Abstract: A data processing method in a data processing device is provided. First to-be-processed data input into a neural network are obtained. Iterative training is performed on the neural network for a first preset number of times by using first target data in the first to-be-processed data, to obtain a seed model of the neural network. First newly added data generated after an elapse of time corresponding to the first time window is obtained, and the first newly added data and the first to-be-processed data are combined into second to-be-processed data. Iterative training is performed on the seed model for a second preset number of times by using second target data in the second to-be-processed data, to obtain a first incremental model of the neural network. A first preset area overlaps between the second time window and the first time window. The first incremental model online is published.

Abstract: A parallel data processing method based on multiple graphic processing units (GPUs) is provided, including: creating, in a central processing unit (CPU), a plurality of worker threads for controlling a plurality of worker groups respectively, the worker groups including one or more GPUs; binding each worker thread to a corresponding GPU; loading a plurality of batches of training data from a nonvolatile memory to GPU video memories in the plurality of worker groups; and controlling the plurality of GPUs to perform data processing in parallel through the worker threads. The method can enhance efficiency of multi-GPU parallel data processing. In addition, a parallel data processing apparatus is further provided.

Abstract: A data processing method in a data processing device is provided. First to-be-processed data input into a neural network are obtained. Iterative training is performed on the neural network for a first preset number of times by using first target data in the first to-be-processed data, to obtain a seed model of the neural network. First newly added data generated after an elapse of time corresponding to the first time window is obtained, and the first newly added data and the first to-be-processed data are combined into second to-be-processed data. Iterative training is performed on the seed model for a second preset number of times by using second target data in the second to-be-processed data, to obtain a first incremental model of the neural network. A first preset area overlaps between the second time window and the first time window. The first incremental model online is published.

Abstract: A parallel data processing method based on multiple graphic processing units (GPUs) is provided, including: creating, in a central processing unit (CPU), a plurality of worker threads for controlling a plurality of worker groups respectively, the worker groups including a plurality of GPUs; binding each worker thread to a corresponding GPU; loading one batch of training data from a nonvolatile memory to a GPU video memory corresponding to one worker group; transmitting, between a plurality of GPUs corresponding to one worker group, data required by data processing performed by the GPUs through peer to peer; and controlling the plurality of GPUs to perform data processing in parallel through the worker threads.

Abstract: A parallel data processing method based on multiple graphic processing units (GPUs) is provided, including: creating, in a central processing unit (CPU), a plurality of worker threads for controlling a plurality of worker groups respectively, the worker groups including one or more GPUs; binding each worker thread to a corresponding GPU; loading a plurality of batches of training data from a nonvolatile memory to GPU video memories in the plurality of worker groups; and controlling the plurality of GPUs to perform data processing in parallel through the worker threads. The method can enhance efficiency of multi-GPU parallel data processing. In addition, a parallel data processing apparatus is further provided.

Abstract: A parallel data processing method based on multiple graphic processing units (GPUs) is provided, including: creating, in a central processing unit (CPU), a plurality of worker threads for controlling a plurality of worker groups respectively, the worker groups including a plurality of GPUs; binding each worker thread to a corresponding GPU; loading one batch of training data from a nonvolatile memory to a GPU video memory corresponding to one worker group; transmitting, between a plurality of GPUs corresponding to one worker group, data required by data processing performed by the GPUs through peer to peer; and controlling the plurality of GPUs to perform data processing in parallel through the worker threads.