A parallel evolutionary algorithm to optimize dynamic memory managers in embedded systems

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@Article{RiscoMartin2010572,
  author =       "Jose L. Risco-Martin and David Atienza and 
                 J. Manuel Colmenar and Oscar Garnica",
  title =        "A parallel evolutionary algorithm to optimize dynamic
                 memory managers in embedded systems",
  journal =      "Parallel Computing",
  volume =       "36",
  number =       "10-11",
  pages =        "572--590",
  year =         "2010",
  note =         "Parallel Architectures and Bioinspired Algorithms",
  ISSN =         "0167-8191",
  DOI =          "doi:10.1016/j.parco.2010.07.001",
  URL =          "http://www.sciencedirect.com/science/article/B6V12-50J9GPR-1/2/e049c72f4c9e284bd1c2bdbf7c09f3aa",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, SBSE, Embedded systems design, Dynamic
                 memory management, Evolutionary computation,
                 Distributed simulation",
  abstract =     "For the last 30 years, several dynamic memory managers
                 (DMMs) have been proposed. Such DMMs include first fit,
                 best fit, segregated fit and buddy systems. Since the
                 performance, memory usage and energy consumption of
                 each DMM differs, software engineers often face
                 difficult choices in selecting the most suitable
                 approach for their applications. This issue has special
                 impact in the field of portable consumer embedded
                 systems, that must execute a limited amount of
                 multimedia applications (e.g., 3D games, video players,
                 signal processing software, etc.), demanding high
                 performance and extensive memory usage at a low energy
                 consumption. Recently, we have developed a novel
                 methodology based on genetic programming to
                 automatically design custom DMMs, optimising
                 performance, memory usage and energy consumption.
                 However, although this process is automatic and faster
                 than state-of-the-art optimizations, it demands
                 intensive computation, resulting in a time-consuming
                 process. Thus, parallel processing can be very useful
                 to enable to explore more solutions spending the same
                 time, as well as to implement new algorithms. In this
                 paper we present a novel parallel evolutionary
                 algorithm for DMMs optimisation in embedded systems,
                 based on the Discrete Event Specification (DEVS)
                 formalism over a Service Oriented Architecture (SOA)
                 framework. Parallelism significantly improves the
                 performance of the sequential exploration algorithm. On
                 the one hand, when the number of generations are the
                 same in both approaches, our parallel optimization
                 framework is able to reach a speed-up of 86.40times
                 when compared with other state-of-the-art approaches.
                 On the other, it improves the global quality (i.e.,
                 level of performance, low memory usage and low energy
                 consumption) of the final DMM obtained in a
                 36.36percent with respect to two well-known
                 general-purpose DMMs and two state-of-the-art
                 optimisation methodologies.",
}

Genetic Programming entries for Jose L Risco-Martin David Atienza Alonso J Manuel Colmenar Oscar Garnica

Citations