Grammatical Evolution + Multi-Cores = Automatic Parallel Programming!

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{Chennupati:thesis,
  author =       "Gopinath Chennupati",
  title =        "Grammatical Evolution + Multi-Cores = Automatic
                 Parallel Programming!",
  school =       "CSIS Department, University of Limerick",
  year =         "2015",
  month =        oct,
  address =      "Ireland",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution",
  URL =          "http://www.skynet.ie/~cgnath/docs/thesis.pdf",
  size =         "311 pages",
  abstract =     "Multi-core processors are shared memory
                 multiprocessors integrated on a single chip which offer
                 significantly higher processing power than traditional,
                 single core processors. However, as the number of cores
                 available on a single processor increases, efficiently
                 programming them becomes increasingly more complex,
                 often to the point where the limiting factor in
                 speeding up tasks is the software.

                 This thesis presents Grammatical Automatic Parallel
                 Programming (GAPP) which uses Grammatical Evolution to
                 automatically generate natively parallel code on
                 multi-core processors by directly embedding GAPP OpenMP
                 parallelization directives in problem-specific Context
                 Free Grammars. As a result, it obviates the need for
                 programmers to think in a parallel manner while still
                 letting them produce parallel code.

                 We first perform a thorough analysis on the
                 computational complexity of Grammatical Evolution using
                 standard benchmark problems. This analysis results in
                 an interesting experiment which produces a system
                 capable of predicting on-the-fly the likelihood of a
                 particular GE run being successful.

                 A number of difficult proof of concept problems are
                 examined in evaluating GAPP. The performance of the
                 system on these informs the further optimization of
                 both the design of grammars and fitness function to
                 extract further parallelism. We demonstrate a
                 surprising side effect of uncontrolled parallelism,
                 which leads to the under-use of the cores. This is
                 addressed through the automatic generation of programs
                 with controlled degree of parallelism. In this case,
                 the automatically generated programs adapt to the
                 number of cores on which they are scheduled to
                 execute.

                 Finally, GAPP is applied to Automatic Lockless
                 Programming, an enormously difficult design problem,
                 resulting in parallel code guaranteed to avoid locks on
                 shared resources, thereby further optimizing the
                 execution time. We then draw conclusions and make
                 future recommendations on the use of evolutionary
                 systems in the generation of highly constrained
                 parallel code.",
  notes =        "Supervisor: Conor Ryan

                 ",
}

Genetic Programming entries for Gopinath Chennupati

Citations