[Oral Presentation]GPU-accelerated parallel optimization for sparse regularization - THE ELEVENTH IEEE SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP (IEEE SAM2020)

GPU-accelerated parallel optimization for sparse regularization

ID：26 Submission ID：318 View Protection：ATTENDEE Updated Time：2020-08-05 10:16:59 Hits：488 Oral Presentation

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

Duration：20min

Session：[R] Regular Session » [R04] Computational 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