KAUST and Cerebras Systems collaborated on multi-dimensional seismic processing using the Condor Galaxy AI supercomputer, achieving record sustained memory bandwidth of 92.58 petabytes per second. They developed a Tile Low-Rank Matrix-Vector Multiplication (TLR-MVM) kernel to exploit the architecture of Cerebras CS-2 systems. This work was recognized as a finalist for the 2023 Gordon Bell Prize. Why it matters: This demonstrates the potential of AI-customized architectures for seismic processing, with broader implications for climate modeling and other scientific domains in the region and globally.
KAUST's supercomputer Shaheen completed ultra-resolution subsurface mapping simulations for Saudi Aramco, producing a 3D image of subsurface geologic layers at a 7.5-meter resolution. Aramco scientists used integrated GeoDRIVE software to achieve this record resolution at a production scale, improving on prior simulations with tens of meters resolution. Shaheen, located in the KAUST Supercomputing Core Laboratory, is one of the largest CPU-based supercomputers globally, featuring 12,348 Intel Haswell CPUs. Why it matters: This achievement enables more precise resource extraction and geological understanding in the Arabian Peninsula, demonstrating the growing capabilities of regional supercomputing for industrial applications.
KAUST researchers have developed a detailed 3D dynamic model using data from the February 2023 Turkiye earthquake to improve earthquake simulations. The model incorporates 3D fault geometry and Earth structure for realistic simulations of ground shaking. It explains complex ground shaking patterns and the impact of supershear ruptures, which can amplify damage far from the epicenter. Why it matters: This research provides a more accurate understanding of earthquake rupture processes, crucial for seismic hazard assessment and infrastructure planning in seismically active regions like the Middle East.
KAUST Ph.D. graduate Tariq Alturkestani won the best paper award at Euro-Par 2020 for his doctoral thesis on overlapping I/O and compute in large-scale scientific computation using multilayered buffering mechanisms. His work re-evaluates the Reverse Time Migration (RTM) method used by geoscientists for oil and gas explorations, utilizing emerging storage technologies. The paper was co-authored with Professor David Keyes and Dr. Hatem Ltaief from the KAUST Extreme Computing Research Center (ECRC). Why it matters: This award highlights KAUST's growing prominence as a hub for Saudi talent and research in supercomputing and extreme computing, particularly in applications relevant to the region's energy sector.
KAUST Ph.D. students Kai Lu and Yuqing Chen won Best Presentation awards at a Society of Exploration Geophysicists workshop in Beijing. Lu's research focuses on machine learning applications in seismic processing, while Chen uses machine learning for automated semblance spectrum picking. They both leverage KAUST's Shaheen II supercomputer for their work. Why it matters: This highlights the increasing role of AI and ML in the oil and gas industry, and KAUST's contribution to advancing these technologies.