Dalal Sukkari

About

Research interests and present research project.

Dalal's research centers on a new high performance implementation of the QR-based Dynamically Weighted Halley iterations (QDWH) to compute the polar decomposition and its application to the SVD (QDWH-SVD). She has introduced a high performance QDWH-SVD implementation on multicore architecture enhanced with multiple GPUs, and on distributed memory based on the state-of-the-art vendor-optimized numerical library ScaLAPACK, and has presented the first asynchronous, task-based formulation of the polar decomposition QDWH and its corresponding implementation in the context of the Chameleon library with the dynamic runtime system StarPU on various architectures.

Why KAUST?

To pursue my passion for science and technology in one of the world's best equipped research facilities and to be supervised by professor David Keyes was the best opportunity toward the achievement of my dream.

Why did you choose your field of research?

I am interested in applying my mathematical background and computer science skills to solve a real-life problems and make an impact in the world.

Education Profile

• PhD in Applied Mathematics and Computational Science, KAUST (2014 - 2019).
• M.Sc in Applied Mathematics and Computational Science, KAUST (2011 - 2013).
• B.Sc in Mathematics, Hashemite University (2004 - 2008).

Areas of Expertise and Research Interests

• Singular value decomposition (SVD).
• Polar decomposition.
• Dense linear algebra.
• High Performance Computing (HPC).

Awards and Distinctions

• Best Paper Award at the PASC Conference (2018).
• Best Paper Award at the EuroPar Conference (2016).
• Dean’s Academic Excellence Award (2013).