That’s why they need to hire a Data Scientist! Hopefully they won’t hold out for a unicorn!
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That’s why they need to hire a Data Scientist! Hopefully they won’t hold out for a unicorn!
Sign up for the free insideAI News newsletter.
“Our design philosophy is centered around our customers. They need solutions that are not just technically advanced but also seamlessly integrated, easily scalable, and reliable.”
Today, every high-performance computing (HPC) workload running globally faces the same crippling issue: Congestion in the network.
Congestion can delay workload completion times for crucial scientific and enterprise workloads, making HPC systems unpredictable and leaving high-cost cluster resources waiting for delayed data to arrive. Despite various brute-force attempts to resolve the congestion issue, the problem has persisted. Until now.
In this paper, Matthew Williams, CTO at Rockport Networks, explains how recent innovations in networking technologies have led to a new network architecture that targets the root causes of HPC network congestion, specifically:
– Why today’s network architectures are not a sustainable approach to HPC workloads
– How HPC workload congestion and latency issues are directly tied to the network architecture
– Why a direct interconnect network architecture minimizes congestion and tail latency
The solution lies in rethinking how enterprises approach AI. Instead of moving sensitive data to external platforms, organizations should adopt Private AI: a model where workloads run inside secure boundaries, where models move to the data, and where enterprises maintain complete control. Private AI makes it possible to access any type of data, at any […]
