Zili Zhang, Chao Jin, Linpeng Tang, Xuanzhe Liu, Xin Jin USENIX Symposium on Networked Systems Design and Implementation ( NSDI 2023 ) , Boston, April 17–19, 2023. [ PDF ] [ Slides ]

Zili Zhang, Chao Jin, Linpeng Tang, Xuanzhe Liu, Xin Jin USENIX Symposium on Networked Systems Design and Implementation , Boston, April 17–19, 2023.

RiseofDistributedDeepLearningTrainingintheBigModelEra:FromASoftware EngineeringPerspective TOSEM’23 May.2023 • XuanzheLiu,DiandianGu,ZhenpengChen,JinfengWen ...

Hornors 2024. Merit Student (1/56); National Scholarship (Top 0.4% nationally); Presidential Scholarship of Peking University (highest honor for Ph.D. students at Peking University); 2023. Top 10 Bachelor Thesis of Peking University School of EECS (10/408); Outstanding Graduation Thesis at Peking University (33/4239); Outstanding Graduation Thesis at Beijing City (1103 undergraduate students ...

Talk 1 on Relevant Topic in Your Field . March 01, 2012. Talk at UC San Francisco, Department of Testing, San Francisco, California

Mar 1, 2024 · Zili Zhang. Get Lost In Nature. Follow. Beijing, China; Email; Github; Google Scholar; ORCID; Talks and presentations. Conference Proceeding talk 3 on Relevant Topic in Your Field . March 01, 2014. Conference proceedings talk, Testing Institute of America 2014 Annual Conference, Los Angeles, CA.

Zili Zhang Chao Jin Linpeng Tang† Xuanzhe Liu Xin Jin Peking University †Moqi Abstract The breakthroughs in deep learning enable unstructured data to be represented as high-dimensional feature vectors for serv-ing a wide range of applications. Processing vector queries (i.e., finding the nearest neighbor vectors for an input vector)

Zili Zhang Peking University Xin Jin Peking University 13th ACM SIGOPS Asia-Pacific Workshop on Systems (APSys 2022) Introduction 01 Design 02 Experiments & Analysis 03 Case Study & Conclusion 04 CONTENTS. Introduction •Convolutional Neural Networks •Successful in recognition and recommendation

Bingyang Wu∗ Zili Zhang∗ Zhihao Bai† Xuanzhe Liu∗ Xin Jin∗ ∗Peking University †Johns Hopkins University Abstract Containers are widely used for resource management in dat-acenters. A common practice to support deep learning (DL) training in container clouds is to statically bind GPUs to con-tainers in entirety.

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