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.
This work was supported by the CICYT-Spain under contracts TIN 2004-03388 and TIN 2007-64974.
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Fritzsche, P., Rexachs, D., Luque, E. (2008). A General Approach to Predict the Performance Order of TSP Family Problems. In: Bourgeois, A.G., Zheng, S.Q. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2008. Lecture Notes in Computer Science, vol 5022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69501-1_12
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DOI: https://doi.org/10.1007/978-3-540-69501-1_12
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