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Automated Parallelization of Non-Uniform Convolutions on Chip Multiprocessors

Automated Parallelization of Non-Uniform Convolutions on Chip Multiprocessors. Yuanrui Zhang, Mahmut Kandemir Computer Science and Engineering, The Pennsylvania State University. Nikos Pitsianis, Xiaobai Sun Computer Science, Duke University. Non-Uniform FFT Local Convolution. Local Support.

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Automated Parallelization of Non-Uniform Convolutions on Chip Multiprocessors

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  1. Automated Parallelization of Non-Uniform Convolutions on Chip Multiprocessors Yuanrui Zhang, Mahmut Kandemir Computer Science and Engineering, The Pennsylvania State University. Nikos Pitsianis, Xiaobai Sun Computer Science, Duke University.

  2. Non-Uniform FFT Local Convolution Local Support sources targets • While FFTs are well implemented by FFTW, we concentrate on accelerating the parallel convolution step on multicores. • Geometric tiling can enhance data locality and reuse for the non-uniform local convolution, a matrix-vector product with an irregular and sparse matrix. • Multicores have different on-chip memory hierarchies and characteristics.

  3. Architecture Aware Hierarchical Geometric Tiling and Parallel Scheduling • Hierarchical tiling according to memory hierarchy and sizes • Neighborhood tile traversing order • Block distribution • Try to take care of data sharing and locality at all levels of cache 1 2 19 18 6 3 16 17 7 5 4 On-chip memory abstraction 8 14 15 13 12 9 10 11

  4. Preliminary Evaluation

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