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By retaining annotated performance profiles of each variant tested over the course of many autotuning runs of the same code across different hardware environments and different input datasets, we can apply machine learning algorithms to generate classifiers for runtime selection of code variants from a library, generate specialized variants, and potentially speed the process of autotuning by starting the search from a point predicted to be close to optimal. 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