Current automatic and empirical performance modelling approaches are heavily challenged by large cluster programs. Especially programs with multiple performance relevant parameters are solvable only with high effort, due to the large search space of performance functions, spanned by combining the performance relevant parameters with simple arithmetic operations. The search space is, therefore, increasing extensively with more parameters. Current empirical performance modelling tools like ExtraP are struggling with large search spaces but are able to deal with them. Actually, ExtraP limits its search space to simple functions, which were covering most of the complexity functions of real-world programs, excluding quadratic or cubic functions, to downsize the search space and decrease the modelling time. To overcome the problem of exploding function search spaces, this work evaluates the usage of Deep Neural Networks to predict a rough complexity class of the performance function and therefore enables the option to significantly refine the performance modeller’s search space while also covering more function types. The deep learning models are trained and evaluated on synthetic datasets with two and three parameters e.g. amount of processors and problem size. Further, this work introduces a multi-parameter approach, which utilizes pre-trained models dealing with fewer parameters, to support the higher parameter model. Evaluation of the deep learning models reaches an accuracy of 98.6% for predicting the correct complexity class of performance functions with 2 performance relevant parameters and 86% with 3 parameters.
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