SOURCE-LINKED INTELLIGENCE

Analyzing and Exploiting Inexactness in Exascale Matrix Computations

CORDIS · observation · Publication date unknown

or analyzing multiple sources of error in matrix computations. Without this basis, the quest for efficiency in areas vitally depending on matrix computations, including, for example, data science and machine learning, will remain reliant on a trial-and-error approach. This project aims to break the current modular approach to the analysis and design of algorithms for matrix computations by understanding how different sources of inexactness interact while being propagated through a computation and their effect on numerical behavior and solution quality. Our holistic approach, rooted in rigorous theoretical analysis, will reveal opportunities for developing new algorithms for exascale problems that exploit inexactness to balance performance and accuracy. The project is structured around four fundamental objectives: WP1: Analysis of exascale matrix computations subject to multiple sources of inexactness WP2: Development of new algorithms that exploit inexactness that are both fast and provably accurate WP3: Making error analysis of exascale computations meaningful in practice WP4

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recordType

award

status

SIGNED

region

EU

value

1496085

unit

EUR