High Performance Computing

New cost-effective GPUs solver redesigns the computational game

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Prof. David Keyes and his team proposed a GPUs light architecture to solve systems of multiple equations involving a large amount of data. Broadly used in computer gaming, mobile, and PC graphics - GPUs allow customizing an effective result-driven support to create a computational framework for increasing the number of processors while reducing the memory required to temporarily store the data.

Pulling rank on spatial statistics

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A technique that uses the power of computing could solve statistical problems cheaper and faster than current methods.

KAUST gets ready for SC18

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For the past 10 years, KAUST has made a strong presence at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) that takes place in different cities around the U.S. every year. 2018 will mark a special year for KAUST at SC, which will take place in Dallas, Texas, in November, with around 13,000 supercomputing professionals in attendance. The University will have an engaging presence on the exhibition floor and in the seminars, and the technical program of SC18 will be chaired by the Director of the KAUST Extreme Computing Research Center (ECRC)

Tariq receives his best paper award at EuroPar'2020

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Maximizing I/O Bandwidth for Out-of-Core HPC Applications on Heterogeneous Large-Scale Systems Best Paper Presentation by Tariq Alturkestani PhD Student, Computer Science, KAUST Thursday, Aug 27, 3:30pm - 4:00pm (AST) https://zoom.us/j/99947879910 Tariq will present his best paper at EuroPar'2020 at the virtual EuroPar'2020 conference in Warsaw, Poland. The reverse time migration (RTM) method is critical in seismic imaging for oil and gas industries. He demonstrates the effectiveness of his Multilayer Buffer System (MLBS) framework on Shaheen-2 (using 2048 compute nodes) and Summit

H2Opus: a Performance-Oriented Library for Hierarchical Matrices

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H2Opus: a Performance-Oriented Library for Hierarchical Matrices Download at https://github.com/ecrc/h2opus. Experience our latest HPC software release that implements H 2-Matrix operations on shared-memory systems, possibly equipped with GPU hardware accelerators. The prime target applications for H2Opus are PDE-constrained optimizations. The features of H2Opus include: Generation of matrix structure from a point set and admissibility condition, Construction of a hierarchical matrix given a kernel function, Matrix-vector and matrix-multiple-vector multiplication, Basis orthogonalization

Our Masked Guest Speakers Series

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Every week, we have a masked guest speaker opening our HiCMA weekly meeting by giving a remote talk on broad topics related to numerical linear algebra, high performance computing, programming models and performance optimizations. Our motto is fun and science!

The Arab World Prepares the Exascale Workforce

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The April 2021 Communications of the ACM special section on computing in the Arab world features an article by ECRC Director and KAUST founding dean David Keyes on preparing the exascale workforce. Keyes muses on the lure of hands-on opportunities to resolve today’s application-architecture tension with innovative algorithms for recruiting students, and he follows the placement of a dozen recent ECRC doctoral students from the MENA region in the US exascale computing project and Saudi industry. For them, “graduation” means an upgrade to a more powerful computer :-). Several have also seen

Yuxi Hong

Ph.D. Student, Computer Science
Yuxi Hong is a PhD student working with Professor David Keyes. He is working on accelerating the training process of deep learning from different aspects. Why KAUST? KAUST is a great place for research. Why HPC and machine learning? I believe we need more computation in the future. Scalable and efficient computing methods are basic tools for the scientific research. Education Profile MS Tsinghua University. BS Tsinghua University.

Wajih Halim Boukaram

Ph.D. Student, Computer Science
Wajih Halim Boukaram is a PhD candidate in Computer Science. He is studying under the supervision of Professor David E. Keyes.

Tariq Alturkestani

Ph.D. Student, Computer Science
Tariq is a PhD student in Computer Science working under the supervision of Professor David E. Keyes. He is working on developing a library that enables users to overlap I/O and compute. Tariq holds a Bachelors degree from Penn State University and a Master from KAUST. Education Profile MS, Computer Science, King Abdullah University of Science and Technology (KAUST), 2014. BS, Computer Science, Penn State University, USA, 2013.

Stefano Zampini

As a computational mathematician and software engineer, Stefano Zampini's work lies at the intersection of numerical analysis, predictive simulations, and high-performance computing, with recent forays into machine learning.

Rabab Alomairy

KAUST alumna Dr. Rabab Alomairy advances high-performance computing and AI research as a postdoctoral fellow at MIT.

Noha AlHarthi

Noha Al-Harthi received her PhD in Computer Science and was a member of KAUST's Extreme Computing Research Center and CEML. Her doctoral dissertation was conducted under the supervision of Professor David Keyes and Professor Hakan Bagci in the field of high-performance computing for wave propagation. Education Ph.D., Computer Science, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, 2012-2019. M.S, Computer Science, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, 2011-2012. B.S., Information System, Effat University, Jeddah

Nicolas Doucet

Nicolas Doucet is a PhD student in Computer Science, working under the supervision of Professor David Keyes.

Marcin Rogowski

Ph.D. Student, Computer Science

Lisandro Dalcin

Lisandro Dalcin obtained a degree in Electromechanical Engineering from National Technological University, Concepción del Uruguay, Argentina in 2001 and a PhD degree in Engineering Sciences, Computational Mechanics from National University of the Littoral, Santa Fe, Argentina in 2008. Research Interest Lisandro Dalcin's research interest include ​​Parallel and High Performance Computing, Scientific Software Development, Finite Element Methods and Computational Fluid Dynamics.​ Education Profile ​​PhD in Engineering Sciences, Computational Mechanics, National University of the Littoral, Santa

Hatem Ltaief

Principal Research Scientist, Applied Mathematics and Computational Science
Dr. Hatem Ltaief is a Principal Research Scientist at KAUST. He works on mixed-precision algorithms and performance optimizations to help HPC scientific applications address the exascale challenges. His innovations have been recognized with the ACM Gordon Bell Prize (shared) for Climate Modeling in 2024.

Dalal Sukkari

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

David Keyes

David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.

Amani Alonazi

Amani Alonazi is a Visiting Researcher in the Image and Viedo Understanding Lab (IVUL) Research Group working with Professor Bernard Ghanem. Previously, she was a Ph.D. candidate in Computer Science at CEMSE, KAUST, and a member of KAUST's Extreme Computing Research Center, ACM, and SIAM. Areas of expertise and current scientific interests Amani's research focuses on developing novel asynchronous task-based algorithms of solvers in scientific simulations. The core of her work lies within the domain of high-performance computing, which combines performance optimizations, modeling, and

Ali Charara

Ali Charara obtained both, his MS Degree and PhD Degree from Computer Science at KAUST in 2011 and 2018 respectively. His master thesis supervisor was Alyn Rockwood and his PhD thesis supervisor was David Keyes. Ali's research interests were in High Performance Computing with a focus on parallel computing and algorithms using multicore and manycore Computer Graphics Units. Education Profile MS Degree, Computer Science, KAUST (2011) PhD Degree, Computer Science, KAUST (2018)

Ali H. AlNasser

M.S. Student, Computer Science
Ali AlNasser is a Masters student in Computer Science. His research interests are scaling deep learning algorithms available now to be used on multicore computers by leveraging the high parallelism in Deep learning. Why KAUST? The abundance of opportunities available in KAUST. Education Profile B.S, Electrical & Computer Engineering, University of Colorado at Boulder. B.S, Applied Mathematics, University of Colorado at Boulder.