High-performance computing and deep learning workloads are extremely sensitive to latency. Packet loss forces retransmission or stalls in the communication pipeline, which directly increases latency and disrupts the synchronization between GPUs. This can degrade the performance of collective operations such as all-reduce or broadcast, where every GPU’s participation is required before progressing.
]]>In today’s data center, there are many ways to achieve system redundancy from a server connected to a fabric. Customers usually seek redundancy to increase service availability (such as achieving end-to-end AI workloads) and find system efficiency using different multihoming techniques. In this post, we discuss the pros and cons of the well-known proprietary multi-chassis link aggregation…
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