2 results about "Client server mode" patented technology

This application discloses an asynchronous checkpoint caching control method, device, and medium for high-performance computing systems. The method includes the following steps: dividing the computing nodes in the system into SBB groups and PFS groups, and ensuring that the SBB and PFS groups complete checkpoint writing with minimal time difference; starting a server on each SBB node, embedding a client in each process of each computing node in the SBB group, and having each client communicate with an SBB server; during the checkpoint caching phase, using different modes to write checkpoints from the SBB and PFS groups to the corresponding SBB and PFS groups, respectively, where the SBB group uses a client-server mode and the PFS group uses a file I / O mode; during the checkpoint refresh phase, checkpoints in the SBB are refreshed using the client-server mode. This application can effectively alleviate performance bottlenecks during large-scale checkpoint applications in HPC systems, improve checkpoint efficiency, and reduce the impact on computing tasks.

The application relates to a label noise robust federated learning method based on self-step learning and an adjacency matrix, a horizontal federated learning framework based on a client-server mode, and distributed training, and comprises the following steps: step S1, selecting a trusted client; step S2, calculating the near neighbor relationship of all client samples according to the trusted client; performing federated learning according to the trusted client obtained in step S1 to obtain a global federated model, and using the global federated model to calculate the near neighbor relationship of all client samples; step S3, self-step updating of sample near neighbor relationship and label evaluation and correction; and step S4, out-of-cluster sample processing. The application provides a safe and reliable label noise robust federated learning method based on self-step learning and an adjacency matrix, can effectively reduce the interference of noise data on a model, and improves the model convergence speed.