Parallel Performance Modeling using a Genetic Programming-based Error Correction Procedure

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Kavitha Raghavachar and G. Mahinthakumar and 
                 Patrick H. Worley and Emily M. Zechman and S. Ranji Ranjithan",
  title =        "Parallel Performance Modeling using a Genetic
                 Programming-based Error Correction Procedure",
  journal =      "Simulation",
  year =         "2007",
  volume =       "83",
  number =       "7",
  pages =        "515--527",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Error
                 correction procedure, performance modeling",
  DOI =          "doi:10.1177/0037549707084691",
  size =         "14 pages",
  abstract =     "Performance models of high performance computing (HPC)
                 applications are important for several reasons. First,
                 they provide insight to designers of HPC systems on the
                 role of subsystems such as the processor or the network
                 in determining application performance. Second, they
                 allow HPC centers more accurately to target
                 procurements to resource requirements. Third, they can
                 be used to identify application performance bottlenecks
                 and to provide insights about scalability issues. The
                 suitability of a performance model, however, for a
                 particular performance investigation is a function of
                 both the accuracy and the cost of the model.

                 A semi-empirical model previously published by the
                 authors for an astrophysics application was shown to be
                 inaccurate when predicting communication cost for large
                 numbers of processors. It is hypothesized that this
                 deficiency is due to the inability of the model
                 adequately to capture communication contention
                 (threshold effects) as well as other unmodeled
                 components such as noise and I/O contention. In this
                 paper we present a new approach to capture these
                 unknown features to improve the predictive capabilities
                 of the model. This approach uses a systematic model
                 error-correction procedure that uses evolutionary
                 algorithms to find an error correction term to augment
                 the existing model. Four variations of this procedure
                 were investigated and all were shown to produce better
                 results than the original model. Successful
                 cross-platform application of this approach showed that
                 it adequately captures machine dependent
                 characteristics. This approach was then successfully
                 demonstrated for a second application, further showing
                 its versatility.",
  notes =        "GYRO B1-std, B2-cy and B3-gtc problems (fitting)",
  bibdate =      "2009-09-28",
  bibsource =    "DBLP,

Genetic Programming entries for Kavitha Raghavachar G (Kumar) Mahinthakumar Patrick H Worley Emily M Zechman S Ranji Ranjithan