This talk will address one of the main challenges in high performance computing which is the increased cost of communication with respect to computation, where communication refers to data transferred either between processors or between different levels of memory hierarchy, including possibly NVMs.
I will overview novel communication avoiding numerical methods and algorithms that reduce the communication to a minimum for operations that are at the heart of many calculations, in particular numerical linear algebra algorithms.
Those algorithms range from iterative methods as used in numerical simulations to low rank matrix approximations as used in data analytics. I will also discuss the algorithm/architecture matching of those algorithms and their integration in several applications.
|Dr. Laura Grigori|
Dr. Laura Grigori obtained her Ph.D. in Computer Science in 2001 from
University Henri Poincare in France. She was a postdoctoral researcher
at UC Berkeley and Lawrence Berkeley National Laboratory, before joining French Institute for Research in Computer Science and Automation (INRIA) in France in 2004.
Currently she now leads a joint research group between INRIA, University of Pierre
and Marie Curie, and the National Center for Scientific Research (CNRS), called Alpines.
Her field of expertise is
high performance scientific computing, numerical linear algebra, and
combinatorial scientific computing. She co-authored the papers
introducing communication avoiding algorithms that provably minimize
She is leading several projects in preconditioning,
communication avoiding algorithms, and associated numerical libraries
for large scale parallel machines. She is currently the Program
Director of the SIAM Special Interest Group on Supercomputing.