KAUST's Supercomputing Laboratory and NVIDIA co-hosted the "Accelerating Scientific Applications Using GPUs" workshop, attended by 120 participants. The event included technical sessions, guest lectures from KAUST faculty and NVIDIA, and presentations on KAUST applications developed on NVIDIA GPUs. KAUST also held its first hackathon, where teams ported scientific applications to GPU accelerators with guidance from KAUST and NVIDIA mentors. Why it matters: This collaboration strengthens KAUST's position as a hub for high-performance computing and GPU-accelerated research in the region, fostering talent development and collaboration with industry partners.
KAUST held its second hackathon and third NVIDIA workshop. Attendees listened to lectures from international experts. Participants worked on porting their scientific applications to a GPU accelerator. Why it matters: Such events help build regional expertise in accelerated computing and attract international collaboration.
QRC has developed Qibo, a Python library enabling classical simulation of quantum algorithms with double precision. Qibo leverages hardware accelerators like GPUs and CPUs with multi-threading. It incorporates a multi-GPU distributed approach for circuit simulation. Why it matters: This framework allows researchers and developers in the region to explore and prototype quantum algorithms using existing classical computing infrastructure, fostering innovation in quantum computing research and applications.
Marcus Engsig from DERC will present a paper at the MATLAB User Group Meeting in Abu Dhabi on October 6. The paper, titled ‘Generalization of Higher Order Methods For Fast Iterative Matrix Inversion Compatible With GPU Acceleration’, discusses a novel approach to matrix inversion using GPUs. The method, named Nested Neumann, achieves 4-100x acceleration compared to standard MATLAB methods for large matrices. Why it matters: This research contributes to faster computation in numerical and physical modeling, crucial for processing large datasets in various scientific and engineering applications in the region.
The article discusses the importance of sample correlations in computer graphics, vision, and machine learning, highlighting how tailored randomness can improve the efficiency of existing models. It covers various correlations studied in computer graphics and tools to characterize them, including the use of neural networks for developing different correlations. Gurprit Singh from the Max Planck Institute for Informatics will be presenting on the topic. Why it matters: Optimizing sampling techniques via understanding and applying correlations can lead to significant advancements and efficiency gains across multiple AI fields.
This is an advertisement for KAUST Discovery, seemingly related to High Performance Computing (HPC). It mentions King Abdullah bin Abdulaziz Al Saud. Why it matters: The ad suggests KAUST is investing in HPC, which is a critical infrastructure component for AI research and development.