Webinar recording now available: Who needs GPUs? Tips for determining if your code is a fit
Graphical Processing Units (GPUs) can significantly accelerate compute-intensive research, but are they a fit for your particular project?
In this webinar recording, NeSI's Alexander Pletzer walks through some of the key questions that can help determine whether GPUs are among the best tool(s) for your project's needs. These questions include:
- what approaches are currently available to offload computations to GPU hardware,
- what speedup can be realistically expected compared to a CPU, and
- what code features could be used to determine whether a program could benefit from running on a GPU
GPU computing is the use of a GPU (graphics processing unit) as a co-processor to accelerate CPUs for general-purpose scientific and engineering computing. They promise to significantly accelerate compute-intensive research and migrating code from CPU to GPUs can often bring huge rewards.
NeSI recently launched new NVIDIA A100 GPUs for researchers using machine learning applications. These new resources complement NeSI's existing set of Pascal P100 GPUs (which are available to any project with a Mahuika allocation).