https://ultraethernet.org/
Nvidia доигралась с поглощением производителей Inifiniband
Nvidia доигралась с поглощением производителей Inifiniband
Ultra Ethernet Consortium
Delivering an Ethernet based open, interoperable, high performance, full-communications stack architecture to meet the growing network demands of AI & HPC at scale.
🤔1
byteflow
https://arxiv.org/pdf/2310.03742.pdf
Novel low-diameter network topologies such as Slim Fly (SF)
offer significant cost and power advantages over the established Fat Tree, Clos, or Dragonfly. To spearhead the adoption
of low-diameter networks, we design, implement, deploy, and
evaluate the first real-world SF installation. We focus on deployment, management, and operational aspects of our test
cluster with 200 servers and carefully analyze performance.
We demonstrate techniques for simple cabling and cabling
validation as well as a novel high-performance routing architecture for InfiniBand-based low-diameter topologies. Our
real-world benchmarks show SF’s strong performance for
many modern workloads such as deep neural network training, graph analytics, or linear algebra kernels. SF outperforms
non-blocking Fat Trees in scalability while offering comparable or better performance and lower cost for large network
sizes. Our work can facilitate deploying SF while the associated (open-source)1
routing architecture is fully portable and
applicable to accelerate any low-diameter interconnect.
offer significant cost and power advantages over the established Fat Tree, Clos, or Dragonfly. To spearhead the adoption
of low-diameter networks, we design, implement, deploy, and
evaluate the first real-world SF installation. We focus on deployment, management, and operational aspects of our test
cluster with 200 servers and carefully analyze performance.
We demonstrate techniques for simple cabling and cabling
validation as well as a novel high-performance routing architecture for InfiniBand-based low-diameter topologies. Our
real-world benchmarks show SF’s strong performance for
many modern workloads such as deep neural network training, graph analytics, or linear algebra kernels. SF outperforms
non-blocking Fat Trees in scalability while offering comparable or better performance and lower cost for large network
sizes. Our work can facilitate deploying SF while the associated (open-source)1
routing architecture is fully portable and
applicable to accelerate any low-diameter interconnect.