Corpus ID: 229156162

Learning to Schedule Halide Pipelines for the GPU

@article{Anderson2020LearningTS,
  title={Learning to Schedule Halide Pipelines for the GPU},
  author={L. Anderson and Andrew Adams and Karima Ma and Tzu-Mao Li and Jonathan Ragan-Kelley},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.07145}
}

• L. Anderson, Andrew Adams, +2 authors Jonathan Ragan-Kelley
• Published 2020
• Computer Science
• ArXiv

We present a new algorithm to automatically generate high-performance GPU implementations of complex imaging and machine learning pipelines, directly from high-level Halide algorithm code. It is fully automatic, requiring no schedule templates or hand-optimized kernels, and it targets a diverse range of computations which is significantly broader than existing autoschedulers. We address the scalability challenge of extending previous approaches to schedule large real world programs, while… Expand

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