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Video: NVIDIA to Accelerate the HPC-AI Convergence

Gunter Roeth from NVIDIA gave this talk at ML4HPC 2020. “The growing adoption of NVIDIA Volta GPU by the Top500 Supercomputers highlights the need of computing acceleration for this HPC & AI convergence. Many projects today demonstrate the benefit of AI for HPC, in terms of accuracy and time to solution, in many domains such as Computational Mechanics, Earth Sciences, Life Sciences, Computational Chemistry, and Computational Physics. NVIDIA today for instance, uses Physics Informed Neural Networks for the heat sink design in our DGX system.”

Podcast: HPC & AI Convergence Enables AI Workload Innovation

In this Conversations in the Cloud podcast, Esther Baldwin from Intel describes how the convergence of HPC and AI is driving innovation. “On the topic of HPC & AI converged clusters, there’s a perception that if you want to do AI, you must stand up a separate cluster, which Esther notes is not true. Existing HPC customers can do AI on their existing infrastructure with solutions like HPC & AI converged clusters.”

The Convergence of HPC and AI

In this special guest feature, Bill Wagner from Bright Computing writes that the convergence of HPC & AI presents new challenges for containers, job scheduling, and system management. “But here’s the rub … traditional HPC applications run under the jurisdiction of an HPC workload manager like Slurm or PBS Pro, whereas machine learning applications are primarily run in containers under the jurisdiction of a container orchestration system, such as Kubernetes.”