Search results
Instructor: Hang Liu; TA: Mingju He (mhe6@stevens.edu) Syllabus: Week 1: Course overview and Introduction; Week 2: Introduction; Week 3: Pipeline and ISA; Week 4: Pipeline and ISA; Week 5: Individual meeting and feedbacks; Week 6: Review of memory hierarchy; Week 7: Memory hierarchy design; Week 8: Instruction Level Parallelism
Hang Liu (Senior Member, IEEE) received the B.E. degree from the Huazhong University of Science and Technology, in 2011, and the Ph.D. degree from the George Washington University, in 2017. He is currently an Assistant Professor of electrical and computer engineering with the Stevens Institute of Technology, where he leads the HPDA Laboratory.
This project aims to develop a scalable symbolic factorization algorithm on GPUs which are absent in current practice. Personnel: Hang Liu (PI) Xiaoye S. Li (Collaborator) Anil Gaihre (PhD student) Luca Pasquariello (Master student) Zehui Xie (Master student) Publications: GSoFa: Scalable Sparse LU Symbolic Factorization on GPUs.
Hang Liu @ Rutgers, the State University of New Jersey. I am an Assistant Professor and Associate Undergraduate Director of Electrical and Computer Engineering at Rutgers, the State University of New Jersey, where I lead the HPDA lab. My research exploits emerging hardware — such as Graphics Processing Unit (GPU), Field-Programmable Gate ...
View Hang Liu’s profile on LinkedIn, a professional community of 1 billion members. ... View Hang’s full profile ... Assistant Professor at Stevens Institute of Technology
- Rutgers Electrical and Computer Engineering
Hang Liu @ Stevens Institute of Technology. Please check out my new website: https://asherliu.github.io. I am a Presidential Fellowship Assistant Professor of Electrical and Computer Engineering at Stevens Institute of Technology, where I lead the HPDA lab. My research exploits emerging hardware — such as Graphics Processing Unit (GPU), Field ...
Email: Hang.Liu@stevens.edu CRII: Expediting Subgraph Matching on GPUs Project Description: We are living in an increasingly connected world, where the Big Data movement has resulted in not only more data but, more importantly, more connected data, such as, social networks, knowledge graphs and deep neural networks.
