318 / 2020-01-06 11:16:00
GPU-accelerated parallel optimization for sparse regularization
block successive convex approximation; LASSO
全文录用
Xingran Wang / TU Darmstadt, Germany
Tianyi Liu / Technische Universit鋞 Darmstadt, Germany
Minh Trinh-Hoang / TU Darmstadt, Germany
Marius Pesavento / Technische Universit鋞 Darmstadt & Merckstr. 25, Germany
We prove the concept that the block successive convex approximation algorithm can be configured in a flexible manner to adjust for implementations on modern parallel hardware architecture. A shuffle order update scheme and a all-close termination criterion are considered for efficient performance and convergence comparisons. Four different implementations are studied and compared. Simulation results on hardware show the condition of using shuffle order and selection of block numbers and implementations.
重要日期
  • 会议日期

    06月08日

    2020

    06月11日

    2020

  • 01月12日 2020

    初稿截稿日期

  • 04月15日 2020

    提前注册日期

  • 12月31日 2020

    注册截止日期

主办单位
IEEE Signal Processing Society
承办单位
Zhejiang University
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