Multi-swarm particle swarm optimization based on CUDA for sparse reconstruction

Han，Wencheng1; Li，Hao1; Gong，Maoguo1; Li，Jianzhao1; Liu，Yiting1; Wang，Zhenkun2

2022-12-01

10.1016/j.swevo.2022.101153

Swarm and Evolutionary Computation   影响因子和分区

2210-6502

2210-6510

Particle swarm optimization (PSO) has been successfully applied to the sparse reconstruction problem and achieved good results. With the dimension of the problem increases, parallelizing PSO is an effective method to reduce its running time. This paper proposes a parallel PSO framework to solve the sparse reconstruction problem based on Compute Unified Device Architecture (CUDA) platform on Graphics Processing Unit (GPU). In order to further utilize potential computing resources in the GPU and improve the performance of the algorithm, each particle is launched by CUDA threads and the swarm is divided into multiple sub-swarms in CUDA streams. A local search strategy based on gradient and a particle coding strategy is combined into PSO for the purposes of achieving better reconstruction accuracy and accelerating convergence. In addition, in order to further optimize the parallel execution process of CUDA, the reduction algorithm and dynamic parallelism are incorporated into the proposed method. In the performance experiments, the proposed algorithm achieves a maximum speedup ratio of 25 times compared to the serial version in the signal reconstruction tasks.

CUDA stream Local search Particle swarm optimization Sparse reconstruction

SCI ; EI

英语

其他

National Natural Science Foundation of China[61906146];National Natural Science Foundation of China[62036006];Fundamental Research Funds for the Central Universities[JB210210];

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000862301600003

ELSEVIER

20223612700277

Computer graphics ; Computer graphics equipment ; Discrete wavelet transforms ; Local search (optimization) ; Particle swarm optimization (PSO) ; Program processors ; Signal reconstruction

Semiconductor Devices and Integrated Circuits:714.2 ; Information Theory and Signal Processing:716.1 ; Computer Circuits:721.3 ; Computer Peripheral Equipment:722.2 ; Computer Software, Data Handling and Applications:723 ; Computer Applications:723.5 ; Mathematical Transformations:921.3 ; Optimization Techniques:921.5

2-s2.0-85137155469

Scopus

1.School of Electronic Engineering,Xidian University,Xi'an,No. 2 South TaiBai Rood,710071,China
2.School of System Design and Intelligent Manufacturing,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China

Han，Wencheng,Li，Hao,Gong，Maoguo,et al. Multi-swarm particle swarm optimization based on CUDA for sparse reconstruction[J]. Swarm and Evolutionary Computation,2022,75.

Han，Wencheng,Li，Hao,Gong，Maoguo,Li，Jianzhao,Liu，Yiting,&Wang，Zhenkun.(2022).Multi-swarm particle swarm optimization based on CUDA for sparse reconstruction.Swarm and Evolutionary Computation,75.

Han，Wencheng,et al."Multi-swarm particle swarm optimization based on CUDA for sparse reconstruction".Swarm and Evolutionary Computation 75(2022).