36results about How to "Relieve memory pressure" patented technology

The invention provides a frame animation caching method, apparatus and equipment, and a computer readable storage medium. The frame animation caching method includes the steps: acquiring the first n frames of images in a frame animation, and loading the first n frames of images into a preset bitmap cache; and successively acquiring other frames of images in the frame animation, and successively loading the other frames of images into the bitmap cache so as to replace the images which have been loaded into the bitmap cache. The frame animation caching method, apparatus and equipment can alleviate the memory pressure of the frame animation, and can avoid the problem of overflow of memory when the frame animation is carried out.

The invention provides a big data integrated machine based on electric power application. The big data integrated machine comprises a cabinet, an external server and one or more transformation equipment measuring point sensors and further comprises one or more lower calculation storage servers arranged at the lower part of the cabinet, a power module, a gigabit management network module, a 10-gigabit optical fiber data network module, one or more upper calculation storage servers, one or more high-performance calculation storage servers, fans and a fan controller, wherein the power module and the gigabit management network module are arranged in the middle of the cabinet; the 10-gigabit optical fiber data network module, the upper calculation storage servers and the high-performance calculation storage servers are arranged at the upper part of the cabinet from top to bottom in sequence; the fans and the fan controller are arranged on the back of the cabinet. The big data integrated machine is high in integration level, high in equipment usability, good in expansibility and stable in system performance.

The invention relates to the technical field of grid processing, and discloses a distributed unstructured grid cross-processor face docking method and system. The docking method uses a two-level index structure to parallelize and identify faces on both sides of the grid partition boundary. Connection relationship; for any two docking surface elements, perform equivalence judgment on their centroid coordinates and normalized grid point sequence in turn. It includes the following steps: S1, parallel import of distributed unstructured grid basic geometric data; S2, cross-processor construction of dual communication lists between sub-regions; S3, parallel construction of surface grid discrete structures at docking boundaries; S4, in Each processor constructs the fork tree structure of query set family; S5, cross-processor query pairing relationship between docking surface elements; S6, parallel export of face docking information of distributed unstructured grid. The invention solves the problems of low processing efficiency, poor data processing capability and the like in the prior art when processing large-scale unstructured grids.

The present invention provides a method, device, equipment and storage medium for processing data of entry stream data in a live broadcast room, which can filter the data of a full amount of live broadcast room synchronized with timing, and obtain the entry stream data of an effective live broadcast room; based on the preset survival time and Redis storage field flow data, and update it in real time; determine whether there is an orderly queue sorted according to the time window in the effective live broadcast room corresponding to the entry flow data; Place it at the head of the ordered queue, and update the ordered queue in real time; if it does not exist, obtain the latest current entry flow data, and generate an ordered queue according to the survival time from large to small. Filter the data of all live broadcast rooms, obtain the entry flow data of effective live broadcast rooms and use Redis to store them, update the orderly queues corresponding to effective live broadcast rooms, relieve the memory pressure caused by massive entry flow data, and ensure real-time processing of entry flow data .

The invention discloses a GPU-based high-performance graph mining method and system. The method comprises the steps: employing a GPU & CPU cooperative computing architecture, carrying out the graph mining operation through multiple threads of a GPU, improving the search efficiency, and storing a large number of intermediate sub-graphs generated in a graph mining process through a CPU memory; describing a system architecture by combining a Grow-Cull execution model: in a system operation process, copying a part of sub-graphs to a GPU each time to execute a Grow operation, judging a relationshipbetween the sub-graphs and vertexes / edges, and copying the generated candidate sub-graphs to a CPU memory; in order to check the legality of the candidate sub-graphs, the CPU multithreading technology is used for executing the Cull operation to judge the candidate sub-graphs, the qualified sub-graphs are stored in the CPU main memory. The system repeats the iteration process. By referring to thethought of a pipeline, CPU calculation and GPU calculation can be executed at the same time during iteration, bidirectional copying of data can also be executed at the same time, and calculation and transmission delay is masked.

The invention discloses a system for settling fierce competition of memory resources in a big data processing system. A memory information feedback module is used for monitoring the memory using condition for a running thread task, converting collected memory information and then feeding back the converted memory information to an information sampling and analyzing module; the information sampling and analyzing module is used for dynamically controlling the number of sampling times of information of all working nodes, analyzing data after the assigned number of sampling times is achieved, and calculating the optimal CPU to memory proportion of the current working node; a decision making and task distributing module is used for making a decision and controlling whether new tasks are distributed to the working nodes for calculation operation according to the information obtained through analysis and the task running information of the current working node to achieve effective limit on the CPU and memory use relation. By means of the system, a memory-perceptive task distribution mechanism can be achieved on a universal big data platform, the I / O expenditure generated when data overflows to a disc due to fierce competition of memory resources can be reduced, and the integral performance of the system can be effectively improved.

The present invention proposes a large volume BIM model data processing and loading method and equipment, belonging to the technical field of BIM data processing, which includes a model data import module; a data export module; a model data grouping module; a model data export module; a model data lightweight module; Model layer making module. The BIM model loading of the present invention adopts the principle of nearby loading, and can quickly display the scene model closest to the current viewing angle. When the number of models reaches a certain online level, it can automatically unload according to the loading situation.

The embodiment of the present invention discloses a real-time positioning method and device applied to automatic driving. The method includes: acquiring inertial measurement sensor data from the built-in inertial measurement sensor of the vehicle, and recording the time point when the inertial measurement sensor data is acquired. When sensor data other than inertial measurement sensor data is obtained at this time point, the vehicle is positioned according to the inertial measurement sensor data and other sensor data, and when no other sensor data is obtained at this time point, the vehicle is positioned based on the inertial measurement sensor data Position the vehicle. By implementing the embodiment of the present invention, the time point when the inertial measurement sensor data is received can be used as the time reference, and the sensor data obtained at the time point can be used for positioning to improve the positioning accuracy of the vehicle.