التفاصيل البيبلوغرافية
| العنوان: |
Multitask and Transfer Learning for Autotuning Exascale Applications |
| المؤلفون: |
Sid-Lakhdar, Wissam M., Aznaveh, Mohsen Mahmoudi, Li, Xiaoye S., Demmel, James W. |
| سنة النشر: |
2019 |
| المجموعة: |
ArXiv.org (Cornell University Library) |
| مصطلحات موضوعية: |
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Statistics - Machine Learning |
| الوصف: |
Multitask learning and transfer learning have proven to be useful in the field of machine learning when additional knowledge is available to help a prediction task. We aim at deriving methods following these paradigms for use in autotuning, where the goal is to find the optimal performance parameters of an application treated as a black-box function. We show comparative results with state-of-the-art autotuning techniques. For instance, we observe an average $1.5x$ improvement of the application runtime compared to the OpenTuner and HpBandSter autotuners. We explain how our approaches can be more suitable than some state-of-the-art autotuners for the tuning of any application in general and of expensive exascale applications in particular. |
| نوع الوثيقة: |
text |
| اللغة: |
unknown |
| العلاقة: |
http://arxiv.org/abs/1908.05792Test |
| الإتاحة: |
http://arxiv.org/abs/1908.05792Test |
| رقم الانضمام: |
edsbas.AE627217 |
| قاعدة البيانات: |
BASE |
