Large language model (LLM) inference in AI datacenters creates a coupled control problem between GPU serving and facility cooling. Raising ambient temperature setpoints can reduce cooling energy and carbon, but also shrinks thermal headroom, induces GPU throttling, and leads to Service-Level-Objective (SLO) violations....
Rui Lu, Rui Ge, Huang-Huang Liang et al.· 0 citations
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, the substantial memory footprint required for model training imposes a critical memory wall on resource-constrained edge devices, severely limiting their participation and compromising the inc...
Yebo Wu, Jing-Guang Li, Chunlin Tian et al.· IEEE Transactions on Paralle...· 1 citation
Ethereum execution clients implement identical Ethereum Virtual Machine (EVM) semantics, yet can exhibit substantially different performance in practice. We study this divergence through a three-step systems analysis of two representative clients, Geth and Reth. First, across historical synchronization, live synchroniz...
Chon Kit Lao, Nora Sinong Lu, Jing-Yi Ning et al.· International Conference on...· 0 citations
Training GNNs on large-scale graphs imposes significant memory constraints for storing substantial amounts of graph structures and node features. This often necessitates the use of memory extensions such as SSDs, leading to a memory hierarchy with disparities in capacity and access speed. Existing approaches focus on m...
Junkun Shen, Yuezhi Che, Haoran Zhou et al.· IEEE Transactions on Paralle...· 0 citations
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