5 results about "High performance parallel computing" patented technology

The invention relates to a high-precision sensing device capable of being used for GIS accidental partial discharge positioning and type diagnosis. The high-precision sensing device comprises an ultrahigh frequency sensor and a data acquisition system. The ultrahigh frequency sensor is responsible for capturing weak accidental partial discharge signals generated in the GIS equipment; the captured original signal is then sent to a filtering and amplifying module and a synchronization module for hardware preprocessing, and is digitalized through a high-speed sampling assembly; the digitized mass data are transmitted to the high-performance GPU parallel computing unit, and the data are efficiently analyzed by utilizing the strong parallel processing capability of the high-performance GPU parallel computing unit. According to the invention, the equipment sampling rate is improved to a high sampling rate of 5GHz, so that the partial discharge pulse sampling interval of the GIS partial discharge monitoring equipment reaches 0.2 ns, and the sampling points and the data volume are greatly increased; gPU parallel computing is combined and utilized to greatly improve the pulse processing capacity, so that the system can effectively find and capture intermittent and accidental partial discharge signals, and the detection rate of the intermittent and accidental signals is remarkably improved.

The application discloses a method and device for constructing a folded hypercube edge-disjoint Hamiltonian cycle, and relates to the technical field of interconnection network topology. The method comprises the following steps: recursively constructing a first Hamiltonian cycle on an n-dimensional folded hypercube; applying a mapping f to each vertex u in the first Hamiltonian cycle, and sequentially connecting the mapped vertices in the original order to obtain a second Hamiltonian cycle; and the first Hamiltonian cycle and the second Hamiltonian cycle do not have any common edge in the n-dimensional folded hypercube. The above method provides key technical support for the topology design and routing protocol of a high-performance parallel computing system.

This invention discloses a feature-preserving mesh processing method and system based on high-performance parallel computing. The input is an unlabeled point cloud dataset, used to train a point cloud feature generator and a prediction head MLP. α The input is a grid dataset with semantic segmentation labels. The grid surface is sampled to generate a sparse point cloud, and noise is added to the grid to represent the redundant structure of the grid geometry clipping task. Then, the trained prediction head MLP is removed. α Train prediction head MLPs separately β With MLP γ The user inputs the mesh to be processed, all vertices are converted into a sparse point cloud, and the point cloud feature generator and prediction head MLP are used. β MLP γ The invention generates semantic segmentation labels and redundant structure category labels for the sparse point cloud, respectively; finally, it performs a culling operation on the points marked as redundant structures to complete the mesh pruning. This invention can effectively understand the features in the mesh data, and the semantic segmentation results are more accurate. The noise addition process of this invention uses a CUDA parallel computing architecture to ensure the high efficiency of the entire computation process.

The invention discloses a high-performance time sequence parallel simulation system and method based on a time acceleration and synchronous control mechanism, and belongs to the technical field of high-performance parallel calculation. The system comprises a timing trigger module, a time mapping engine module, a task generation and distribution module, a parallel processing module, a synchronous blocking module, a state aggregation module and a visual output module. By establishing a double-layer time system of physical time takt-simulation time window, a time mapping engine dynamically calculates the number of virtual time steps needing to be propelled according to acceleration multiplied speed, and acceleration mapping of physical time and simulation time is achieved. The parallel processing module executes tasks in a thread pool with a fixed size, and the synchronous blocking module performs unified time sequence control on execution of each thread through a counting synchronizer, so that state consistency and calculation reproducibility under a high-speed operation condition are ensured. And the state aggregation module is used for integrating the synchronized local calculation results and updating the global system state. According to the method, high-speed simulation and high-fidelity playback of large-scale time-dependent data are realized on the premise of not damaging sequential logic, and the modeling verification efficiency and stability of a complex system can be remarkably improved.

The invention provides a direct current optimal power flow problem rapid solving method and device based on a GPU, and belongs to the field of power system optimization and high-performance parallel computing. The method comprises the following steps: establishing a direct current optimal power flow model; re-expressing the direct current optimal power flow model as a compact form of a linear programming model, and then converting the direct current optimal power flow model into a corresponding dual form; and solving the dual form of the direct current optimal power flow model on the GPU in parallel by using an sGS-HPR algorithm combined with symmetric Gauss-Seidel decomposition and a Halpern Peaeman-Rachford algorithm based on a positive semidefinite near-end item, and obtaining an optimization result of the direct current optimal power flow. According to the method, the parallel computing capability of the GPU can be fully utilized, the power grid optimization problem of the large-scale security constraint is efficiently solved, and the method has a relatively high application value.