SC13 Home > SC13 Schedule > SC13 Presentation - Enhancing Learning-Based Autotuning with Composite and Diagnostic Feature Vectors

SCHEDULE: NOV 16-22, 2013

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Enhancing Learning-Based Autotuning with Composite and Diagnostic Feature Vectors

SESSION: Research Poster Reception

EVENT TYPE: Posters, Electronic Posters, and Education Posters

TIME: 5:15PM - 7:00PM

AUTHOR(S):Saami Rahman, Richard Hay, Mario A. Gutierrez, Apan Qasem

ROOM:Mile High Pre-Function

The use of machine learning techniques has emerged as a promising strategy for autotuning. Central to the success of such learned heuristics is the construction of a feature vector that accurately captures program behavior. Although the salient features of certain domain specific kernels are well understood, at least in principle, automatically deriving suitable features for general numerical applications remains a significant challenge. This poster presents a learning-based autotuning system that introduces two new classes of features that encapsulate key architecture-sensitive performance characteristics for a broad range of scientific applications. The first class of features are high-level, composite and derived from compiler models for data locality and parallelization. The second class of features is a series of synthesized and normalized HW performance counter values that diagnose causes of program inefficiencies. Preliminary experimental results show that the enhanced feature vectors increase prediction accuracy by as much as 23% for several learning algorithms.

Chair/Author Details:

Saami Rahman - Texas State University

Richard Hay - Texas State University

Mario A. Gutierrez - Texas State University

Apan Qasem - Texas State University

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