A Framework for Automated Generation of Specialized Function Variants

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dc.contributor.advisor Malony, Allen en_US
dc.contributor.author Chaimov, Nicholas en_US
dc.creator Chaimov, Nicholas en_US
dc.date.accessioned 2012-10-26T04:04:56Z
dc.date.available 2012-10-26T04:04:56Z
dc.date.issued 2012
dc.identifier.uri http://hdl.handle.net/1794/12434
dc.description.abstract 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. The 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. This 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. en_US
dc.language.iso en_US en_US
dc.publisher University of Oregon en_US
dc.rights All Rights Reserved. en_US
dc.subject autotuning en_US
dc.subject optimization en_US
dc.subject specialization en_US
dc.title A Framework for Automated Generation of Specialized Function Variants en_US
dc.type Electronic Thesis or Dissertation en_US

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