Autor:
Tamarit, Salvador

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salvador.tamarit@upm.es
salvador.tamarit@upm.es, Spain
stamarit@dsic.upv.es
salvador.tamarit@upm.e
stamarit@babel.ls.fi.upm.es

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Tamarit

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Salvador

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Universidad Politécnica de Madrid, Spain
Universidad Politécnica de Madrid
Universitat Politècnica de València , Spain
Universidad Politécnica de Madrid Madrid, Spain

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Mostrando 1 - 4 de 4
  • Artículo
    A Declarative Debugger for Concurrent Erlang Programs
    Caballero, Rafael; Martin-Martin, Enrique; Riesco, Adrián; Tamarit, Salvador. Actas de las XV Jornadas de Programación y Lenguajes (PROLE 2015), 2015-09-15.
    Erlang is a concurrent language with features such as actor model concurrency, no shared memory, message passing communication, high scalability and availability. However, the development of concurrent programs is a complex and error prone task. In this paper we present a declarative debugging approach for concurrent Erlang programs. Our debugger asks questions about the validity of transitions between the different points of the program that involve message passing and/or process creation. The answers, which represent the intended behavior of the program, are compared with the transitions obtained in an actual execution of the program. The differences allow us to detect program errors and to point out the pieces of source code responsible for the bugs. In order to represent the computations we present a semantic calculus for concurrent Core Erlang programs. The debugger uses the proof trees in this calculus as debugging trees used for selecting the questions asked to the user. The relation between the debugging trees and the semantic calculus allows us to establish the soundness of the approach. The theoretical ideas have been implemented in a debugger prototype.
  • Artículo
    Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code
    Vigueras, Guillermo; Carro, Manuel; Tamarit, Salvador; Mariño, Julio. Actas de las XVI Jornadas de Programación y Lenguajes (PROLE 2016), 2016-09-02.
    The current trends in next-generation exascale systems go towards integrating a wide range of specialized (co-)processors into traditional supercomputers. Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per surface unit, opening the access of heterogeneous platforms to a range of users as wide as possible is an important problem to be tackled. However, the integration of heterogeneous, specialized devices increases programming complexity, restricting it to a few experts, and makes porting applications onto different computational infrastructures extremely costly. In order to bridge the gap between the programming needs of heterogeneous systems and the expertise of programmers, program transformation has been proposed elsewhere as a means to ease program generation and adaptation. This brings about several issues such as how to plan a transformation strategy which eventually generates code with increased performance. In this paper we propose a machine learning-based approach to learn heuristics for defining transformation strategies of a program transformation system. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of this approach.
  • Artículo
    A Collection of Website Benchmarks Labelled for Template Detection and Content Extraction
    Alarte, Julián; Insa, David; Silva, Josep; Tamarit, Salvador. Actas de las XV Jornadas de Programación y Lenguajes (PROLE 2015), 2015-09-15.
    Template detection and content extraction are two of the main areas of information retrieval applied to the Web. They perform different analyses over the structure and content of webpages to extract some part of the document. However, their objectives are different. While template detection identifies the template of a webpage (usually comparing with other webpages of the same website), content extraction identifies the main content of the webpage discarding the other part. Therefore, they are somehow complementary, because the main content is not part of the template. It has been measured that templates represent between 40% and 50% of data on the Web. Therefore, identifying templates is essential for indexing tasks because templates usually contain irrelevant information such as advertisements, menus and banners. Processing and storing this information is likely to lead to a waste of resources (storage space, bandwidth, etc.). Similarly, identifying the main content is essential for many information retrieval tasks. In this paper, we present a benchmark suite to test different approaches for template detection and content extraction. The suite is public, and it contains real heterogeneous webpages that have been labelled so that different techniques can be suitable (and automatically) compared.
  • Artículo
    Towards a Semantics-Aware Code Transformation Toolchain for Heterogeneous Systems
    Tamarit, Salvador; Mariño, Julio; Vigueras, Guillermo; Carro, Manuel. Actas de las XVI Jornadas de Programación y Lenguajes (PROLE 2016), 2016-09-02.
    Obtaining good performance when programming heterogeneous computing platforms poses significant challenges. We present a program transformation environment, implemented in Haskell, where architecture-agnostic scientific C code with semantic annotations is transformed into functionally equivalent code better suited for a given platform. The transformation steps are represented as rules which can be fired when certain syntactic and semantic conditions are fulfilled. These rules are not hard-wired into the rewriting engine: they are written in a C-like language and are automatically processed and incorporated by the rewriting engine. That makes it possible for end-users to add their own rules or to provide sets of rules which are adapted to certain specific domains or purposes.