GPU Programming for Scientific Computing and Beyond
Optimise GPU programming to accelerate scientific computing and other operations
Accelerators such as graphics processing units (GPUs) and co-processors are very effective at achieving high-performance computing (HPC) to optimise a device’s power. Typical GPU architecture means the units often perform several operations in parallel, breaking down large problems into smaller and simpler ones that can be executed at the same time.
To get the most from these resources, end-users and scientific developers need to use the parallel programming of a graphics processing unit at maximum efficiency. On this five-week course from the Partnership for Advanced Computing in Europe (PRACE) you’ll learn to do that.
Achieve accelerated parallel programming
Faster parallel programming means better performance and quicker scientific computing and other HPC results, making it a top priority. On this course, you’ll learn how to accelerate parallel programming using a GPU.
With this enhanced computational power, you’ll be able to run scientific and engineering simulations for scientific computing, solve matrices and vectors for artificial intelligence, or optimally run end-user applications like games.
Get all you need to kick-start your GPGPU programming
The course covers all aspects of general-purpose graphics processing unit (GPGPU) programming. You’ll get comprehensive instruction on GPU architecture, programming languages, code optimisation and tuning, and everything else required for any kind of HPC.
Learn GPGPU programming with a world-class team
PRACE is dedicated to enabling high-impact scientific and engineering discovery and research, and to strengthening HPC usage across Europe. This mission, along with the Partnership’s combined expertise, makes it ideally suited to helping you take your GPGPU programming game further.
Learning on this course
On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.
What will you achieve?
By the end of the course, you‘ll be able to...
- GPU parallel programming: CUDA and OpenACC programming model; GPU architecture; Efficient implementation of computational linear algebra routines; Running the scientific applications; Code optimization and fine-tuning concerning different architecture.
Who is the course for?
This course is designed for anyone who needs to use GPGPU programming, from end-users playing complex video games to researchers involved with artificial intelligence and scientific computing.
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