MLTUNE: A Tool-Chain for Automating the Workﬂow of Machine-Learning Based Performance Tuning
Authors: Biplab Kumar Saha (Texas State University), Saami Rahman (Texas State University), Apan Qasem (Texas State University)
Abstract: Recent interest in machine learning-based methods have produced several sophisticated models for performance optimization and workload characterization. Generally, these models are sensitive to architectural parameters and are most effective when trained on the target platform. Training of these models, however, is a fairly involved process. It requires knowledge of statistical techniques that the end-users of such tools may not possess. This poster presents MLTUNE, a tool-chain that automates the workflow for developing machine learning algorithms for performance tuning. Leveraging existing open-source software, the tool-chain provides automated mechanisms for sample generation, dynamic feature extraction, feature selection, data labeling, validation and model selection. MLTUNE can also be used by performance engineers to build their own learning models. The poster highlights the key design features of MLTUNE, which sacrifices some sophistication for generalization and automation. The system's applicability is demonstrated with an auto-generated model for predicting profitable affinity configurations for parallel workloads.
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