Parallel Implementation of Neural Network Model for Quadratic Programming on GPGPU

被引：0
|
作者：
Ma, Anguo [1 ]
Tan, Caifeng [1 ]
Cheng, Yu [1 ]
机构：
[1] Natl Univ Def Technol, Sch Comp Sci, Changsha, Hunan, Peoples R China
来源：
2011 AASRI CONFERENCE ON INFORMATION TECHNOLOGY AND ECONOMIC DEVELOPMENT (AASRI-ITED 2011), VOL 1 | 2011年
关键词：
artificial neural network; quadratic programming; GPGPU; OPTIMIZATION;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
GPGPU is characterized by high computational power and large scale parallel computing capability, and is widely used in the fields of high performance computing and scientific computing nowadays. Quadratic programming problem has high computing density and parallelism. BP neural network model for quadratic programming problem is implemented in parallel on GPGPU, and the effect of problem scale on acceleration is illustrated. The experimental results show that 4x speedup can be achieved on GPGPU.
引用
收藏
页码：241 / 245
页数：5
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