Abstract multiple specialization and its application to program parallelization.

Program specialization optimizes programs for known valúes of the input. It is often the case that the set of possible input valúes is unknown, or this set is infinite. However, a form of specialization can still be performed in such cases by means of abstract interpretation, specialization then being with respect to abstract valúes (substitutions), rather than concrete ones. We study the múltiple specialization of logic programs based on abstract interpretation. This involves in principie, and based on information from global analysis, generating several versions of a program predicate for different uses of such predicate, optimizing these versions, and, finally, producing a new, "multiply specialized" program. While múltiple specialization has received theoretical attention, little previous evidence exists on its practicality. In this paper we report on the incorporation of múltiple specialization in a parallelizing compiler and quantify its effects. A novel approach to the design and implementation of the specialization system is proposed. The resulting implementation techniques result in identical specializations to those of the best previously proposed techniques but require little or no modification of some existing abstract interpreters. Our results show that, using the proposed techniques, the resulting "abstract múltiple specialization" is indeed a relevant technique in practice. In particular, in the parallelizing compiler application, a good number of run-time tests are eliminated and invariants extracted automatically from loops, resulting generally in lower overheads and in several cases in increased speedups.

Compile-time derivation of variable dependency using abstract interpretation

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 runtime 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 concentrates on inferring 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. In addition, a new, abstract domain independent ñxpoint algorithm is presented and described in detail. The algorithms are illustrated with examples. Finally, results from an implementation of the abstract interpreter are presented.

Towards execution time estimation in abstract machine-based languages

Abstract machines provide a certain separation between platformdependent and platform-independent concerns in compilation. Many of the differences between architectures are encapsulated in the speciflc abstract machine implementation and the bytecode is left largely architecture independent. Taking advantage of this fact, we present a framework for estimating upper and lower bounds on the execution times of logic programs running on a bytecode-based abstract machine. Our approach includes a one-time, programindependent proflling stage which calculates constants or functions bounding the execution time of each abstract machine instruction. Then, a compile-time cost estimation phase, using the instruction timing information, infers expressions giving platform-dependent upper and lower bounds on actual execution time as functions of input data sizes for each program. Working at the abstract machine level makes it possible to take into account low-level issues in new architectures and platforms by just reexecuting the calibration stage instead of having to tailor the analysis for each architecture and platform. Applications of such predicted execution times include debugging/veriflcation of time properties, certiflcation of time properties in mobile code, granularity control in parallel/distributed computing, and resource-oriented specialization.

Towards description and optimization of abstract machines in an extension of prolog

Competitive abstract machines for Prolog are usually large, intricate, and incorpórate sophisticated optimizations. This makes them difñcult to code, optimize, and, especially, maintain and extend. This is partly due to the fact that efñciency considerations make it necessary to use low-level languages in their implementation. Writing the abstract machine (and ancillary code) in a higher-level language can help harness this inherent complexity. In this paper we show how the semantics of basic components of an efficient virtual machine for Prolog can be described using (a variant of) Prolog which retains much of its semantics. 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 descriptions 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 highly-tuned, hand-crafted emulators.

Abstract interpretation with specialized definitions

The relationship between abstract interpretation and partial evaluation has received considerable attention and (partial) integrations have been proposed starting from both the partial evaluation and abstract interpretation perspectives. In this work we present what we argüe is the first generic algorithm for efñcient and precise integration of abstract interpretation and partial evaluation from an abstract interpretation perspective. Taking as starting point state-of-the-art algorithms for context-sensitive, polyvariant abstract interpretation and (abstract) partial evaluation of logic programs, we present an algorithm which combines the best of both worlds. Key ingredients include the accurate success propagation inherent to abstract interpretation and the powerful program transformations achievable by partial deduction. In our algorithm, the calis which appear in the analysis graph are not analyzed w.r.t. the original definition of the procedure but w.r.t. specialized definitions of these procedures. Such specialized definitions are obtained by applying both unfolding and abstract executability. Also, our framework is parametric w.r.t. different control strategies and abstract domains. Different combinations of these parameters correspond to existing algorithms for program analysis and specialization. Our approach efficiently computes strictly more precise results than those achievable by each of the individual techniques. The algorithm is one of the key components of CiaoPP, the analysis and specialization system of the Ciao compiler.

