Max-Plus Quasi-interpretations

Amadio, Roberto M.

doi:10.1007/3-540-44904-3_3

Cited by 32 publications

(53 citation statements)

References 18 publications

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“…First, it is clear that an automatisation of our approach relies on the possibility of synthesizing quasi-interpretations. Preliminaries experiences suggest that quasi-interpretations are not too hard to find in practice (see, e.g., [4]), but it remains to be seen whether this approach scales up to large programs. Second, one might wonder whether the read-once condition can be dropped.…”

Section: Resultsmentioning

confidence: 99%

Feasible reactivity in a synchronous Π-calculus

Amadio

Dabrowski

2007

Proceedings of the 9th ACM SIGPLAN International Conference on Principles and Practice of Declarative Programming

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Reactivity is an essential property of a synchronous program. Informally, it guarantees that at each instant the program fed with an input will 'react' producing an output. In the present work, we consider a refined property that we call feasible reactivity. Beyond reactivity, this property guarantees that at each instant both the size of the program and its reaction time are bounded by a polynomial in the size of the parameters at the beginning of the computation and the size of the largest input. We propose a method to annotate programs and we develop related static analysis techniques that guarantee feasible reactivity for programs expressed in the Sπ-calculus. The latter is a synchronous version of the π-calculus based on the SL synchronous programming model. IntroductionMastering the computational complexity of programs is an important aspect of computer security with applications ranging from embedded systems to mobile code and smartcards. One approach to this problem is to monitor at run time the resource consumption and to rise an exception when some bound is reached. A variant of this approach is to instrument the code so that bounds are checked at appropriate time. An alternative approach is to analyse statically the program to guarantee that during the execution it will respect certain resource bounds. In other words, the first approach performs a dynamic verification while the second relies on a static analysis. As usual, the main advantage of the first approach is its flexibility while the advantage of the second approach is the fact that it does not introduce an overhead at run time and, perhaps more importantly, that it allows an early detection of 'buggy' programs. In this work, we will focus on the static analyses which offer the more challenging problems while keeping in mind that the two approaches are complementary. For instance, static analyses may be helpful in reducing the frequency of dynamic verifications. *

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Section: Resultsmentioning

confidence: 99%

Feasible reactivity in a synchronous Π-calculus

Amadio

Dabrowski

2007

Proceedings of the 9th ACM SIGPLAN International Conference on Principles and Practice of Declarative Programming

Self Cite

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“…Using this characterization, we can analyze functions of this class in an easier way based on the premise that it is practically easier to write a first order functional program on streams than the corresponding Unary Oracle Turing Machine. The drawback is that the tool suffers from the same problems as polynomial interpretation: the difficulty to automatically synthesize the interpretation of a given program (see [16]). As a proof of versatility of this tool, we provide a partial characterization of the bff class (the full characterization remaining open), just by changing the interpretation codomain: for that purpose, we use restricted exponentials instead of polynomials in the interpretation of a stream argument.…”

Section: Introductionmentioning

confidence: 99%

Characterizing polynomial time complexity of stream programs using interpretations

Férée

Hainry

Hoyrup

et al. 2015

Theoretical Computer Science

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This paper provides a criterion based on interpretation methods on term rewrite systems in order to characterize the polynomial time complexity of second order functionals. For that purpose it introduces a first order functional stream language that allows the programmer to implement second order functionals. This characterization is extended through the use of exp-poly interpretations as an attempt to capture the class of Basic Feasible Functionals (bff). Moreover, these results are adapted to provide a new characterization of polynomial time complexity in computable analysis. These characterizations give a new insight on the relations between the complexity of functional stream programs and the classes of functions computed by Oracle Turing Machine, where oracles are treated as inputs.

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“…A variety of different techniques have been developed independently not only on the language level but also on the byte code level [1]. Researchers use polynomial interpolation [31], reachability-bound analysis [16], amortization [17], polynomial quasi-interpretation [6] and new language features such as programmer-controlled destruction and copying of data structures [10]. Of course, such analyses are undecidable in general.…”

Section: Introductionmentioning

confidence: 99%

Making resource analysis practical for real-time Java

Kersten

Shkaravska

Gastel

et al. 2012

Proceedings of the 10th International Workshop on Java Technologies for Real-Time and Embedded Systems

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For real-time and embedded systems limiting the consumption of time and memory resources is often an important part of the requirements. Being able to predict bounds on the consumption of these resources during the development process of the code can be of great value.Recent research results have advanced the state of the art of resource consumption analysis. In this paper we present a tool that makes it possible to apply these research results in practice for real-time systems enabling Java developers to analyse loop bounds, bounds on heap size and bounds on stack size. We describe which theoretical additions were needed in order to achieve this.We give an overview of the capabilities of the tool ResAna that is the result of this effort. The tool can not only perform generally applicable analyses, but it also contains a part of the analysis which is dedicated to the developers' (real-time) virtual machine, such that the results apply directly to the actual development environment that is used in practice.

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Max-Plus Quasi-interpretations

Cited by 32 publications

References 18 publications

Feasible reactivity in a synchronous Π-calculus

Feasible reactivity in a synchronous Π-calculus

Characterizing polynomial time complexity of stream programs using interpretations

Making resource analysis practical for real-time Java

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