Xinyue Chu1, Jiaquan Gao1,*, Bo Sheng2
Computer Systems Science and Engineering, Vol.38, No.3, pp. 305-320, 2021, DOI:10.32604/csse.2021.017144
- 19 May 2021
Abstract Given that the concurrent L1-minimization (L1-min) problem is often required in some real applications, we investigate how to solve it in parallel on GPUs in this paper. First, we propose a novel self-adaptive warp implementation of the matrix-vector multiplication (Ax) and a novel self-adaptive thread implementation of the matrix-vector multiplication (ATx), respectively, on the GPU. The vector-operation and inner-product decision trees are adopted to choose the optimal vector-operation and inner-product kernels for vectors of any size. Second, based on the above proposed kernels, the iterative shrinkage-thresholding algorithm is utilized to present two concurrent L1-min solvers from More >
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