数据驱动建模方法改变了发电机传统的建模范式,导致传统的机电暂态时域仿真方法无法直接应用于新范式下的电力系统。为此,该文提出一种基于数据-模型混合驱动的机电暂态时域仿真(data and physics driven time domain simulation,DPD-T...数据驱动建模方法改变了发电机传统的建模范式,导致传统的机电暂态时域仿真方法无法直接应用于新范式下的电力系统。为此,该文提出一种基于数据-模型混合驱动的机电暂态时域仿真(data and physics driven time domain simulation,DPD-TDS)算法。算法中发电机状态变量与节点注入电流通过数据驱动模型推理计算,并通过网络方程完成节点电压计算,两者交替求解完成仿真。算法提出一种混合驱动范式下的网络代数方程组预处理方法,用以改善仿真的收敛性;算法设计一种中央处理器单元-神经网络处理器单元(central processing unit-neural network processing unit,CPU-NPU)异构计算框架以加速仿真,CPU进行机理模型的微分代数方程求解;NPU作协处理器完成数据驱动模型的前向推理。最后在IEEE-39和Polish-2383系统中将部分或全部发电机替换为数据驱动模型进行验证,仿真结果表明,所提出的仿真算法收敛性好,计算速度快,结果准确。展开更多
在构建CPU(Central Process Unit,CPU)与GPU(Graphic Process Unit)或者CPU与其它设备协同计算的过程中,通过PCI(Peripheral Component Interconnect)总线将GPU等其他设备连接至CPU,承担并行计算的任务.为了解决PCI接口芯片与GPU芯片之...在构建CPU(Central Process Unit,CPU)与GPU(Graphic Process Unit)或者CPU与其它设备协同计算的过程中,通过PCI(Peripheral Component Interconnect)总线将GPU等其他设备连接至CPU,承担并行计算的任务.为了解决PCI接口芯片与GPU芯片之间的异步传输和时序匹配问题,基于PCI总线规范与GPU芯片的时序规范,采用跨时钟域信号的处理方法,设计了一个CPU与GPU之间跨时钟域连接的时序匹配接口电路.通过仿真,验证了该电路的正确性.结果表明,该电路可工作在252 MHz频率下,能够满足GPU与CPU间接口电路对速率和带宽的要求.展开更多
Anovel beamforming algorithmnamed Delay Multiply and Sum(DMAS),which excels at enhancing the resolution and contrast of ultrasonic image,has recently been proposed.However,there are nested loops in this algorithm,so t...Anovel beamforming algorithmnamed Delay Multiply and Sum(DMAS),which excels at enhancing the resolution and contrast of ultrasonic image,has recently been proposed.However,there are nested loops in this algorithm,so the calculation complexity is higher compared to the Delay and Sum(DAS)beamformer which is widely used in industry.Thus,we proposed a simple vector-based method to lower its complexity.The key point is to transform the nested loops into several vector operations,which can be efficiently implemented on many parallel platforms,such as Graphics Processing Units(GPUs),and multi-core Central Processing Units(CPUs).Consequently,we considered to implement this algorithm on such a platform.In order to maximize the use of computing power,we use the GPUs andmulti-core CPUs inmixture.The platform used in our test is a low cost Personal Computer(PC),where a GPU and a multi-core CPU are installed.The results show that the hybrid use of a CPU and a GPU can get a significant performance improvement in comparison with using a GPU or using amulti-core CPU alone.The performance of the hybrid system is increased by about 47%–63%compared to a single GPU.When 32 elements are used in receiving,the fame rate basically can reach 30 fps.In the best case,the frame rate can be increased to 40 fps.展开更多
文摘在构建CPU(Central Process Unit,CPU)与GPU(Graphic Process Unit)或者CPU与其它设备协同计算的过程中,通过PCI(Peripheral Component Interconnect)总线将GPU等其他设备连接至CPU,承担并行计算的任务.为了解决PCI接口芯片与GPU芯片之间的异步传输和时序匹配问题,基于PCI总线规范与GPU芯片的时序规范,采用跨时钟域信号的处理方法,设计了一个CPU与GPU之间跨时钟域连接的时序匹配接口电路.通过仿真,验证了该电路的正确性.结果表明,该电路可工作在252 MHz频率下,能够满足GPU与CPU间接口电路对速率和带宽的要求.
基金This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJQN201801606)the Natural Sci-ence Foundation Project of CQ CSTC(cstc2017jcyjAX0092)+3 种基金the Scientific Research Program of Chongqing University of Education(Grant Nos.KY201924C,2017XJZDWT02)the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJ1601410)the Project‘Future School(Infant Education)’of National Center For Schooling Development Programme of China(Grant No.CSDP18FC2202)the Chongqing Electronics Engineering Technology Research Center for Interactive Learning,and the Chongqing Big Data Engineering Laboratory for Children.
文摘Anovel beamforming algorithmnamed Delay Multiply and Sum(DMAS),which excels at enhancing the resolution and contrast of ultrasonic image,has recently been proposed.However,there are nested loops in this algorithm,so the calculation complexity is higher compared to the Delay and Sum(DAS)beamformer which is widely used in industry.Thus,we proposed a simple vector-based method to lower its complexity.The key point is to transform the nested loops into several vector operations,which can be efficiently implemented on many parallel platforms,such as Graphics Processing Units(GPUs),and multi-core Central Processing Units(CPUs).Consequently,we considered to implement this algorithm on such a platform.In order to maximize the use of computing power,we use the GPUs andmulti-core CPUs inmixture.The platform used in our test is a low cost Personal Computer(PC),where a GPU and a multi-core CPU are installed.The results show that the hybrid use of a CPU and a GPU can get a significant performance improvement in comparison with using a GPU or using amulti-core CPU alone.The performance of the hybrid system is increased by about 47%–63%compared to a single GPU.When 32 elements are used in receiving,the fame rate basically can reach 30 fps.In the best case,the frame rate can be increased to 40 fps.
