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High-Performance Scientific Applications Using Mixed Precisions and Low-Rank Approximations Powered by Task-based Runtime Systems

Rabab Alomairy, Postdoctoral Research Fellow, King Abdullah University of Science and Technology

Jun 20, 11:00 - 13:00

B9 L4 R4223

Tile Low Rank Algorithmic redesign Task based Runtime Systems

Scientific applications from diverse sources rely on dense matrix operations. These operations arise in: Schur complements, integral equations, covariances in spatial statistics, ridge regression, radial basis functions from unstructured meshes, and kernel matrices from machine learning, among others. This thesis demonstrates how to extend the problem sizes that may be treated and reduce their execution time. Sometimes, even forming the dense matrix can be a bottleneck – in computation or storage.