Shaohua Duan
Assistant Professor (Starting 2023 Fall)
School of Electrical Engineering & Computer Science
Contact: shaohua.duan (at) wsu (dot) edu
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About Me
About Me
I am an incoming Assistant Professor at School of Electrical Engineering & Computer Science, Washington State University. I was a postdoctoral scholar at Department of Computer Science, University of Wisconsin - Madison, mentored by Andrea Arpaci-Dusseau and Remzi Arpaci-Dusseau. I received my PhD at Department of Computer Science, Rutgers University, advised by Manish Parashar. I did my MS at Department of Computer Science, The George Washington University, advised by Timothy W. Wood.
My research focuses broadly on storage systems and the way to build fault-tolerance and robust systems that function correctly and deliver high performance on all emerging platforms and hardware. This spans topics in key-value stores, distributed systems, data staging, and software testing.
Prospective students: I am always looking for brilliant students. I am especially interested in working with self-motivated students who have previously worked in Storage System or Software Engineering. Drop me an email if you are interested in working with me.
Publication
Publication
Philip E. Davis, Pradeep Subedi, Shaohua Duan, Lee Ricketson, Jeffrey A.F. Hittinger, Manish Parashar: “Benesh: a Programming Model for Coupled Scientific Workflows”. In IEEE/ACM 5th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2), November 2020.
Shaohua Duan, Manish Parashar: “Scalable Crash Consistency for Staging-based In-situ Scientific Workflows”. In IEEE International Parallel and Distributed Processing Symposium (IPDPS’20 HIPS workshop), May 2020.
Zhe Wang, Pradeep Subedi, Shaohua Duan, Yubo Qin, Philip Davis, Anthony Simonet, Ivan Rodero, Manish Parashar: “Exploring Trade-offs in Dynamic Task Triggering for Loosely Coupled Scientific Workflows”. arXiv:2004.10381, April, 2020.
Shaohua Duan, Pradeep Subedi, Keita Teranishi, Philip E. Davis, Hemanth Kolla, Marc Gamell, Manish Parashar: “CoREC: Scalable and Resilient In-Memory Data Staging for In-Situ Workflows”. ACM Transactions on Parallel Computing (TOPC), February, 2020.
Shaohua Duan, Pradeep Subedi, Philip E. Davis, Manish Parashar: “Addressing Data Resiliency for Staging Based Scientific Workflows”. In High Performance Computing, Networking, Storage and Analysis International Conference (SC’19), November 2019.
Pradeep Subedi, Anthony Simonet, Philip E. Davis, Shaohua Duan, Zhe Wang, Manish Parashar: “Data Management for Extreme Scale In-situ Workflows”. A book chapter of Future Trends of HPC in a Disruptive Scenario, IOS Press, 2019.
Esma Yildirim, Shaohua Duan, Xin Qi: “A Distributed Deep Memory Hierarchy System for Content-based Image Retrieval of Big Whole Slide Image Datasets”. In Workshop on Memory Centric High Performance Computing (MCHPC'19), November 2019.
Pradeep Subedi, Philip E. Davis, Shaohua Duan, Scott Klasky, Hemanth Kolla, Manish Parashar: “Stacker: an autonomic data movement engine for extreme-scale data staging-based in-situ workflows”. In High Performance Computing, Networking, Storage and Analysis International Conference (SC’18), November 2018.
Shaohua Duan, Pradeep Subedi, Keita Teranishi, Philip E. Davis, Hemanth Kolla, Marc Gamell, Manish Parashar: “Scalable Data Resilience for In-memory Data Staging”. In IEEE International Parallel and Distributed Processing Symposium (IPDPS’18), May 2018.
Sundaresan Rajasekaran, Shaohua Duan, Wei Zhang, Timothy Wood: “Multi-cache: Dynamic, Efficient Partitioning for Multi-tier Caches in Consolidated VM Environments”. In 4th IEEE International Conference on Cloud Engineering (IC2E’16): 182-191
Wei Zhang, Sundaresan Rajasekaran, Shaohua Duan, Timothy Wood, Mingfa Zhu: “Minimizing Interference and Maximizing Progress for Hadoop Virtual Machines”. SIGMETRICS Performance Evaluation Review 42(4): 62-71 (2015)
Teaching
Teaching
CPT S 560 - Operating Systems, Fall 2023
CPT S 360 - Systems Programming, Spring 2024
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