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)


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

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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.
block successive convex approximation; LASSO
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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    Jun 08



    Jun 11


  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

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Sponsored By
IEEE Signal Processing Society
Organized By
Zhejiang University
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