GPU-accelerated parallel optimization for sparse regularization
ID:26 View Protection:ATTENDEE Updated Time:2020-08-05 10:16:59 Hits:479 Oral Presentation

Start Time:2020-06-09 15:20(Asia/Shanghai)

Duration:20min

Session:R Regular Session » R04Computational and Optimization Techniques for Multi-Channel Processing

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Abstract
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.
Keywords
block successive convex approximation; LASSO
Speaker
Xingran Wang
TU Darmstadt, Germany

Submission Author
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
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Important Date
  • Conference Date

    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

    Early Bird Registration

Sponsored By
IEEE Signal Processing Society
Organized By
Zhejiang University
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