Tools are demanded that help users of HPC-facilities to implement efficient input/output (I/O) in their programs. It is difficult to find the best access parameters and patterns due to complex parallel storage systems. To develop tools which support the implementation of efficient I/O a computational model of the storage system is key. For single hard disk systems such a model can be derived analytically [1]; however, for the complex storage system of a super computer these models become too difficult to configure [2]. Therefore we searched for good predictors of I/O performance using a machine learning approach with artificial neural networks (ANNs). A hypothesis was then proposed: The I/O-path significantly influences the time needed to access a file. In our analysis we used ANNs with different input information for the prediction of access times. To use I/O-paths as input for the ANNs, we developed a method, which approximates the different I/O-paths the storage system used during a benchmark-test. This method utilizes error classes.

@misc{PIIHUANNSK16,
	author	 = {Jan Fabian Schmid and Julian Kunkel},
	title	 = {{Predicting I/O-performance in HPC using Artificial Neural Networks}},
	year	 = {2016},
	month	 = {21},
	location	 = {Frankfurt},
	activity	 = {ISC High Performance 2015},
	abstract	 = {Tools are demanded that help users of HPC-facilities to implement efficient input/output (I/O) in their programs. It is difficult to find the best access parameters and patterns due to complex parallel storage systems. To develop tools which support the implementation of efficient I/O a computational model of the storage system is key. For single hard disk systems such a model can be derived analytically [1]; however, for the complex storage system of a super computer these models become too difficult to configure [2]. Therefore we searched for good predictors of I/O performance using a machine learning approach with artificial neural networks (ANNs). A hypothesis was then proposed: The I/O-path significantly influences the time needed to access a file. In our analysis we used ANNs with different input information for the prediction of access times. To use I/O-paths as input for the ANNs, we developed a method, which approximates the different I/O-paths the storage system used during a benchmark-test. This method utilizes error classes.},
}