公开(公告)号：US11625320B2

公开(公告)日：2023-04-11

申请号：US16946690

申请日：2020-07-01

Applicant: Huazhong University of Science and Technology

Inventor： Xuanhua Shi ,  Xuan Peng ,  Hai Jin ,  Hulin Dai ,  Weiliang Ma ,  Qian Xiong

IPC: G06F12/02 ,  G06F12/0806 ,  G06N5/046 ,  G06T1/60

Abstract: The present disclosure relates to a tensor-based optimization method for GPU memory management of deep learning, at least comprising steps of: executing at least one computing operation, which gets tensors as input and generates tensors as output; when one said computing operation is executed, tracking access information of the tensors, and setting up a memory management optimization decision based on the access information, during a first iteration of training, performing memory swapping operations passively between a CPU memory and a GPU memory so as to obtain the access information about the tensors regarding a complete iteration; according to the obtained access information about the tensors regarding the complete iteration, setting up a memory management optimization decision; and in a successive iteration, dynamically adjusting the set optimization decision of memory management according to operational feedbacks.