基于OpenCL的自动微分并行实现及其应用  

Automatic Differentiation Based on OpenCL Parallel Computing and Its Application

作　　者：叶爱芬[1] 王环[2] 沈雁[3] Ye Aifen;Wang Huan;Shen Yan(Zhejiang Dongfang Polytechnic, Wenzhou325000, China;National-Local Joint Engineering Laboratory of Electrical Digital Design Technology, Wenzhou University, Wenzhou325035, China;College of Electrical and Information Engineering, Hunan University, Changsha410082, China)

机构地区：[1]浙江东方职业技术学院电气自动化研究室,浙江温州325035 [2]温州大学电气数字化设计技术国家地方联合工程实验室,浙江温州325035 [3]湖南大学电气与信息工程学院,湖南长沙410082

出　　处：《计算机测量与控制》2019年第5期155-159,共5页Computer Measurement &Control

基　　金：浙江省自然科学基金重点项目(LZ16E050002)

摘　　要：针对如光束平差这样的大规模优化问题,实现基于OpenCL的并行化自动微分;采用更有效的反向计算模式,实现对多参数函数的导数计算;在OpenCL框架下,主机端完成C/C++形式的函数构建以及基于拓扑排序的计算序列生成,设备端按照计算序列完成函数值以及导数的并行计算;测试结果表明,将实现的自动微分应用于光束平差的雅可比矩阵计算后,相比于采用OpenMP的Ceres Solver,运行速度提高了约3.6倍。A parallelized implementation of automatic differentiation that derives from the problem of bundle adjustment is proposed,which is based on OpenCL parallel computing framework.Reverse mode of automatic differentiation is more efficient to compute the derivatives of functions with multiple parameters,which is the case of computing the Jacobian matrix in bundle adjustment problem.Under the framework of OpenCL,C/C++style function construction and topological sorting based computational sequence generation are implemented on the host side.On the device side,function values and derivatives are computed in parallel according to computational sequence.Large scale bundle adjustment datasets are used to evaluate the proposed implementation.The result shows that our implementation runs about 3.6 times faster than Ceres Solver which utilizes OpenMP parallel programming model.

关 键 词：自动微分 并行计算 OPENCL 

分 类 号：O246[理学—计算数学] TP311.1[理学—数学]