公开(公告)号：US20190278573A1

公开(公告)日：2019-09-12

申请号：US16216155

申请日：2018-12-11

Applicant: Huazhong University of Science and Technology

Inventor： Xuanhua SHI ,  Hai Jin ,  Zhixiang Ke ,  Wenchao Wu

IPC: G06F8/41 ,  G06F11/34 ,  G06F11/36

Abstract: The present invention relates to a method of memory estimation and configuration optimization for a distributed data processing system involves performing match between an application data stream and a data feature library, wherein the application data stream has received analysis and processing on conditional branches and/or loop bodies of an application code in a Java archive of the application, estimating a memory limit for at least one stage of the application based on the successful matching result, optimizing configuration parameters of the application accordingly, and acquiring static features and/or dynamic features of the application data based on running of the optimized application and performing persistent recording. Opposite to machine-learning-based memory estimation that does not ensure accuracy and fails to provide fine-grained estimation for individual stages, this method uses application analysis and existing data feature to estimate overall memory occupation more precisely and to estimate memory use of individual job stages for more fine-grained configuration optimization.

公开(公告)号：US20240086650A1

公开(公告)日：2024-03-14

申请号：US18217207

申请日：2023-06-30

Applicant: Huazhong University Of Science And Technology

Inventor： Xuanhua SHI ,  Yao WAN ,  Teng ZHANG ,  Hai JIN ,  Nan CAO ,  Mengfan LI

IPC: G06F40/53 ,  G06F40/295 ,  G06F40/30

CPC classification number: G06F40/53 ,  G06F40/295 ,  G06F40/30

Abstract: The present invention relates to a relation extraction system adapted to financial entities and fused with prior knowledge and a method thereof, the system at least comprising: a deep pretraining module, for training and generating a deep pretrained model for recognizing attributes of the financial entities; a keyword analyzing module, for extracting and outputting positional information and importance vectors of keywords in a Chinese finance-related text; an attention mechanism module, for encoding the positional information of the keywords to obtain attention masks, and inputting them with entity information into the deep pretrained model to acquire text feature vectors; and an optimal margin distribution model module, for predicting financial-entity relations based on the text feature vectors and the importance vectors. Aiming at low applicability of existing models to specific Chinese fields, the present invention obtains more accurate extraction results of entities and related features in Chinese finance-related texts.

公开(公告)号：US20210142178A1

公开(公告)日：2021-05-13

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: G06N3/08 ,  G06N3/063 ,  G06N5/04 ,  G06F12/02 ,  G06F12/0806 ,  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.