About Rabab Alomairy Rabab Alomairy Assistant Professor, Computer Science Performance optimization dense linear algebra High Performance Computing KAUST Ibn Rushd Assistent Professor Rabab Alomairy advances high-performance computing and AI research. 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, Assistant Professor, Computer Science 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, Assistant Professor, Computer Science 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.
Related Sites Hierarchical Computations on Manycore Architectures (HiCMA) Computer Science (CS) Related Content Articles 5 Events 1 Related Links Publication list per year Also view Publications in the KAUST Repository.
