CURC's newest supercomputer, Alpine, offers users access to state-of-the-art hardware, including AMD MI100s and NVIDIA A100 GPUs. Though this hardware can process complex, deep learning workflows and significantly reduce run times for some computations, users must first overcome the barriers to entry before taking full advantage.

This training, aimed at researchers with little to no GPU programming experience, introduces heterogeneous (CPU/GPU) computing and popular GPU programming models. The session also presents criteria for porting workflows to GPUs and a high-level overview of factors that determine speed-up. Participants will learn how to request GPU nodes on Alpine with Slurm, then try a hands-on example to compare run times for CPU-only and GPU-accelerated scripts. Examples will focus on Python and C programming languages, but experience with either language is not required.

This training is part of our two-part GPU Acceleration Series. Part 2 focuses on utilizing the AMD GPUs and ROCm ecosystem.