Jarníkovská přednáška dr. Erin Carson

Erin Claire Carson působí na Katedře numerické matematiky MFF UK. Vystudovala informatiku na University of Virginia, doktorský titul získala na University of California, Berkeley. Tři roky působila na New York University. Od roku 2018 je odbornou asistentkou na MFF UK. Její výzkumy leží na pomezí numerické lineární algebry, vysoce výkonných počítačů a paralelních algoritmů. V roce 2023 získala prestižní ERC Starting Grant, v rámci pětiletého projektu vyvíjí nové algoritmy pro nejmodernější superpočítače.

Abstrakt přednášky:

On supercomputers that exist today, achieving even close to the peak performance is incredibly difficult if not impossible for many applications. Techniques designed to improve the performance of matrix computations – making computations less expensive by reorganizing an algorithm, making intentional approximations, and using lower precision – all introduce what we can generally call “inexactness”. The questions to ask are then:

1. With all these various sources of inexactness involved, does a given algorithm still get close enough to the right answer?
2. Given a user constraint on required accuracy, how can we best exploit and balance different types of inexactness to improve performance?

Studying the combination of different sources of inexactness can thus reveal not only limitations, but also new opportunities for developing algorithms for matrix computations that are both fast and provably accurate. We present a few recent examples of this approach, in which mixed precision computation is combined with other sources of inexactness.

OPMK