Predicting the Flexibility of Dynamic Loop Scheduling Using an Artificial Neural Network
Srishti Srivastava, Brandon Malone, Nitin Sukhija, Ioana Banicescu, Florina M. Ciorba · 2013
In this paper, an artificial neural network (ANN) model is proposed to predict the flexibility (or robustness against system load fluctuations in heterogeneous computing systems) of dynamic loop scheduling (DLS) methods. The multilayer perceptron (MLP) ANN model has been used to predict the degree of robustness of a DLS method, given specific values for the problem size, the system size, and the characteristics of the system load fluctuations as a compound effect of the variations in the application's iteration execution times and the processor availabilities. The developed MLP ANN model can be useful in an effective selection of the most robust DLS technique for scheduling a certain type of scientific application onto a given set of non-dedicated heterogeneous processors, when their system load is expected to fluctuate unpredictably during the application's runtime.