Your journey toward the achievement of your dreams will be incredible if you keep it simple, be patient and confident.
Dalal Sukkari PhD alumna AMCS

Education

  • 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).

Research Interests

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

Honors & Awards

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

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.

Selected Publications

Sukkari, D., Ltaief, H., Faverge, M., & Keyes, D. (2018). Asynchronous Task-Based Polar Decomposition on Single Node Manycore Architectures. IEEE Transactions on Parallel and Distributed Systems, 29(2), 312–323. doi:10.1109/tpds.2017.2755655
Sukkari, D., Ltaief, H., & Keyes, D. (2016). High Performance Polar Decomposition on Distributed Memory Systems. Lecture Notes in Computer Science, 605–616. doi:10.1007/978-3-319-43659-3_44
Sukkari, D. E., Ltaief, H., Keyes, D. E. (2014). Implementing a New Dense Symmetric Eigensolver Using Mixed Precision Techniques on Multicore Systems With Hardware Accelerators, SHAXC-2 Workshop 2014. https://hdl.handle.net/10754/624936
Sukkari, D. E. (2013). Implementing a New Dense Symmetric Eigensolver on Multicore Systems, (Thesis). https://hdl.handle.net/10754/296952