Managing GPU workloads with Univa Grid Engine - Univa Corporation

For almost two decades, GPUs (Graphics Processing Units) have been steadily revolutionizing high-performance computing (HPC) and AI. Originally designed for graphics-intensive applications such as gaming and image processing, it didn't take long for HPC professionals to see the potential of low-cost, massively parallel processors able to handle then billions (and now trillions) of floating-point operations per second. In this two-part article, I'll discuss GPU workloads and how they are managed with Univa Grid Engine. First, I'll provide a short primer on GPUs, explain how they are used in HPC and AI, and cover some of the specific challenges when running GPU applications on shared clusters. In part II, I'll focus on some of the specific innovations in Univa Grid Engine that help make GPU applications much easier to deploy and manage at scale.