About Rabab Alomairy Rabab Alomairy Ibn Rushd 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 Oct 11 - Oct 17, 2026 From Abstraction to Acceleration: Productive, Precision-Aware Scientific Computing Rabab Alomairy, Ibn Rushd Assistant Professor, Computer Science Oct 12, 12:00 - 13:00 B9 R2325 programming abstractions algorithms software/hardware co-design scientific computing HPC This talk explores how high-level programming abstractions, precision-aware algorithms, and hardware-conscious design can work together to accelerate scientific computing while improving productivity, scalability, and energy efficiency. Jun 19 - Jun 25, 2022 High-Performance Scientific Applications Using Mixed Precisions and Low-Rank Approximations Powered by Task-based Runtime Systems Rabab Alomairy, Ibn Rushd 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.
From Abstraction to Acceleration: Productive, Precision-Aware Scientific Computing Rabab Alomairy, Ibn Rushd Assistant Professor, Computer Science Oct 12, 12:00 - 13:00 B9 R2325 programming abstractions algorithms software/hardware co-design scientific computing HPC This talk explores how high-level programming abstractions, precision-aware algorithms, and hardware-conscious design can work together to accelerate scientific computing while improving productivity, scalability, and energy efficiency.
High-Performance Scientific Applications Using Mixed Precisions and Low-Rank Approximations Powered by Task-based Runtime Systems Rabab Alomairy, Ibn Rushd 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.
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