RNG deploys the first production flat datacenter network using quasi-random graphs, a new distributed routing protocol, and a passive optical cabling shuffle device, achieving fat-tree performance at substantially lower cost.
ByteScale: Communication-Efficient Scaling of LLM Training with a 2048K Context Length on 16384 GPUs , url=
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SCENIC delivers a programmable 200G SmartNIC with offloaded protocol stacks, stream compute units, and full OS transparency that matches commercial performance for custom offloads like collective communication and GPU data partitioning.
FCP shards sequences at block level with flexible P2P communication and bin-packing to achieve near-linear scaling up to 256 GPUs and 1.13x-2.21x higher attention MFU in foundation model pre-training.
citing papers explorer
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RNG: Flat Datacenter Networks at Scale
RNG deploys the first production flat datacenter network using quasi-random graphs, a new distributed routing protocol, and a passive optical cabling shuffle device, achieving fat-tree performance at substantially lower cost.
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SCENIC: Stream Computation-Enhanced SmartNIC
SCENIC delivers a programmable 200G SmartNIC with offloaded protocol stacks, stream compute units, and full OS transparency that matches commercial performance for custom offloads like collective communication and GPU data partitioning.
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Unleashing Scalable Context Parallelism for Foundation Models Pre-Training via FCP
FCP shards sequences at block level with flexible P2P communication and bin-packing to achieve near-linear scaling up to 256 GPUs and 1.13x-2.21x higher attention MFU in foundation model pre-training.