FastLA is an Associate Team between INRIA project-team HiePacs, Scientific Computing Group in the Computational Research Division in Lawrence Berkeley National Laboratory and the Institute for Computational and Mathematical Engineering and Stanford University, funded from 2012 to 2013.



General description

FastLA - Fast and Scalable Hierarchical Algorithms for Computational Linear Algebra

It is admitted today that numerical simulation is the third pillar for the development of scientific discovery at the same level as theory and experimentation. Numerous analyses also confirmed that high performance simulation will open new opportunities not only for research but also for a large spectrum of industrial sectors. On the route to exascale, emerging parallel platforms exhibit hierarchical structures both in their memory organization and in the granularity of the parallelism they can exploit.

In this joint project between Inria HiePACS, Lawrence Berkeley National Laboratory (LBNL) and Stanford we propose to study, design and implement hierarchical parallel scalable numerical techniques to address two challenging numerical kernels involved in many intensive simulation codes: namely, N-body interaction calculations and the solution of large sparse linear systems. Those two kernels share common hierarchical features and algorithmic challenges as well as numerical tools such as low-rank matrix approximations expressed through H-matrix calculations.

This project has started from January 1st for three years. It will address algorithmic and numerical challenges, and will result in parallel software prototype to be validated on large scale applications.