GPU Programming for Scientific Computing and Beyond

Optimise GPU programming for scientific computing and other operations

High-performance computing (HPC), which is achieved by accelerators such as co-processors and graphics processing units (GPUs), can be achieved with great efficiency using co-processors and GPUs. The typical GPU architecture allows for multiple operations to be performed simultaneously, breaking down complex problems into smaller and more manageable ones that can all be done at once.

End-users and scientists need to make the most of these resources. Scientific developers and end-users must use parallel programming to maximize efficiency. This course, which lasts five weeks, is offered by the Partnership for Advanced Computing in Europe.

It is a top priority because faster parallel programming results in better performance and faster scientific computing. This course will teach you how to accelerate parallel programming with a GPU.

This enhanced computational power will allow you to run scientific and engineering calculations for scientific computing, solve vectors and matrices for artificial intelligence, and run optimal end-user applications such as games.

This course covers all aspects related to general-purpose graphics processing units (GPGPU) programming. You will learn everything you need to know about GPU architecture, programming languages and code optimisation and tuning.

PRICE is dedicated towards enabling high-impact engineering and scientific discovery and research and strengthening HPC usage throughout Europe. This mission, together with the Partnership's combined expertise makes PRICE ideally suited for helping you improve your GPGPU programming game.