Minimizing the cost of iterative compilation with active learning

Ogilvie, William and Petoumenos, Pavlos and Wang, Zheng and Leather, Hugh (2017) Minimizing the cost of iterative compilation with active learning. In: International Symposium on Code Generationand Optimization (CGO), 2017. IEEE, pp. 245-256. ISBN 9781509049325

[img]
Preview
PDF (paper (6))
paper_6_.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (749kB)

Abstract

Since performance is not portable between platforms, engineers must fine-tune heuristics for each processor in turn. This is such a laborious task that high-profile compilers, supporting many architectures, cannot keep up with hardware innovation and are actually out-of-date. Iterative compilation driven by machine learning has been shown to be efficient at generating portable optimization models automatically. However, good quality models require costly, repetitive, and extensive training which greatly hinders the wide adoption of this powerful technique. In this work, we show that much of this cost is spent collecting training data, runtime measurements for different optimization decisions, which contribute little to the final heuristic. Current implementations evaluate randomly chosen, often redundant, training examples a pre-configured, almost always excessive, number of times – a large source of wasted effort. Our approach optimizes not only the selection of training examples but also the number of samples per example, independently. To evaluate, we construct 11 high-quality models which use a combination of optimization settings to predict the runtime of benchmarks from the SPAPT suite. Our novel, broadly applicable, methodology is able to reduce the training overhead by up to 26x compared to an approach with a fixed number of sample runs, transforming what is potentially months of work into days.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Subjects:
ID Code:
83483
Deposited By:
Deposited On:
06 Dec 2016 10:04
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Dec 2020 09:42