Towards Polyhedral Automatic DifferentiationDownload PDF

17 Sep 2019NeurIPS 2019 Workshop Program Transformations SubmissionReaders: Everyone
  • Keywords: Automatic Differentiation, Polyhedral Compiler, Parallelisation, Vectorisation
  • TL;DR: The polyhedral model could enable AD tools to compute more efficient derivatives of arbitrary models.
  • Abstract: Most Automatic Differentiation (AD) tools lack a way to explicitly represent or differentiate performance-critical and hardware-dependent constructs such as parallelism, vectorisation, or tiling. Machine-learning frameworks work around this by hiding implementation details from the AD process, but lack the generality of general-purpose programming languages. Instead, this talk discusses the polyhedral model as a way for general-purpose AD tools to preserve performance tweaks through the differentiation process.
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