45 citations · 62 across the 10 of their papers we have counts for
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cs.DC2026
Architecting for Hardware Diversity: Meta's End-to-End Co-Design for High-Performance AI Training on AMD GPUs
Haoyu Zhang, Arshad Rahman, Adnan Aziz +55
Meta deploys a heterogeneous fleet comprising NVIDIA GPUs, AMD GPUs, and proprietary MTIA accelerators to reduce costs and mitigate vendor lock-in. However, the extensive history o…
cs.DC2020★ 45 cited
TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with Auto-Parallelism
Zhenkun Cai, Kaihao Ma, Xiao Yan +5
A good parallelization strategy can significantly improve the efficiency or reduce the cost for the distributed training of deep neural networks (DNNs). Recently, several methods h…
cs.DC2016★ 12 cited
Efficient Processing of Very Large Graphs in a Small Cluster
Da Yan, Yuzhen Huang, James Cheng +1
Inspired by the success of Google's Pregel, many systems have been developed recently for iterative computation over big graphs. These systems provide a user-friendly vertex-centri…