214,108 research outputs found
Towards Energy Consumption Verification via Static Analysis
In this paper we leverage an existing general framework for resource usage
verification and specialize it for verifying energy consumption specifications
of embedded programs. Such specifications can include both lower and upper
bounds on energy usage, and they can express intervals within which energy
usage is to be certified to be within such bounds. The bounds of the intervals
can be given in general as functions on input data sizes. Our verification
system can prove whether such energy usage specifications are met or not. It
can also infer the particular conditions under which the specifications hold.
To this end, these conditions are also expressed as intervals of functions of
input data sizes, such that a given specification can be proved for some
intervals but disproved for others. The specifications themselves can also
include preconditions expressing intervals for input data sizes. We report on a
prototype implementation of our approach within the CiaoPP system for the XC
language and XS1-L architecture, and illustrate with an example how embedded
software developers can use this tool, and in particular for determining values
for program parameters that ensure meeting a given energy budget while
minimizing the loss in quality of service.Comment: Presented at HIP3ES, 2015 (arXiv: 1501.03064
Towards an Abstract Domain for Resource Analysis of Logic Programs Using Sized Types
We present a novel general resource analysis for logic programs based on
sized types.Sized types are representations that incorporate structural (shape)
information and allow expressing both lower and upper bounds on the size of a
set of terms and their subterms at any position and depth. They also allow
relating the sizes of terms and subterms occurring at different argument
positions in logic predicates. Using these sized types, the resource analysis
can infer both lower and upper bounds on the resources used by all the
procedures in a program as functions on input term (and subterm) sizes,
overcoming limitations of existing analyses and enhancing their precision. Our
new resource analysis has been developed within the abstract interpretation
framework, as an extension of the sized types abstract domain, and has been
integrated into the Ciao preprocessor, CiaoPP. The abstract domain operations
are integrated with the setting up and solving of recurrence equations for
both, inferring size and resource usage functions. We show that the analysis is
an improvement over the previous resource analysis present in CiaoPP and
compares well in power to state of the art systems.Comment: Part of WLPE 2013 proceedings (arXiv:1308.2055
A General Framework for Static Profiling of Parametric Resource Usage
Traditional static resource analyses estimate the total resource usage of a
program, without executing it. In this paper we present a novel resource
analysis whose aim is instead the static profiling of accumulated cost, i.e.,
to discover, for selected parts of the program, an estimate or bound of the
resource usage accumulated in each of those parts. Traditional resource
analyses are parametric in the sense that the results can be functions on input
data sizes. Our static profiling is also parametric, i.e., our accumulated cost
estimates are also parameterized by input data sizes. Our proposal is based on
the concept of cost centers and a program transformation that allows the static
inference of functions that return bounds on these accumulated costs depending
on input data sizes, for each cost center of interest. Such information is much
more useful to the software developer than the traditional resource usage
functions, as it allows identifying the parts of a program that should be
optimized, because of their greater impact on the total cost of program
executions. We also report on our implementation of the proposed technique
using the CiaoPP program analysis framework, and provide some experimental
results. This paper is under consideration for acceptance in TPLP.Comment: Paper presented at the 32nd International Conference on Logic
Programming (ICLP 2016), New York City, USA, 16-21 October 2016, 22 pages,
LaTe
An Approach to Static Performance Guarantees for Programs with Run-time Checks
Instrumenting programs for performing run-time checking of properties, such
as regular shapes, is a common and useful technique that helps programmers
detect incorrect program behaviors. This is specially true in dynamic languages
such as Prolog. However, such run-time checks inevitably introduce run-time
overhead (in execution time, memory, energy, etc.). Several approaches have
been proposed for reducing such overhead, such as eliminating the checks that
can statically be proved to always succeed, and/or optimizing the way in which
the (remaining) checks are performed. However, there are cases in which it is
not possible to remove all checks statically (e.g., open libraries which must
check their interfaces, complex properties, unknown code, etc.) and in which,
even after optimizations, these remaining checks still may introduce an
unacceptable level of overhead. It is thus important for programmers to be able
to determine the additional cost due to the run-time checks and compare it to
some notion of admissible cost. The common practice used for estimating
run-time checking overhead is profiling, which is not exhaustive by nature.
Instead, we propose a method that uses static analysis to estimate such
overhead, with the advantage that the estimations are functions parameterized
by input data sizes. Unlike profiling, this approach can provide guarantees for
all possible execution traces, and allows assessing how the overhead grows as
the size of the input grows. Our method also extends an existing assertion
verification framework to express "admissible" overheads, and statically and
automatically checks whether the instrumented program conforms with such
specifications. Finally, we present an experimental evaluation of our approach
that suggests that our method is feasible and promising.Comment: 15 pages, 3 tables; submitted to ICLP'18, accepted as technical
communicatio
Applied Evaluative Informetrics: Part 1
This manuscript is a preprint version of Part 1 (General Introduction and
Synopsis) of the book Applied Evaluative Informetrics, to be published by
Springer in the summer of 2017. This book presents an introduction to the field
of applied evaluative informetrics, and is written for interested scholars and
students from all domains of science and scholarship. It sketches the field's
history, recent achievements, and its potential and limits. It explains the
notion of multi-dimensional research performance, and discusses the pros and
cons of 28 citation-, patent-, reputation- and altmetrics-based indicators. In
addition, it presents quantitative research assessment as an evaluation
science, and focuses on the role of extra-informetric factors in the
development of indicators, and on the policy context of their application. It
also discusses the way forward, both for users and for developers of
informetric tools.Comment: The posted version is a preprint (author copy) of Part 1 (General
Introduction and Synopsis) of a book entitled Applied Evaluative
Bibliometrics, to be published by Springer in the summer of 201
Knowledge-based Expressive Technologies within Cloud Computing Environments
Presented paper describes the development of comprehensive approach for
knowledge processing within e-Sceince tasks. Considering the task solving
within a simulation-driven approach a set of knowledge-based procedures for
task definition and composite application processing can be identified. This
procedures could be supported by the use of domain-specific knowledge being
formalized and used for automation purpose. Within this work the developed
conceptual and technological knowledge-based toolbox for complex
multidisciplinary task solv-ing support is proposed. Using CLAVIRE cloud
computing environment as a core platform a set of interconnected expressive
technologies were developed.Comment: Proceedings of the 8th International Conference on Intelligent
Systems and Knowledge Engineering (ISKE2013). 201
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
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