About Rabab Alomairy Rabab Alomairy Postdoctoral Research Fellow, King Abdullah University of Science and Technology Performance optimization dense linear algebra High Performance Computing KAUST alumna Dr. Rabab Alomairy advances high-performance computing and AI research as a postdoctoral fellow at MIT. Events Presented Events Jun 19 - Jun 25, 2022 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.
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.
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