Anton Lokhmotov
Grigori Fursin
ABSTRACT
Autotuning is a popular technique to ensure performance portability for important algorithms such as BLAS, FFT and DNN across the ever evolving software and hardware stack. Unfortunately, when performed on a single machine, autotuning can explore only a tiny fraction of the ever growing and non-linear optimization spaces and thus can easily miss optimal solutions. We propose to practically solve this problem with the help of the community using the open-source Collective Knowledge framework (CK). We have customized the universal multi-objective autotuning engine of CK to optimize the local work size and other parameters of OpenCL workloads across diverse inputs and devices. Optimal solutions (with speed increases of up to 20x and energy savings of up to 30% over the default configurations) are preserved in the open repository of optimization knowledge at http://cknowledge.org/repo.
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Digital Library
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