And more improving training.
Optimizing Ethernet-Based AI Management Fabrics with MLAG
Jun 21, 2023, By Davinder Singh
For HPC clusters purposely built for AI training, such as the NVIDIA DGX BasePOD and NVIDIA DGX SuperPOD, fine-tuning the cluster is critical to increasing and optimizing the overall performance of the cluster. This includes fine-tuning the overall performance of the management fabric (based on Ethernet), storage fabric (Ethernet or InfiniBand), and the compute fabric (Ethernet or InfiniBand).
This post discusses how to maximize the overall throughput of the management fabric with Multi-Chassis Link Aggregation (MLAG), available on NVIDIA Cumulus Linux. MLAG enables two separate switches to advertise the same LACP system ID to downstream hosts. As a result, the downstream hosts see the uplinks as if they are connected to a single LACP partner.
One benefit of using MLAG is physical switch-level redundancy. If either of the two uplink switches experiences a failure, downstream host traffic will not be impacted. A second benefit is that the uplinks of the aggregated bond are all used at the same time. Finally, MLAG technology provides gateway-level redundancy, using technologies such as VRR/VRRP. ... '
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