Building on a new model proposed recently for calculating constant electro-magnetic field values, the present article explores the electro-magnetic field configuration generated by parallel electrical wires. This impo...Building on a new model proposed recently for calculating constant electro-magnetic field values, the present article explores the electro-magnetic field configuration generated by parallel electrical wires. This imposes a reevaluation of the drawing procedure for constructing field curves with a constant field values around multiple parallel electrical conducting wires. To achieve this, we employ methods akin to those used for creating contours on topographical maps, ensuring a consistent numerical field value along the entire length of the field curves. Subsequent calculations will be conducted for scenarios where wires are not parallel.展开更多
Training deep neural networks(DNNs)requires a significant amount of time and resources to obtain acceptable results,which severely limits its deployment in resource-limited platforms.This paper proposes DarkFPGA,a nov...Training deep neural networks(DNNs)requires a significant amount of time and resources to obtain acceptable results,which severely limits its deployment in resource-limited platforms.This paper proposes DarkFPGA,a novel customizable framework to efficiently accelerate the entire DNN training on a single FPGA platform.First,we explore batch-level parallelism to enable efficient FPGA-based DNN training.Second,we devise a novel hardware architecture optimised by a batch-oriented data pattern and tiling techniques to effectively exploit parallelism.Moreover,an analytical model is developed to determine the optimal design parameters for the DarkFPGA accelerator with respect to a specific network specification and FPGA resource constraints.Our results show that the accelerator is able to perform about 10 times faster than CPU training and about a third of the energy consumption than GPU training using 8-bit integers for training VGG-like networks on the CIFAR dataset for the Maxeler MAX5 platform.展开更多
Based on domain decomposition, a parallel two-level finite element method for the stationary Navier-Stokes equations is proposed and analyzed. The basic idea of the method is first to solve the Navier-Stokes equations...Based on domain decomposition, a parallel two-level finite element method for the stationary Navier-Stokes equations is proposed and analyzed. The basic idea of the method is first to solve the Navier-Stokes equations on a coarse grid, then to solve the resulted residual equations in parallel on a fine grid. This method has low communication complexity. It can be implemented easily. By local a priori error estimate for finite element discretizations, error bounds of the approximate solution are derived. Numerical results are also given to illustrate the high efficiency of the method.展开更多
【应用背景】快速射电暴(Fast Radio Burst,FRB)搜寻是500米口径球面射电望远镜(FAST)的重要科学目标之一,其计算复杂度高,数据量大,当前算法GPU利用率偏低,数据处理需较多的人工介入操作。【目的】在不修改算法实现的前提下,实现进程级...【应用背景】快速射电暴(Fast Radio Burst,FRB)搜寻是500米口径球面射电望远镜(FAST)的重要科学目标之一,其计算复杂度高,数据量大,当前算法GPU利用率偏低,数据处理需较多的人工介入操作。【目的】在不修改算法实现的前提下,实现进程级GPU并行优化,提高GPU整体资源利用率,简化算法运行调度,支持利用自动化脚本驱动计算过程。【方法】利用容器化封装FRB搜寻算法,结合GPU聚合技术实现多个FRB搜寻计算容器的多进程并行,支持GPU闲时复用。通过容器化封装屏蔽了GPU调用、依赖库管理等技术细节,减少人工介入操作。【结果】算法实验结果表明,在不修改原始算法、不增加GPU资源的前提下,将单GPU绑定6个计算进程,并行优化可实现FRB搜寻算法的加速比达到5.3,并行效率达到0.88,取得良好的并行效果。【结论】基于容器化封装及进程级GPU聚合的并行优化,可实现GPU利用率及计算效率的提升,有效支持自动化处理。该方法还具有良好的通用性,可适用于类似应用的并行优化。展开更多
文摘Building on a new model proposed recently for calculating constant electro-magnetic field values, the present article explores the electro-magnetic field configuration generated by parallel electrical wires. This imposes a reevaluation of the drawing procedure for constructing field curves with a constant field values around multiple parallel electrical conducting wires. To achieve this, we employ methods akin to those used for creating contours on topographical maps, ensuring a consistent numerical field value along the entire length of the field curves. Subsequent calculations will be conducted for scenarios where wires are not parallel.
文摘Training deep neural networks(DNNs)requires a significant amount of time and resources to obtain acceptable results,which severely limits its deployment in resource-limited platforms.This paper proposes DarkFPGA,a novel customizable framework to efficiently accelerate the entire DNN training on a single FPGA platform.First,we explore batch-level parallelism to enable efficient FPGA-based DNN training.Second,we devise a novel hardware architecture optimised by a batch-oriented data pattern and tiling techniques to effectively exploit parallelism.Moreover,an analytical model is developed to determine the optimal design parameters for the DarkFPGA accelerator with respect to a specific network specification and FPGA resource constraints.Our results show that the accelerator is able to perform about 10 times faster than CPU training and about a third of the energy consumption than GPU training using 8-bit integers for training VGG-like networks on the CIFAR dataset for the Maxeler MAX5 platform.
基金Project supported by the National Natural Science Foundation of China(No.11001061)the Science and Technology Foundation of Guizhou Province of China(No.[2008]2123)
文摘Based on domain decomposition, a parallel two-level finite element method for the stationary Navier-Stokes equations is proposed and analyzed. The basic idea of the method is first to solve the Navier-Stokes equations on a coarse grid, then to solve the resulted residual equations in parallel on a fine grid. This method has low communication complexity. It can be implemented easily. By local a priori error estimate for finite element discretizations, error bounds of the approximate solution are derived. Numerical results are also given to illustrate the high efficiency of the method.
文摘【应用背景】快速射电暴(Fast Radio Burst,FRB)搜寻是500米口径球面射电望远镜(FAST)的重要科学目标之一,其计算复杂度高,数据量大,当前算法GPU利用率偏低,数据处理需较多的人工介入操作。【目的】在不修改算法实现的前提下,实现进程级GPU并行优化,提高GPU整体资源利用率,简化算法运行调度,支持利用自动化脚本驱动计算过程。【方法】利用容器化封装FRB搜寻算法,结合GPU聚合技术实现多个FRB搜寻计算容器的多进程并行,支持GPU闲时复用。通过容器化封装屏蔽了GPU调用、依赖库管理等技术细节,减少人工介入操作。【结果】算法实验结果表明,在不修改原始算法、不增加GPU资源的前提下,将单GPU绑定6个计算进程,并行优化可实现FRB搜寻算法的加速比达到5.3,并行效率达到0.88,取得良好的并行效果。【结论】基于容器化封装及进程级GPU聚合的并行优化,可实现GPU利用率及计算效率的提升,有效支持自动化处理。该方法还具有良好的通用性,可适用于类似应用的并行优化。
