Multi-plane HyperX achieves significantly smaller network diameter and superior cost-effectiveness versus multi-plane Fat-Tree, Dragonfly, and Dragonfly+ for large AI/HPC systems.
In2024 IEEE Symposium on High-Performance Interconnects (HOTI)
2 Pith papers cite this work. Polarity classification is still indexing.
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Embedding CUDA Graphs in UCX for multi-path intra-node GPU communication yields up to 2.95x bandwidth improvement over single-path UCX on a four-GPU node for large messages.
citing papers explorer
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Multi-Plane HyperX: A Low-Latency and Cost-Effective Network for Large-Scale AI and HPC Systems
Multi-plane HyperX achieves significantly smaller network diameter and superior cost-effectiveness versus multi-plane Fat-Tree, Dragonfly, and Dragonfly+ for large AI/HPC systems.
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Accelerating Intra-Node GPU-to-GPU Communication Through Multi-Path Transfers with CUDA Graphs
Embedding CUDA Graphs in UCX for multi-path intra-node GPU communication yields up to 2.95x bandwidth improvement over single-path UCX on a four-GPU node for large messages.