assertions hold (Le., they are validated) or that they are violated (and thus bugs detected). User-provided assertions (or parts of assertions) which cannot be statically proved ñor disproved are optionally translated into run-time tests. The implementation of the preprocessor is generic in that it can be easily customized to different CLP systems and dialects and in that it is designed to allow the integration of additional analyses in a simple way. We also report on two tools which are instances of the generic preprocessor: CiaoPP (for the Ciao Prolog system) and CHIPRE (for the CHIP CLP(FL>) system). The currently existing analyses include types, modes, non-failure, determinacy, and computational cost, and can treat modules separately, performing incremental analysis.

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We discuss a framework for the application of abstract interpretation as an aid during program development, rather than in the more traditional application of program optimization. Program validation and detection of errors is first performed statically by comparing (partial) specifications written in terms of assertions against information obtained from (global) static analysis of the program. The results of this process are expressed in the user assertion language. Assertions (or parts of assertions) which cannot be checked statically are translated into run-time tests. The framework allows the use of assertions to be optional. It also allows using very general properties in assertions, beyond the predefined set understandable by the static analyzer and including properties defined by user programs. We also report briefly on an implementation of the framework. The resulting tool generates and checks assertions for Prolog, CLP(R), and CHIP/CLP(fd) programs, and integrates compile-time and run-time checking in a uniform way. The tool allows using properties such as types, modes, non-failure, determinacy, and computational cost, and can treat modules separately, performing incremental analysis.

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Abstract interpretation has been widely used for the analysis of object-oriented languages and, in particular, Java source and bytecode. However, while most existing work deals with the problem of flnding expressive abstract domains that track accurately the characteristics of a particular concrete property, the underlying flxpoint algorithms have received comparatively less attention. In fact, many existing (abstract interpretation based—) flxpoint algorithms rely on relatively inefHcient techniques for solving inter-procedural caligraphs or are speciflc and tied to particular analyses. We also argüe that the design of an efficient fixpoint algorithm is pivotal to supporting the analysis of large programs. In this paper we introduce a novel algorithm for analysis of Java bytecode which includes a number of optimizations in order to reduce the number of iterations. The algorithm is parametric -in the sense that it is independent of the abstract domain used and it can be applied to different domains as "plug-ins"-, multivariant, and flow-sensitive. Also, is based on a program transformation, prior to the analysis, that results in a highly uniform representation of all the features in the language and therefore simplifies analysis. Detailed descriptions of decompilation solutions are given and discussed with an example. We also provide some performance data from a preliminary implementation of the analysis.

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CIAO is an advanced programming environment supporting Logic and Constraint programming. It offers a simple concurrent kernel on top of which declarative and non-declarative extensions are added via librarles. Librarles are available for supporting the ISOProlog standard, several constraint domains, functional and higher order programming, concurrent and distributed programming, internet programming, and others. The source language allows declaring properties of predicates via assertions, including types and modes. Such properties are checked at compile-time or at run-time. The compiler and system architecture are designed to natively support modular global analysis, with the two objectives of proving properties in assertions and performing program optimizations, including transparently exploiting parallelism in programs. The purpose of this paper is to report on recent progress made in the context of the CIAO system, with special emphasis on the capabilities of the compiler, the techniques used for supporting such capabilities, and the results in the áreas of program analysis and transformation already obtained with the system.

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Knowing the size of the terms to which program variables are bound at run-time in logic programs is required in a class of applications related to program optimization such as, for example, recursion elimination and granularity analysis. Such size is difficult to even approximate at compile time and is thus generally computed at run-time by using (possibly predefined) predicates which traverse the terms involved. We propose a technique based on program transformation which has the potential of performing this computation much more efficiently. The technique is based on finding program procedures which are called before those in which knowledge regarding term sizes is needed and which traverse the terms whose size is to be determined, and transforming such procedures so that they compute term sizes "on the fly". We present a systematic way of determining whether a given program can be transformed in order to compute a given term size at a given program point without additional term traversal. Also, if several such transformations are possible our approach allows finding minimal transformations under certain criteria. We also discuss the advantages and present some applications of our technique.

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We present in a tutorial fashion CiaoPP, the preprocessor of the Ciao multi-paradigm programming system, which implements a novel program development framework which uses abstract interpretation as a fundamental tool. The framework uses modular, incremental abstract interpretation to obtain information about the program. This information is used to validate programs, to detect bugs with respect to partial specifications written using assertions (in the program itself and/or in system libraries), to generate and simplify run-time tests, and to perform high-level program transformations such as multiple abstract specialization, parallelization, and resource usage control, all in a provably correct way. In the case of validation and debugging, the assertions can refer to a variety of program points such as procedure entry, procedure exit, points within procedures, or global computations. The system can reason with much richer information than, for example, traditional types. This includes data structure shape (including pointer sharing), bounds on data structure sizes, and other operational variable instantiation properties, as well as procedure-level properties such as determinacy, termination, non-failure, and bounds on resource consumption (time or space cost).