A generator of efficient abstract machine implementations and its application to emulator minimization

The implementation of abstract machines involves complex decisions regarding, e.g., data representation, opcodes, or instruction specialization levéis, all of which affect the final performance of the emulator and the size of the bytecode programs in ways that are often difficult to foresee. Besides, studying alternatives by implementing abstract machine variants is a time-consuming and error-prone task because of the level of complexity and optimization of competitive implementations, which makes them generally difficult to understand, maintain, and modify. This also makes it hard to genérate specific implementations for particular purposes. To ameliorate those problems, we propose a systematic approach to the automatic generation of implementations of abstract machines. Different parts of their definition (e.g., the instruction set or the infernal data and bytecode representation) are kept sepárate and automatically assembled in the generation process. Alternative versions of the abstract machine are therefore easier to produce, and variants of their implementation can be created mechanically, with specific characteristics for a particular application if necessary. We illustrate the practicality of the approach by reporting on an implementation of a generator of production-quality WAMs which are specialized for executing a particular fixed (set of) program(s). The experimental results show that the approach is effective in reducing emulator size.

Experiments in abstract interpretation-based code certification for pervasive systems

Proof carrying code (PCC) is a general is originally a roof in ñrst-order logic of certain vermethodology for certifying that the execution of an un- ification onditions and the checking process involves trusted mobile code is safe. The baste idea is that the ensuring that the certifícate is indeed a valid ñrst-order code supplier attaches a certifícate to the mobile code proof. which the consumer checks in order to ensure that the The main practical difñculty of PCC techniques is in code is indeed safe. The potential benefit is that the generating safety certiñeates which at the same time: i) consumer's task is reduced from the level of proving to allow expressing interesting safety properties, ii) can be the level of checking. Recently, the abstract interpre- generated automatically and, iii) are easy and efficient tation techniques developed, in logic programming have to check. In [1], the abstract interpretation techniques been proposed as a basis for PCC. This extended ab- [5] developed in logic programming1 are proposed as stract reports on experiments which illustrate several is- a basis for PCC. They offer a number of advantages sues involved in abstract interpretation-based certifica- for dealing with the aforementioned issues. In particution. First, we describe the implementation of our sys- lar, the xpressiveness of existing abstract domains will tem in the context of CiaoPP: the preprocessor of the be implicitly available in abstract interpretation-based Ciao multi-paradigm programming system. Then, by code certification to deñne a wide range of safety propermeans of some experiments, we show how code certifi- ties. Furthermore, the approach inherits the automation catión is aided in the implementation of the framework. and inference power of the abstract interpretation en- Finally, we discuss the application of our method within gines used in (Constraint) Logic Programming, (C)LP. the área, of pervasive systems

Abstract interpretation-based mobile code certification

Current approaches to mobile code safety – inspired by the technique of Proof-Carrying Code (PCC) [4] – associate safety information (in the form of a certificate) to programs. The certificate (or proof) is created by the code supplier at compile time, and packaged along with the untrusted code. The consumer who receives the code+certificate package can then run a checker which, by a straightforward inspection of the code and the certificate, is able to verify the validity of the certificate and thus compliance with the safety policy. The main practical difficulty of PCC techniques is in generating safety certificates which at the same time: i) allow expressing interesting safety properties, ii) can be generated automatically and, iii) are easy and efficient to check.

Multivariant non-failure analysis via standard abstract interpretation

Non-failure analysis aims at inferring that predicate calis in a program will never fail. This type of information has many applications in functional/logic programming. It is essential for determining lower bounds on the computational cost of calis, useful in the context of program parallelization, instrumental in partial evaluation and other program transformations, and has also been used in query optimization. In this paper, we re-cast the non-failure analysis proposed by Debray et al. as an abstract interpretation, which not only allows to investígate it from a standard and well understood theoretical framework, but has also several practical advantages. It allows us to incorpórate non-failure analysis into a standard, generic abstract interpretation engine. The analysis thus benefits from the fixpoint propagation algorithm, which leads to improved information propagation. Also, the analysis takes advantage of the multi-variance of the generic engine, so that it is now able to infer sepárate non-failure information for different cali patterns. Moreover, the implementation is simpler, and allows to perform non-failure and covering analyses alongside other analyses, such as those for modes and types, in the same framework. Finally, besides the precisión improvements and the additional simplicity, our implementation (in the Ciao/CiaoPP multiparadigm programming system) also shows better efRciency.

