Jialin is a computer engineer in the Data Analytics Service group. He earned his Ph.D. in computer science from Texas Tech University with the dissertation 'Fast Data Analysis Framework for Scientific Big Data Applications'. He has interest in data management/analytics and parallel I/O.
Jialin Liu, Yu Zhuang, Yong Chen, "Hierarchical Collective I/O Scheduling for High-Performance Computing", Big Data Research, September 1, 2015,
Jialin Liu, Yong Chen, "Segmented In-Advance Computing for Fast Scientific Discovery", Transactions on Cloud Computing, 2015,
Alex Gittens et al, "Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies", 2016 IEEE International Conference on Big Data, July 1, 2017,
Jialin Liu, Evan Racah, Quincey Koziol, Richard Shane Canon,
Alex Gittens, Lisa Gerhardt, Suren Byna, Mike F. Ringenburg, Prabhat,
"H5Spark: Bridging the I/O Gap between Spark and Scientific Data Formats on HPC Systems",
Cray User Group,
May 13, 2016,
Mostofa Patwary, Nadathur Satish, Narayanan Sundaram, Jialin Liu, Peter Sadowski, Evan Racah, Suren Byna, Craig Tull, Wahid Bhimji, Prabhat, Pradeep Dubey, "PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures", IPDPS 2016, April 5, 2016,
Jialin Liu, Yong Chen, Surendra Byna, "Collective Computing for Scientific Big Data Analysis", 44th International Conference on Parallel Processing Workshops (ICPPW), September 1, 2015,
Annette Greiner, Evan Racah, Shane Canon, Jialin Liu, Yunjie Liu, Debbie Bard, Lisa Gerhardt, Rollin Thomas, Shreyas Cholia, Jeff Porter, Wahid Bhimji, Quincey Koziol, Prabhat, "Data-Intensive Supercomputing for Science", Berkeley Institute for Data Science (BIDS) Data Science Faire, May 3, 2016,
Review of current DAS activities for a non-NERSC audience.