POLICY · CODING · #810
NVIDIA announces Topograph toolkit to publish cluster topology for topology-aware scheduling
NVIDIA Topograph is an open-source toolkit that discovers and normalizes cluster topology from cloud APIs and on‑prem fabrics, then publishes that model as Kubernetes node labels, Slurm configuration, or Slinky ConfigMaps so workload managers can perform topology-aware scheduling. It supports several cloud providers and on‑prem fabrics (InfiniBand, Spectrum‑X, Multi‑Node NVLink), integrates with DSX OS components such as DRA and KAI Scheduler, and is available from the dsx-ai-factory/topograph GitHub repo with Helm and native Slurm deployment options and simulation utilities.
KEY POINTS
- NVIDIA Topograph is an open-source toolkit that discovers and normalizes cluster topology from cloud APIs and on‑prem fabrics, then publishes that model as Kubernetes node labels, Slurm configuration, or Slinky ConfigMaps so workload managers can perform topology-aware scheduling.
- It supports several cloud providers and on‑prem fabrics (InfiniBand, Spectrum‑X, Multi‑Node NVLink), integrates with DSX OS components such as DRA and KAI Scheduler, and is available from the dsx-ai-factory/topograph GitHub repo with Helm and native Slurm deployment options and simulation utilities.
- Keeping an up‑to‑date, normalized topology view enables schedulers to place tightly coupled GPU workloads in locality domains, improving throughput and reducing cost and fragmentation across AI infrastructure.
WHY IT MATTERS
Keeping an up‑to‑date, normalized topology view enables schedulers to place tightly coupled GPU workloads in locality domains, improving throughput and reducing cost and fragmentation across AI infrastructure.