SCCS Colloquium - Oct 28, 2020: Difference between revisions

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The AutoPas library is capable of simulating computationally intensive molecular dynamic simulations. To reduce the run time, it provides many configurations to select the fastest for the specific scenario. AutoPas tries to achieve this task by testing the whole search space frequently. In this thesis, a new approach is implemented that uses extrapolation to predict the efficiency and only tests the best predictions to avoid inefficient configurations. The predicting strategy can reduce the run time by up to 74%.
The AutoPas library is capable of simulating computationally intensive molecular dynamic simulations. To reduce the run time, it provides many configurations to select the fastest for the specific scenario. AutoPas tries to achieve this task by testing the whole search space frequently. In this thesis, a new approach is implemented that uses extrapolation to predict the efficiency and only tests the best predictions to avoid inefficient configurations. The predicting strategy can reduce the run time by up to 74%.
'''Keywords:''' Molecular Dynamics, AutoPas, Auto-Tuning, Extrapolation

Revision as of 07:53, 14 September 2020

Date: Oct 28, 2020
Room: Online (BBB)
Time: 15:00 - 16:00

Julian Pelloth: Implementing a predictive tuning strategy in AutoPas using extrapolation

Bachelor's thesis submission talk. Julian is advised by Fabio Gratl and Steffen Seckler.

The AutoPas library is capable of simulating computationally intensive molecular dynamic simulations. To reduce the run time, it provides many configurations to select the fastest for the specific scenario. AutoPas tries to achieve this task by testing the whole search space frequently. In this thesis, a new approach is implemented that uses extrapolation to predict the efficiency and only tests the best predictions to avoid inefficient configurations. The predicting strategy can reduce the run time by up to 74%.

Keywords: Molecular Dynamics, AutoPas, Auto-Tuning, Extrapolation