Blog Post About Efficient NVIDIA GPU Management in HPC and AI Workflows with Gridware Cluster Scheduler (2025-04-13)

Over at HPC Gridware I recently published a blog post highlighting how Gridware Cluster Scheduler (formerly known as "Grid Engine") can significantly simplify GPU management and maximize efficiency in HPC and AI environments.

In the post, we cover exciting new capabilities and improvements, including intelligent scheduling to ensure your valuable GPUs never stay idle, automated GPU setup with simple one-line prolog and epilog scripts, and comprehensive per-job GPU monitoring with detailed accounting metrics. We also walk through integrated support for NVIDIA’s latest ARM-based Grace Hopper and Grace Blackwell platforms, showcasing Gridware’s flexibility for modern hybrid compute clusters with mixed compute architectures.

Additionally, The article provides hands-on examples, such as running GROMACS workloads seamlessly on the new NVIDIA architecture, and integrating NVIDIA containers effortlessly using Enroot. To further improve visibility and operational efficiency, Gridware now supports exporting key GPU metrics to Grafana.

Interested in ensuring your GPUs are always working at full capacity while keeping management complexity at bay? Check out the full blog post on HPC Gridware for all the details!