Can Flexible Multi-Core Scheduling Help to Execute Machine Learning Algorithms Resource-Efficiently?
Machine learning techniques such as model-based optimization are frequently used to solve expensive problems. Since a sequential execution of these algorithms is time-intensive due to the problem complexity, several attempts have been made to parallelize existing approaches. However, no state-of-the-art technique is able to efficiently exploit the full potential of multi-core architectures up to now. In this work, we propose a flexible extension to the Resource-Aware Model-Based Optimization framework (RAMBO) adopting multi-core scheduling techniques, which allows to use the available resources in a more efficient way and thus reduces the time required to solve expensive optimization problems.
- Published in:
SCOPES '19: Proceedings of the 22nd International Workshop on Software and Compilers for Embedded Systems Workshop on Software and Compilers for Embedded Systems (SCOPES) - Type:
Inproceedings - Authors:
H. Kotthaus, L. Schönberger, A. Lang, J. Chen, P. Marwedel - Year:
2019
Citation information
H. Kotthaus, L. Schönberger, A. Lang, J. Chen, P. Marwedel: Can Flexible Multi-Core Scheduling Help to Execute Machine Learning Algorithms Resource-Efficiently?, Workshop on Software and Compilers for Embedded Systems (SCOPES), SCOPES '19: Proceedings of the 22nd International Workshop on Software and Compilers for Embedded Systems, 2019, https://doi.org/10.1145/3323439.3323986, Kotthaus.etal.2019,
@Inproceedings{Kotthaus.etal.2019,
author={H. Kotthaus, L. Schönberger, A. Lang, J. Chen, P. Marwedel},
title={Can Flexible Multi-Core Scheduling Help to Execute Machine Learning Algorithms Resource-Efficiently?},
booktitle={Workshop on Software and Compilers for Embedded Systems (SCOPES)},
journal={SCOPES '19: Proceedings of the 22nd International Workshop on Software and Compilers for Embedded Systems},
url={https://doi.org/10.1145/3323439.3323986},
year={2019},
abstract={Machine learning techniques such as model-based optimization are frequently used to solve expensive problems. Since a sequential execution of these algorithms is time-intensive due to the problem complexity, several attempts have been made to parallelize existing approaches. However, no state-of-the-art technique is able to efficiently exploit the full potential of multi-core architectures up to...}}