A general approach to predict the performance order of TSP family problems

Abstract

Parallel computers provide an efficient and economical way to solve large-scale and/or time-constrained scientific, engineering, and industry problems. Consequently, there is a need to predict the performance order of both deterministic and non-deterministic parallel algorithms. The performance prediction of the traveling salesman problem (TSP) is a challenging problem because similar input data sets may cause significant variability in execution times. Parallel performance of data-dependent algorithms depends on the problem size, the number of processors, and other parameters. Discovering the main other parameters is the real key to obtain a good estimation of performance order. This paper presents a novel methodology to the problem of predicting the performance of a parallel algorithm for solving the TSP. The entire process explores data in search of patterns and/or relationships detecting the main parameters that affect performance. Then, it uses the measured values for this limited number of inputs to produce a multiple-linear-regression model. Finally, the regression equation allows for predicting how the algorithm will respond when given new input data sets. The preliminary experimental results are quite promising. © 2008 Springer-Verlag Berlin Heidelberg.