Speaker: Meiyue Shao
Time: 10:30 am, May 28, Wednesday
Zoom Meeting ID: 881 0309 9426
Password:540575
摘要：
In recent years mixed-precision algorithms have received increasing attention in numerical linear algebra and high performance computing. Modern mixed-precision algorithms perform a significant amount of low-precision arithmetic in order to speed up the computation, while still providing the desired solution in working precision. A number of existing works in the literature focus on mixed-precision linear solvers—much less is known about how to improve eigensolvers. In this talk we discuss several techniques that can accelerate eigenvalue computations by exploiting low-precision arithmetic. These techniques lead to several mixed-precision symmetric eigensolvers, for both dense and sparse eigenvalue problems. Our mixed-precision algorithms outperform existing fixed-precision algorithms without compromising the accuracy of the final solution.
Introduction to the speaker:
Meiyue Shao received his Ph.D. in 2014 from the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. From 2014 to 2019, he worked as a postdoctoral researcher and project scientist at Lawrence Berkeley National Laboratory, United States. Since 2019, he has been serving as an Associate Professor at the School of Data Science, Fudan University. His research focuses on numerical linear algebra, high-performance computing, and quantum mechanical computations. His work has been published in top-tier journals such as SISC, SIMAX, LAA and ACM TOMS. He has also developed several high-performance software packages, including BESPACK and PDHSEQR.
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