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El Análisis de Consumo de Recursos o Análisis de Coste trata de aproximar el coste de ejecutar un programa como una función dependiente de sus datos de entrada. A pesar de que existen trabajos previos a esta tesis doctoral que desarrollan potentes marcos para el análisis de coste de programas orientados a objetos, algunos aspectos avanzados, como la eficiencia, la precisión y la fiabilidad de los resultados, todavía deben ser estudiados en profundidad. Esta tesis aborda estos aspectos desde cuatro perspectivas diferentes: (1) Las estructuras de datos compartidas en la memoria del programa son una pesadilla para el análisis estático de programas. Trabajos recientes proponen una serie de condiciones de localidad para poder mantener de forma consistente información sobre los atributos de los objetos almacenados en memoria compartida, reemplazando éstos por variables locales no almacenadas en la memoria compartida. En esta tesis presentamos dos extensiones a estos trabajos: la primera es considerar, no sólo los accesos a los atributos, sino también los accesos a los elementos almacenados en arrays; la segunda se centra en los casos en los que las condiciones de localidad no se cumplen de forma incondicional, para lo cual, proponemos una técnica para encontrar las precondiciones necesarias para garantizar la consistencia de la información acerca de los datos almacenados en memoria. (2) El objetivo del análisis incremental es, dado un programa, los resultados de su análisis y una serie de cambios sobre el programa, obtener los nuevos resultados del análisis de la forma más eficiente posible, evitando reanalizar aquellos fragmentos de código que no se hayan visto afectados por los cambios. Los analizadores actuales todavía leen y analizan el programa completo de forma no incremental. Esta tesis presenta un análisis de coste incremental, que, dado un cambio en el programa, reconstruye la información sobre el coste del programa de todos los métodos afectados por el cambio de forma incremental. Para esto, proponemos (i) un algoritmo multi-dominio y de punto fijo que puede ser utilizado en todos los análisis globales necesarios para inferir el coste, y (ii) una novedosa forma de almacenar las expresiones de coste que nos permite reconstruir de forma incremental únicamente las funciones de coste de aquellos componentes afectados por el cambio. (3) Las garantías de coste obtenidas de forma automática por herramientas de análisis estático no son consideradas totalmente fiables salvo que la implementación de la herramienta o los resultados obtenidos sean verificados formalmente. Llevar a cabo el análisis de estas herramientas es una tarea titánica, ya que se trata de herramientas de gran tamaño y complejidad. En esta tesis nos centramos en el desarrollo de un marco formal para la verificación de las garantías de coste obtenidas por los analizadores en lugar de analizar las herramientas. Hemos implementado esta idea mediante la herramienta COSTA, un analizador de coste para programas Java y KeY, una herramienta de verificación de programas Java. De esta forma, COSTA genera las garantías de coste, mientras que KeY prueba la validez formal de los resultados obtenidos, generando de esta forma garantías de coste verificadas. (4) Hoy en día la concurrencia y los programas distribuidos son clave en el desarrollo de software. Los objetos concurrentes son un modelo de concurrencia asentado para el desarrollo de sistemas concurrentes. En este modelo, los objetos son las unidades de concurrencia y se comunican entre ellos mediante llamadas asíncronas a sus métodos. La distribución de las tareas sugiere que el análisis de coste debe inferir el coste de los diferentes componentes distribuidos por separado. En esta tesis proponemos un análisis de coste sensible a objetos que, utilizando los resultados obtenidos mediante un análisis de apunta-a, mantiene el coste de los diferentes componentes de forma independiente. Abstract Resource Analysis (a.k.a. Cost Analysis) tries to approximate the cost of executing programs as functions on their input data sizes and without actually having to execute the programs. While a powerful resource analysis framework on object-oriented programs existed before this thesis, advanced aspects to improve the efficiency, the accuracy and the reliability of the results of the analysis still need to be further investigated. This thesis tackles this need from the following four different perspectives. (1) Shared mutable data structures are the bane of formal reasoning and static analysis. Analyses which keep track of heap-allocated data are referred to as heap-sensitive. Recent work proposes locality conditions for soundly tracking field accesses by means of ghost non-heap allocated variables. In this thesis we present two extensions to this approach: the first extension is to consider arrays accesses (in addition to object fields), while the second extension focuses on handling cases for which the locality conditions cannot be proven unconditionally by finding aliasing preconditions under which tracking such heap locations is feasible. (2) The aim of incremental analysis is, given a program, its analysis results and a series of changes to the program, to obtain the new analysis results as efficiently as possible and, ideally, without having to (re-)analyze fragments of code that are not affected by the changes. During software development, programs are permanently modified but most analyzers still read and analyze the entire program at once in a non-incremental way. This thesis presents an incremental resource usage analysis which, after a change in the program is made, is able to reconstruct the upper-bounds of all affected methods in an incremental way. To this purpose, we propose (i) a multi-domain incremental fixed-point algorithm which can be used by all global analyses required to infer the cost, and (ii) a novel form of cost summaries that allows us to incrementally reconstruct only those components of cost functions affected by the change. (3) Resource guarantees that are automatically inferred by static analysis tools are generally not considered completely trustworthy, unless the tool implementation or the results are formally verified. Performing full-blown verification of such tools is a daunting task, since they are large and complex. In this thesis we focus on the development of a formal framework for the verification of the resource guarantees obtained by the analyzers, instead of verifying the tools. We have implemented this idea using COSTA, a state-of-the-art cost analyzer for Java programs and KeY, a state-of-the-art verification tool for Java source code. COSTA is able to derive upper-bounds of Java programs while KeY proves the validity of these bounds and provides a certificate. The main contribution of our work is to show that the proposed tools cooperation can be used for automatically producing verified resource guarantees. (4) Distribution and concurrency are today mainstream. Concurrent objects form a well established model for distributed concurrent systems. In this model, objects are the concurrency units that communicate via asynchronous method calls. Distribution suggests that analysis must infer the cost of the diverse distributed components separately. In this thesis we propose a novel object-sensitive cost analysis which, by using the results gathered by a points-to analysis, can keep the cost of the diverse distributed components separate.