As clusters grow in scale and complexity, managing GPU resources becomes critical for achieving both high utilization and predictable performance. The GB200 NVL72 system introduces larger AI job segment sizes and fine-grained scheduling control, enabling operators to align segment configurations with workload needs. Together with GB200 NVL72-aware scheduling extensions in the Slurm workload manager, this approach balances large and small jobs to maximize efficiency even in the presence of hardware faults.
Unlock Exascale Performance on NVIDIA GB200 NVL72 with Slurm Topology-Aware Job Scheduling | NVIDIA Technical BlogNVIDIA GB200 NVL72 is an exascale computer in a single rack. With 72 NVIDIA Blackwell GPUs interconnected by the largest production scale-up compute fabric, NVIDIA NVLink provides 130 terabytes per second (TB/s) of low-latency GPU communication bandwidth for AI and high-performance computing (HPC) workloads. Multiple GB200 NVL72 systems combined in a cluster create hierarchical network topology with large domains of very high networking bandwidth. An AI training job can greatly benefit from the abundant networking bandwidth offered by GB200 NVL72, when scheduled to maximize the use of NVLink
Unlock Exascale Performance on NVIDIA GB200 NVL72 with Slurm Topology-Aware Job Scheduling | NVIDIA Technical Blog… Key Features Offloads collectives communications from MPI onto NVIDIA Quantum InfiniBand networking hardware Multiple transport support, including Reliable Connection RC , Dynamic Connected DC , and Unreliable Datagram UD Intra-node shared memory communication Receive-side tag matching Native suppo… …
… With Slinky DCGM integration and HPC job mapping support , you can enable per-job GPU metrics labeled with Slurm job IDs, giving you workload-level GPU monitoring across your cluster. …
… For AI architects and HPC platform operators, the challenge isn’t just racking and stacking hardware—it’s turning infrastructure into safe, performant, and easy-to-use resources for end users. …
… View all posts by Sachin Lakharia View all posts by Sachin Lakharia About Vipin Sirohi Vipin Sirohi is a principal HPC architect at NVIDIA with over a decade of experience in HPC and EDA infrastructure. …
… Cluster administrators can also decide to switch from guidance to enforcement by rejecting jobs that do not meet cluster guidelines. …
… In almost any scenario, AMD lets us afford as many as twice the GPUs.” Looking for a partner, not just a hardware supplier Early hardware experiences did not match the depth or pace Maincode needed. “We found a lot of legacy HPC thinking and enterprise optimization,” Lemphers says. “We need technic… …
… So, if you are a: Computational biologist scaling structure prediction pipelines AI researcher training generative protein models HPC engineer optimizing GPU workloads Bioinformatician team building structural resources You will learn how to: Design a proteome-scale complex prediction strategy Sepa… …
… It supports on-prem clusters, cloud providers such as AWS, Azure, and GCP, multi-cloud environments, NVIDIA Jetson™ and ARM edge hardware, and mixed compute setups. …
… By utilizing HPC systems with acceleration and specialized networking, scientists can meet these demands. Using cuPyNumeric, programmers are able to utilize a single programming model that works both on traditional systems and utilizes the modern hardware features. …
… Thermal Management Excellence: Direct-to-Chip D2C liquid cooling efficiently manages intense heat, maintaining optimal temperatures and reducing hardware stress. …