基于有序回归的模板自调整:扩展摘要

Biagio Cosenza, J. Durillo, Stefano Ermon, B. Juurlink
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引用次数: 3

摘要

随着当今计算机体系结构性能的不断提高,硬件复杂性也出现了前所未有的增加。不幸的是,这会导致难以调优的软件,并必然导致潜在峰值性能与实际性能之间的差距。自动调优是一种新兴的方法,可以帮助程序员管理这种复杂性。然而,最先进的自动调谐器是有限的:它们要么需要很长的调谐时间,例如,由于迭代搜索,要么由于所使用的监督机器学习(ML)方法的限制,无法解决问题的复杂性。特别是,利用分类算法(如神经网络和支持向量机)的传统ML自动调整方法在捕获大型搜索空间的所有特征方面面临困难。我们提出了一种基于结构学习的自动调优方法:将调优问题表述为一个版本排序预测模型,并使用有序回归进行求解。我们展示了它在一个众所周知的自动调优问题上的潜力:模板计算。我们比较了最先进的迭代编译方法和我们的有序回归方法,并根据肯德尔秩相关系数分析了获得的排名的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stencil Autotuning with Ordinal Regression: Extended Abstract
The increasing performance of today's computer architecture comes with an unprecedented augment of hardware complexity. Unfortunately this results in difficult-to-tune software and consequentially in a gap between the potential peak performance and the actual performance. Automatic tuning is an emerging approach that assists the programmer in managing this complexity. State-of-the-art autotuners are limited, though: they either require long tuning times, e.g., due to iterative searches, or cannot tackle the complexity of the problem due to the limitation of the supervised machine learning (ML) methodologies used. In particular, 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. We propose a new way of performing automatic tuning based on structural learning: the tuning problem is formulated as a version ranking prediction modeling and solved using ordinal regression. We demonstrate its potential on a well-known autotuning problem: stencil computations. We compare state-of-the-art iterative compilation methods with our ordinal regression approach and analyze the quality of the obtained ranking in terms of Kendall rank correlation coefficients.
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