6results about How to "Improve Parallel Computing Efficiency" patented technology

This invention belongs to the field of medical temporal signal processing technology, specifically a method for predicting respiratory-related displacement based on attention-embedded dilated convolution. The method involves acquiring respiratory-related temporal data of the target object over continuous time; inputting this data into a trained respiratory-related displacement temporal prediction network model, which outputs the predicted respiratory displacement. The network model comprises multiple cascaded dilated convolutional residual blocks and a prediction layer. Each dilated convolutional residual block includes a causal dilated convolutional unit, a self-attention mechanism unit, and a residual connection unit. The output of the last dilated convolutional residual block is input into the prediction layer, which outputs the respiratory-related displacement of the target object at future time points. This invention dynamically weights key time steps during each layer's temporal feature extraction process, thereby improving the predictive ability for respiratory-related displacement and tumor motion.

The invention discloses an operator fusion model training method and apparatus, a device, a storage medium and a program product. The method comprises the steps of obtaining performance data of an operator fusion model in a historical training process; wherein the performance data comprises at least one of operator operation performance data and operator calculation structures corresponding to different operator combinations; preprocessing the performance data, and obtaining key feature data from the preprocessed performance data; carrying out updating training on the operator fusion model by utilizing the key feature data; in the updating training process, adjusting and optimizing hyper-parameters of the operator fusion model to obtain a trained operator fusion model; wherein the operator fusion model is used for predicting the performance of the operator fusion strategy. By adopting the embodiment of the invention, the problem of overhigh memory occupation during model training can be avoided, the normal operation of model training is ensured, and the model training efficiency is improved.

The invention belongs to the field of database maintenance, and discloses a method and a related system for rapid data statistics of industrial time series data, which realize standardization and structuring of statistical logic by analyzing a customer data statistical request, extracting a tag name and a statistical task. Semantic differences of different databases in the aspects of statistical functions, aggregation rules, time window definitions and the like are avoided, and statistical tasks can be executed in a unified logic model. According to the method, the statistical task is divided into a plurality of time periods according to the time region, so that the system can independently execute statistical operation in different time windows. The sectional calculation not only improves the parallel calculation efficiency, but also avoids the deviation caused by different time index precisions of the bottom layer of the database, and effectively ensures the consistency and comparability of statistical results.

This application provides a parallel computing method, apparatus, electronic device, storage medium, and product. The method includes: receiving a computing request, the computing request including input data; sending the input data to a local accelerator according to the computing request, so that the local accelerator performs computing processing on the input data through a first module; receiving first boundary data sent by the local accelerator, obtaining second boundary data from a neighboring processor based on the first boundary data, and sending the second boundary data to the local accelerator, wherein the neighboring processor corresponds to a different computing node than the local accelerator; receiving a target computing result sent by the local accelerator, and writing the target computing result to a local hard disk through an input / output library. The method of this application, by performing parallel computing through accelerators with a larger number of cores, can execute multiple threads simultaneously, thereby improving parallel computing efficiency.

The application discloses a kind of real-time radar signal processing systems based on high-speed acquisition and isomeric parallel, belong to real-time radar signal processing technical field;Including high-speed data acquisition and analysis module, CPU and GPU isomeric parallel processing module, the high-speed data acquisition and analysis module includes Gigabit fiber optic network card and WinPcap analysis unit, the Gigabit fiber optic network card is connected using QSFP interface with radar digital transceiver board, the WinPcap analysis unit bypasses operating system protocol stack, directly with Gigabit fiber optic network card drive interaction through driver, the high-speed data acquisition and analysis module is connected with CPU and GPU isomeric parallel processing module communication;The CPU and GPU isomeric parallel processing module includes mutually communicating CPU module and GPU module, the real-time radar signal processing system based on high-speed acquisition and isomeric parallel of the application, through the synergistic cooperation of high-speed data acquisition and analysis front end and multilayer parallelization signal processing, realizes the real-time processing requirement from data acquisition to target detection.

The present application belongs to the technical field of semiconductor wafer detection, and particularly relates to a wafer surface topography defect detection system based on three-dimensional point cloud reconstruction, comprising: a division module that obtains three-dimensional point cloud data of a wafer to be measured and fits a reference plane, selects the original point closest to the center of the reference plane as an initial sampling point, and divides the data into N subsets and distributes them to parallel computing units; a calculation module that iteratively performs farthest point sampling, takes the distance of the new point as a step size, combines a preset constant to update the Z-axis weight of the anisotropic Euclidean distance, recalculates the distance in each unit and selects the locally farthest point, and obtains the globally farthest point; and an identification module that fits a reference polynomial surface based on the sampled point set, calculates the orthogonal distance deviation of the three-dimensional point cloud data of the entire wafer to be measured to the surface, determines an abnormal threshold value according to the deviation, and identifies topography defects. The present application enhances the stability of the system to outliers, and reduces the false detection and missed detection rates of topography defects.