Performance and power prediction on heterogeneous systems using statistical methods


Heterogeneous systems provide high computing performance, combining low cost and low power consumption. These systems include various computational resources with different architectures, such as CPUs, GPUs, DSPs or FPGAs. It is crucial to have full knowledge of these architectures, but also of the programming models used in order to increase the performance on a heterogeneous system.

One way to achieve this goal, is the prediction of the execution time on the different computational resources, using statistical values which we collect with the use of hardware counters. The purpose of this thesis is to increase the performance of a heterogeneous system using the data we collected by training a statistical model which will predict the execution time. Further goal is to use this prediction model inside a run-time scheduler which will migrate the running application in order to decrease the execution time and increase the overall performance.

We used various statistical models, such as linear regression, neural networks and random forests and we predicted the execution time to Intel CPUs and NVIDIA GPUs, with different levels of success.

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