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A FRAMEWORK FOR AUTOMATED GENERATION OF SPECIALIZED FUNCTION VARIANTS
Eﬃcient large-scale scientiﬁc computing requires eﬃcient code, yet optimizing code to render it eﬃcient simultaneously renders the code less readable, less maintainable, less portable, and requires detailed knowledge of low-level computer architecture, which the developers of scientiﬁc applications may lack. e necessary knowledge is subject to change over time as new architectures, such as GPGPU architectures like CUDA, which require very diﬀerent optimizations than CPU-targeted code, become more prominent. the development of scientiﬁc cloud computing means that developers may not even know what machine their code will be running on when they are developing it.
it is work takes steps towards automating the generation of code variants which are automatically optimized for both execution environment and input dataset. We demonstrate that augmenting an autotuning framework with a performance database which captures metadata about environment and input and performing decision tree learning over that data can help more fully automate the process of enhancing software performance.