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Main Title: Autotuning Stencil Computations with Structural Ordinal Regression Learning
Author(s): Cosenza, Biagio
Durillo, Juan J.
Ermon, Stefano
Juurlink, Ben
Type: Conference Object
Language Code: en
Abstract: Stencil computations expose a large and complex space of equivalent implementations. These computations often rely on autotuning techniques, based on iterative compilation or machine learning (ML), to achieve high performance. Iterative compilation autotuning is a challenging and time-consuming task that may be unaffordable in many scenarios. Meanwhile, traditional ML autotuning approaches exploiting classification algorithms (such as neural networks and support vector machines) face difficulties in capturing all features of large search spaces. This paper proposes a new way of automatically tuning stencil computations based on structural learning. By organizing the training data in a set of partially-sorted samples (i.e., rankings), the problem is formulated as a ranking prediction model, which translates to an ordinal regression problem. Our approach can be coupled with an iterative compilation method or used as a standalone autotuner. We demonstrate its potential by comparing it with state-of-the-art iterative compilation methods on a set of nine stencil codes and by analyzing the quality of the obtained ranking in terms of Kendall rank correlation coefficients.
Issue Date: 2017
Date Available: 5-Jun-2018
DDC Class: 004 Datenverarbeitung; Informatik
Subject(s): automatic tuning
structural SVMs
stencil computations
Proceedings Title: 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Publisher: IEEE
Publisher Place: New York
Publisher DOI: 10.1109/IPDPS.2017.102
Page Start: 287
Page End: 296
ISBN: 978-1-5386-3914-6
ISSN: 1530-2075
Appears in Collections:FG Architektur eingebetteter Systeme » Publications

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