Parallel computing

Thursday, June 9, 2016 - 3:15pm - 4:15pm
Andrew Barker (Lawrence Livermore National Laboratory)
Optimization of controls and parameters coming from realistic full-scale simulation requires enormous computational effort. To make such optimization practical requires optimal multilevel solvers and scalable parallel algorithms. Even in the case where such solvers and algorithms are well understood for the forward problem, adapting them to the optimization context can be interesting and complicated.
Friday, January 14, 2011 - 11:00am - 12:00pm
Michael Heroux (Sandia National Laboratories)
After 15-20 years of architectural stability, we are in the midst of a dramatic change in high performance computing systems design. In this talk we discuss the commonalities across the viable systems of today, and look at opportunities for numerical algorithms research and development.
Thursday, January 13, 2011 - 11:00am - 12:00pm
Mike Giles (University of Oxford)
Thursday, January 13, 2011 - 3:00pm - 4:00pm
Geoffrey Fox (Indiana University)
1) We analyze the different tradeoffs and goals of Grid, Cloud and parallel (cluster/supercomputer) computing.

2) They tradeoff performance, fault tolerance, ease of use (elasticity), cost, interoperability.

3) Different application classes (characteristics) fit different architectures and we describe a hybrid model with Grids for data, traditional supercomputers for large scale simulations and clouds for broad based capacity computing including many data intensive problems.
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