Optimization of Clutter Simulation Based on GPU

被引：4

作者：

Xu, Guowei ^{[1
,2
,3
]}

Hao, Fangrui ^{[1
]}

Chen, Jian ^{[1
]}

Xiu, Chunbo ^{[1
,2
]}

机构：

[1] Tiangong Univ, Sch Elect Engn & Automat, Tianjin 300387, Peoples R China

[2] Tiangong Univ, Key Lab Adv Elect Engn & Energy Technol, Tianjin 300387, Peoples R China

[3] Tiangong Univ, Natl Demonstrat Ctr Expt Engn Training Educ, Tianjin 300387, Peoples R China

来源：

IEEE ACCESS | 2020年 / 8卷 / 08期

关键词：

GPU parallel operation; clutter model; fine-grained optimization; convolution calculation;

D O I：

10.1109/ACCESS.2020.2972941

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Weibull clutter is used as an example in this paper. Based on the serial parallel analysis of Zero-memory non-linear transformation;s Weibull distributed clutter algorithm, fine-grained optimization is performed. The fine-grained part uses the cuBLAS library to optimize the performance of convolution calculations. Compared with CUDA shared memory convolution method and GPU parallel matrix multiplication convolution method, its computational performance can be significantly improved under a large amount of data. Simulation results show that the Zero-memory non-linear transformation;s Weibull distributed clutter simulation method is optimized and accelerated. The real-time performance of clutter data is significantly improved and its acceleration effect will be better as the amount of clutter data increases. It turns out that through fine-grained optimization, the performance of convolution calculations with large amounts of data is improved.

引用

页码：29501 / 29507

页数：7

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