面向CPU、GPU多目标机的混合求解器设计与实现  

Design and Implementation of A Hybrid Solver on CPU and GPU Multi-target Machines

作　　者：马琳 张雪松[1] 雷新丽 包铁[1] Ma Lin;Zhang Xuesong;Lei Xinlin;Bao Tie(College of Computer Science and Technology,Jilin University,Changchun 130012,China)

机构地区：[1]吉林大学计算机科学与技术学院,吉林长春130012

出　　处：《系统仿真学报》2022年第4期670-678,共9页Journal of System Simulation

基　　金：国家重点研发计划(2018YFB1701600)。

摘　　要：传统常微分方程的并行求解方法主要包括面向任务的并行和面向方法的并行,但是这两种求解算法,只能利用CPU,或者只能面向同质形式的ODE(ordinary differential equations)簇,存在严重不足。以RIDC(revisionist integral deferred correction)算法为基础,设计了一种面向CPU、GPU多目标机的混合求解器,基于流水线形式求解微分方程组,实现了单个方程组的内部和不同方程组之间的并行计算,进而能够充分发挥GPU的多核优势,有利于计算节点内部的负载均衡。仿真实验验证了框架的效率、准确率和精准度。The traditional parallel solving methods for the ordinary differential equations mainly include the task-oriented parallelism and the method-oriented parallelism.However,these two solving algorithms have serious shortcomings,which can only use CPU resource or just design for the homogeneous form of ODE(ordinary differential equations)clusters.By using RIDC(revisionist integral deferred correction)algorithm,a hybrid solver based on CPU and GPU multi-target machine is designed,which solves the differential equation system based on the pipeline form.Meanwhile,the parallel calculation within a single equation group and between the different equation groups is realized,which can give full play to the multi-core advantage of GPU,and also help to balance the load inside the computing node.The simulation experiments verify the efficiency,accuracy and precision of the framework.

关 键 词：常微分方程 混合求解 多目标机 CPU GPU 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]