High-Performance Algorithms For Large-Scale Singular Value Problems and Big Data Applications
Student: Lingfei Wu (William & Mary)
Advisor: Andreas Stathopoulos (College of William & Mary)
Abstract: As "big data" has increasing influence on our daily life and research activities, it poses significant challenges on various research areas. Some applications often demand a fast solution of large-scale singular value problems; In other applications, extracting knowledge from large-scale data requires many techniques such as data mining, and high performance computing. We firstly present a preconditioned hybrid, two-stage method to effectively and accurately compute a small number of extreme singular triplets. More importantly, we have implemented a high-performance preconditioned SVD solver software, PRIMME\_SVDS. PRIMME\_SVDS fills a gap in production level software for computing the partial SVD, especially with preconditioning. In addition, we propose a real-time outlier detection algorithm to efficiently find blob-filaments in fusion experiments and numerical simulations. We have implemented this algorithm with hybrid MPI/OpenMP and show that we can achieve linear time speedup and complete blob detection in two or three milliseconds using a HPC cluster.
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