浮动车数据(floating car data, FCD)技术是大规模城市路网交通流实时采集的有效方法.城市交通的动态诱导和控制需要对海量FCD进行快速处理.鉴于此,提出了FCD并行计算的动态任务调度方法.针对FCD数据包计算时间的不确定性和动态性,根据...浮动车数据(floating car data, FCD)技术是大规模城市路网交通流实时采集的有效方法.城市交通的动态诱导和控制需要对海量FCD进行快速处理.鉴于此,提出了FCD并行计算的动态任务调度方法.针对FCD数据包计算时间的不确定性和动态性,根据计算节点的处理能力进行数据包的动态分割,在处理过程中,采用动态任务分配策略以实现计算节点的同步.该方法在龙芯国产大数据一体机平台上进行了实现,并采用现场FCD数据进行了实验验证,结果表明,该方法较轮询和Min-Min调度算法,显著地提高了并行处理的性能.展开更多
随着多核处理器片上集成核数的不断增多,并行任务的调度能力越来越成为制约性能提升的关键因素。文章设计一种面向异构多核计算系统的动态任务调度控制器,主要实现动态监控处理单元的负载情况、动态任务唤醒、乱序任务发射、任务写回安...随着多核处理器片上集成核数的不断增多,并行任务的调度能力越来越成为制约性能提升的关键因素。文章设计一种面向异构多核计算系统的动态任务调度控制器,主要实现动态监控处理单元的负载情况、动态任务唤醒、乱序任务发射、任务写回安全管理等功能;研究一种降低计算任务结果数据回写双倍数据速率(double data rate,DDR)外存储器次数的方法,大幅节省了访存开销,进一步提升了计算性能。仿真及性能测试显示,在典型应用场景下,与已有的无动态调度功能的任务发射控制器相比,实现了显示并行化编程向任务并行的自动化控制过渡,编程友好度显著提高,在不同类型的测试案例中,分别提升了11.3%~37.9%的计算性能。展开更多
In this paper,we consider a multi-UAV surveillance scenario where a team of unmanned aerial vehicles(UAVs)synchronously covers an area for monitoring the ground conditions.In this scenario,we adopt the leader-follower...In this paper,we consider a multi-UAV surveillance scenario where a team of unmanned aerial vehicles(UAVs)synchronously covers an area for monitoring the ground conditions.In this scenario,we adopt the leader-follower control mode and propose a modified Lyapunov guidance vector field(LGVF)approach for improving the precision of surveillance trajectory tracking.Then,in order to adopt to poor communication conditions,we propose a prediction-based synchronization method for keeping the formation consistently.Moreover,in order to adapt the multi-UAV system to dynamic and uncertain environment,this paper proposes a hierarchical dynamic task scheduling architecture.In this architecture,we firstly classify all the algorithms that perform tasks according to their functions,and then modularize the algorithms based on plugin technology.Afterwards,integrating the behavior model and plugin technique,this paper designs a three-layer control flow,which can efficiently achieve dynamic task scheduling.In order to verify the effectiveness of our architecture,we consider a multi-UAV traffic monitoring scenario and design several cases to demonstrate the online adjustment from three levels,respectively.展开更多
文摘浮动车数据(floating car data, FCD)技术是大规模城市路网交通流实时采集的有效方法.城市交通的动态诱导和控制需要对海量FCD进行快速处理.鉴于此,提出了FCD并行计算的动态任务调度方法.针对FCD数据包计算时间的不确定性和动态性,根据计算节点的处理能力进行数据包的动态分割,在处理过程中,采用动态任务分配策略以实现计算节点的同步.该方法在龙芯国产大数据一体机平台上进行了实现,并采用现场FCD数据进行了实验验证,结果表明,该方法较轮询和Min-Min调度算法,显著地提高了并行处理的性能.
文摘随着多核处理器片上集成核数的不断增多,并行任务的调度能力越来越成为制约性能提升的关键因素。文章设计一种面向异构多核计算系统的动态任务调度控制器,主要实现动态监控处理单元的负载情况、动态任务唤醒、乱序任务发射、任务写回安全管理等功能;研究一种降低计算任务结果数据回写双倍数据速率(double data rate,DDR)外存储器次数的方法,大幅节省了访存开销,进一步提升了计算性能。仿真及性能测试显示,在典型应用场景下,与已有的无动态调度功能的任务发射控制器相比,实现了显示并行化编程向任务并行的自动化控制过渡,编程友好度显著提高,在不同类型的测试案例中,分别提升了11.3%~37.9%的计算性能。
基金Project(2017YFB1301104)supported by the National Key Research and Development Program of ChinaProjects(61906212,61802426)supported by the National Natural Science Foundation of China。
文摘In this paper,we consider a multi-UAV surveillance scenario where a team of unmanned aerial vehicles(UAVs)synchronously covers an area for monitoring the ground conditions.In this scenario,we adopt the leader-follower control mode and propose a modified Lyapunov guidance vector field(LGVF)approach for improving the precision of surveillance trajectory tracking.Then,in order to adopt to poor communication conditions,we propose a prediction-based synchronization method for keeping the formation consistently.Moreover,in order to adapt the multi-UAV system to dynamic and uncertain environment,this paper proposes a hierarchical dynamic task scheduling architecture.In this architecture,we firstly classify all the algorithms that perform tasks according to their functions,and then modularize the algorithms based on plugin technology.Afterwards,integrating the behavior model and plugin technique,this paper designs a three-layer control flow,which can efficiently achieve dynamic task scheduling.In order to verify the effectiveness of our architecture,we consider a multi-UAV traffic monitoring scenario and design several cases to demonstrate the online adjustment from three levels,respectively.
