Cummins, C, Petoumenos, P, Wang, Z orcid.org/0000-0001-6157-0662 et al. (1 more author) (2017) End-to-End Deep Learning of Optimization Heuristics. In: 2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT). PACT 2017, 09-13 Sep 2017, Portland, Oregon, USA. IEEE , pp. 219-232. ISBN 978-1-5090-6764-0
Abstract
Accurate automatic optimization heuristics are necessary for dealing with the complexity and diversity of modern hardware and software. Machine learning is a proven technique for learning such heuristics, but its success is bound by the quality of the features used. These features must be hand crafted by developers through a combination of expert domain knowledge and trial and error. This makes the quality of the final model directly dependent on the skill and available time of the system architect. Our work introduces a better way for building heuristics. We develop a deep neural network that learns heuristics over raw code, entirely without using code features. The neural network simultaneously constructs appropriate representations of the code and learns how best to optimize, removing the need for manual feature creation. Further, we show that our neural nets can transfer learning from one optimization problem to another, improving the accuracy of new models, without the help of human experts. We compare the effectiveness of our automatically generated heuristics again stones with features hand-picked by experts. We examine two challenging tasks: predicting optimal mapping for heterogeneous parallelism and GPU thread coarsening factors. In 89% of the cases, the quality of our fully automatic heuristics matches or surpasses that of state-of-the-art predictive models using hand-crafted features, providing on average 14% and 12% more performance with no human effort expended on designing features.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Optimization Heuristics; Machine Learning; Compiler Optimizations; Heterogeneous Systems |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Sep 2019 12:06 |
Last Modified: | 11 Feb 2020 16:59 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/PACT.2017.24 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151219 |