Towards Integrating Partial Evaluation in a Specialization Framework based on Generic Abstract Interpretation

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Removing superfluous versions in polyvariant specialization of prolog programs.

Polyvariant specialization allows generating múltiple versions of a procedure, which can then be separately optimized for different uses. Since allowing a high degree of polyvariance often results in more optimized code, polyvariant specializers, such as most partial evaluators, can genérate a large number of versions. This can produce unnecessarily large residual programs. Also, large programs can be slower due to cache miss effects. A possible solution to this problem is to introduce a minimization step which identifies sets of equivalent versions, and replace all occurrences of such versions by a single one. In this work we present a unifying view of the problem of superfluous polyvariance. It includes both partial deduction and abstract múltiple specialization. As regards partial deduction, we extend existing approaches in several ways. First, previous work has dealt with puré logic programs and a very limited class of builtins. Herein we propose an extensión to traditional characteristic trees which can be used in the presence of calis to external predicates. This includes all builtins, librarles, other user modules, etc. Second, we propose the possibility of collapsing versions which are not strictly equivalent. This allows trading time for space and can be useful in the context of embedded and pervasive systems. This is done by residualizing certain computations for external predicates which would otherwise be performed at specialization time. Third, we provide an experimental evaluation of the potential gains achievable using minimization which leads to interesting conclusions

Constrained partial deduction and the preservation of characteristic trees

Partial deduction strategies for logic programs often use an abstraction operator to guarantee the finiteness of the set of goals for which partial deductions are produced. Finding an abstraction operator which guarantees finiteness and does not lose relevant information is a difficult problem. In earlier work Gallagher and Bruynooghe proposed to base the abstraction operator on characteristic paths and trees, which capture the structure of the generated incomplete SLDNF-tree for a given goal.In this paper we exhibit the advantages of characteristic trees over purely syntactical measures: if characteristic trees can be preserved upon generalisation, then we obtain an almost perfect abstraction operator, providing just enough polyvariance to avoid any loss of local specialisation. Unfortunately, the abstraction operators proposed in earlier work do not always preserve the characteristic trees upon generalisation. We show that this can lead to important specialisation losses as well as to non-termination of the partial deduction algorithm. Furthermore, this problem cannot be adequately solved in the ordinary partial deduction setting.We therefore extend the expressivity and precision of the Lloyd and Shepherdson partial deduction framework by integrating constraints. We provide formal correctness results for the so obtained generic framework of constrained partial deduction. Within this new framework we are, among others, able to overcome the above mentioned problems by introducing an alternative abstraction operator, based on so called pruning constraints. We thus present a terminating partial deduction strategy which, for purely determinate unfolding rules, induces no loss of local specialisation due to the abstraction while ensuring correctness of the specialised programs

Constrained Partial Deduction and the Preservation of Characteristic Trees

Partial deduction strategies for logic programs often use an abstraction operator to guarantee the finiteness of the set of goals for which partial deductions are produced. Finding an abstraction operator which guarantees finiteness and does not lose relevant information is a difficult problem. In earlier work Gallagher and Bruynooghe proposed to base the abstraction operator on characteristic paths and trees, which capture the structure of the generated incomplete SLDNF-tree for a given goal. In this paper we exhibit the advantages of characteristic trees over purely syntactical measures: if characteristic trees can be preserved upon generalisation, then we obtain an almost perfect abstraction operator, providing just enough polyvariance to avoid any loss of local specialisation. Unfortunately, the abstraction operators proposed in earlier work do not always preserve the characteristic trees upon generalisation. We show that this can lead to important specialisation losse..

Poly-controlled partial evaluation and its application to resource-aware program specialization

La Evaluación Parcial es una técnica automática para la optimización de programas. Su objetivo principal es el de especializar un programa con respecto a parte de sus datos de entrada, los que se conocen como datos estáticos. La calidad del código generado por la evaluación parcial de programas lógicos depende, en gran medida, de la estrategia de control que se haya empleado. Desafortunadamente, aún estamos lejos de contar con una estrategia de control suficientemente sofisticada como para comportarse de manera óptima para cualquier programa. La principal contribución de esta tesis es el desarrollo de la Evaluación Parcial Policontrolada, un novedoso entorno para la evaluación parcial de programas lógicos, el cual es policontrolado en el sentido de que puede tomar en cuenta conjuntos de reglas de control global y local, en lugar de emplear una única combinación predeterminada (como es el caso de la evaluación parcial tradicional). Este entorno es más flexible que los enfoques existentes, ya que permite asignar diferentes reglas de control local y global a diferentes patrones de llamada. De este modo, es posible obtener programas especializados que no pueden ser generados usando evaluación parcial tradicional. En consecuencia, el entorno de evaluación parcial policontrolada puede generar conjuntos de programas especializados, en lugar de un único programa. A través de técnicas auto-ajustables, es posible hacer que este enfoque sea completamente automático. Dichas técnicas permiten medir la calidad de los diferentes programas especializados obtenidos. Este entorno es consciente de los recursos, en el sentido de que cada una de las soluciones obtenidas a través de la evaluación parcial policontrolada es valorada utilizando funciones de adecuación, las que pueden tener en cuenta factores tales como el tamaño de los programas especializados, o la cantidad de memoria que consumen, además de la velocidad del programa especializado que es el factor habitualmente considerado en otros entornos de evaluación parcial. Este entorno de evaluación parcial policontrolada ha sido implementado en el sistema CiaoPP, y evaluado con numerosos programas de prueba. Los resultados experimentales muestran que nuestra propuesta obtiene en muchos casos mejores especializaciones que aquellas generadas usando la evaluación parcial tradicional, especialmente cuando la especialización es consciente de los recursos. Otra de las principales contribuciones de esta tesis es la presentación de una visión unificada del problema de eliminar la polivarianza superflua en la evaluación parcial y en la especialización abstracta múltiple, a través del uso de un paso de minimización, el cual agrupa versiones equivalentes de predicados. Este paso se puede aplicar en la especialización de cualquier programa Prolog, inclusive aquellos que contienen llamadas a predicados predefinidos o predicados externos. Además, ofrecemos la posibilidad de agrupar versiones que no sean estrictamente equivalentes, con el propósito de obtener programas más pequeños