Abstract Many applications require conformance with specifications that constrain the use of resources, such as execution time, energy, bandwidth, etc. We present a configurable framework for static resource usage verification where specifications can include data size-dependent resource usage functions, expressing both lower and upper bounds. Ensuring conformance with respect to such specifications is an undecidable problem. Therefore, to statically check such specifications, our framework infers the same type of resource usage functions, which safely approximate the actual resource usage of the program, and compares them against the specification. We review how this framework supports several languages and compilation output formats by translating them to an intermediate representation based on Horn clauses and using the configurability of the framework to describe the resource semantics of the input language. We provide a detailed formalization and extend the framework so that both resource usage specification and analysis/verification output can include preconditions expressing intervals for the input data sizes for which assertions are intended to hold, proved, or disproved. Most importantly, we also extend the classes of functions that can be checked. We also report on and provide results from an implementation within the Ciao/CiaoPP framework, as well as on a practical tool built by instantiating this framework for the verification of energy consumption specifications for imperative/embedded programs. Finally, we show as an example how embedded software developers can use this tool, in particular, for determining values for program parameters that ensure meeting a given energy budget while minimizing the loss in quality of service.

Abstract We provide an overall description of the Ciao multiparadigm programming system emphasizing some of the novel aspects and motivations behind its design and implementation. An important aspect of Ciao is that, in addition to supporting logic programming (and, in particular, Prolog), it provides the programmer with a large number of useful features from different programming paradigms and styles and that the use of each of these features (including those of Prolog) can be turned on and off at will for each program module. Thus, a given module may be using, e.g., higher order functions and constraints, while another module may be using assignment, predicates, Prolog meta-programming, and concurrency. Furthermore, the language is designed to be extensible in a simple and modular way. Another important aspect of Ciao is its programming environment, which provides a powerful preprocessor (with an associated assertion language) capable of statically finding non-trivial bugs, verifying that programs comply with specifications, and performing many types of optimizations (including automatic parallelization). Such optimizations produce code that is highly competitive with other dynamic languages or, with the (experimental) optimizing compiler, even that of static languages, all while retaining the flexibility and interactive development of a dynamic language. This compilation architecture supports modularity and separate compilation throughout. The environment also includes a powerful autodocumenter and a unit testing framework, both closely integrated with the assertion system. The paper provides an informal overview of the language and program development environment. It aims at illustrating the design philosophy rather than at being exhaustive, which would be impossible in a single journal paper, pointing instead to previous Ciao literature.

We report on a detailed study of the application and effectiveness of program analysis based on abstract interpretation of automatic program parallelization. We study the case of parallelizing logic programs using the notion of strict independence. We first propose and prove correct a methodology for the application in the parallelization task of the information inferred by abstract interpretation, using a parametric domain. The methodology is generic in the sense of allowing the use of different analysis domains. A number of well-known approximation domains are then studied and the transformation into the parametric domain defined. The transformation directly illustrates the revelance and applicability of each abstract domain for the application. Both local and global analyzers are then built using these domains and embedded in a complete parallelizing compiler. Then, the performance of the domains in this context is assessed through a number of experiments. A comparatively wide range of aspects is studied, from the resources needed by the analyzers in terms of time and memory to the actual benefits obtained from the information inferred. Such benefits are evaluated both in terms of the characteristics of the parallelized code and of the actual speedups obtained from it. The results show that data flow analysis plays an important role in achieving efficient parallelizations, and that the cost of such analysis con be reasonable even for quite sophisticated abstract domains. Furthermore, the results also offer significant insight into the characteristics of the domains, the demands of the application, and the trade-offs involved.