Optimisation of LHCb Applications for Multi- and Manycore Job Submission

Nowadays, the Worldwide LHC Computing Grid mainly consists of multi- and manycore processors. The thesis investigates how such resources can be used more efficiently at the example of the LHCb experiment. It analyses how to improve software in terms of memory requirements and concurrency. The research involves the implementation of a moldable job scheduler and a supervised learning algorithm which helps to better predict LHCb workloads.