Abstract:

We explore the possibilities of a hybrid system capable of solving both HPC and AI scientific problems. Such a hybrid architecture combines the synergism between classical HPC platforms and dedicated AI chip systems, which is important due to the computational challenges brought to the fore by massively parallel Exascale systems.

Abstract:

Come and share your experiences with the state-of-the-art of mixed-precision techniques! By wisely trading off accuracy, we can mitigate data movement overheads and increase performance of applications, including real-time adaptive optics simulations on ground-based telescopes and genome-wide association study for agricultural genomics.

Regular stencil computations constitute the main core kernel in many temporally explicit approaches for structured grid finite-difference, finite-volume, and finite-element discretizations of partial differential equation conservation laws.

 

KAUST Professor of Computer Science Peter Richtárik and his former student Nicolas Loizou, currently a postdoctoral researcher at Mila - Quebec Artificial Intelligence Institute and soon to take up an assistant professorship position at Johns Hopkins University, recently received the 2020 Computational Optimization and Applications (COAP) Best Paper Award.