Interplay of Linear Algebra, Machine Learning, and High Performance Computing

In recent years, we have seen a large body of research using hierarchical matrix algebra to construct low complexity linear solvers and preconditioners. Not only can these fast solvers significantly accelerate the speed of large scale PDE based simulations, but also they can speed up many AI and machine learning algorithms which are often matrix-computation-bound. On the other hand, statistical and machine learning methods can be used to help select best solvers or solvers’ configurations for specific problems and computer platforms. In all these fields, high performance computing is an indispensable cross-cutting tool for achieving real-time solutions for big data problems. In this talk, we will show our recent developments in the intersection of these areas.

Speaker: Xiaoye Li, Lawrence Berkeley National Laboratory