Efficient negation using abstract interpretation

While negation has been a very active área of research in logic programming, comparatively few papers have been devoted to implementation issues. Furthermore, the negation-related capabilities of current Prolog systems are limited. We recently presented a novel method for incorporating negation in a Prolog compiler which takes a number of existing methods (some modified and improved by us) and uses them in a combined fashion. The method makes use of information provided by a global analysis of the source code. Our previous work focused on the systematic description of the techniques and the reasoning about correctness and completeness of the method, but provided no experimental evidence to evalúate the proposal. In this paper, we report on an implementation, using the Ciao Prolog system preprocessor, and provide experimental data which indicates that the method is not only feasible but also quite promising from the efficiency point of view. In addition, the tests have provided new insight as to how to improve the proposal further. Abstract interpretation techniques are shown to offer important improvements in this application.

Abstract specialization and its application to program parallelization

Program specialization optimizes programs for known valúes of the input. It is often the case that the set of possible input valúes is unknown, or this set is infinite. However, a form of specialization can still be performed in such cases by means of abstract interpretation, specialization then being with respect to abstract valúes (substitutions), rather than concrete ones. This paper reports on the application of abstract múltiple specialization to automatic program parallelization in the &-Prolog compiler. Abstract executability, the main concept underlying abstract specialization, is formalized, the design of the specialization system presented, and a non-trivial example of specialization in automatic parallelization is given.

Combined determination of sharing and freeness of program variables through abstract interpretation

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.

Determination of variable dependence information through abstract interpretation

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.

An abstract machine for restricted and-parallel execution of logic programs

Although the sequential execution speed of logic programs has been greatly improved by the concepts introduced in the Warren Abstract Machine (WAM), parallel execution represents the only way to increase this speed beyond the natural limits of sequential systems. However, most proposed parallel logic programming execution models lack the performance optimizations and storage efficiency of sequential systems. This paper presents a parallel abstract machine which is an extension of the WAM and is thus capable of supporting ANDParallelism without giving up the optimizations present in sequential implementations. A suitable instruction set, which can be used as a target by a variety of logic programming languages, is also included. Special instructions are provided to support a generalized version of "Restricted AND-Parallelism" (RAP), a technique which reduces the overhead traditionally associated with the run-time management of variable binding conflicts to a series of simple run-time checks, which select one out of a series of compiled execution graphs.

Abstract Interpretation-based verification/certification in the ciaoPP system

CiaoPP is the abstract interpretation-based preprocessor of the Ciao multi-paradigm (Constraint) Logic Programming system. It uses modular, incremental abstract interpretation as a fundamental tool to obtain information about programs. In CiaoPP, the semantic approximations thus produced have been applied to perform high- and low-level optimizations during program compilation, including transformations such as múltiple abstract specialization, parallelization, partial evaluation, resource usage control, and program verification. More recently, novel and promising applications of such semantic approximations are being applied in the more general context of program development such as program verification. In this work, we describe our extensión of the system to incorpórate Abstraction-Carrying Code (ACC), a novel approach to mobile code safety. ACC follows the standard strategy of associating safety certificates to programs, originally proposed in Proof Carrying- Code. A distinguishing feature of ACC is that we use an abstraction (or abstract model) of the program computed by standard static analyzers as a certifícate. The validity of the abstraction on the consumer side is checked in a single-pass by a very efficient and specialized abstractinterpreter. We have implemented and benchmarked ACC within CiaoPP. The experimental results show that the checking phase is indeed faster than the proof generation phase, and that the sizes of certificates are reasonable. Moreover, the preprocessor is based on compile-time (and run-time) tools for the certification of CLP programs with resource consumption assurances.