GPUs are expensive resources but often underutilized. This inefficiency is exacerbated by the increasing diversity of AI workloads, where small or specialized models often fail to saturate high-capacity devices, necessitating GPU sharing. Existing sharing approaches force a hard tradeoff. They either partition hardware...
Yi-Cheng Liu, Yi-Fan Qiao, Tian Xia et al.· Proceedings of the 32nd ACM...· 0 citations
This work presents EMA, a memory sharing system that allows GPUs within a server to borrow and reclaim memory from each other, forming an elastic pool of capacity, and ensures performance transparency for both borrowers and lenders.
Yi Xu, Tian Xia, Ion Stoica· arXiv.org· 0 citations
Communication has become a bottleneck in distributed training and inference of large models. Overlapping communication with computation at the granularity of kernels, on separate streams, reduces only part of this communication cost. Fused kernels often have better performance by transmitting each output tile as soon a...
Zi-Ming Mao, Yi-Han Zhang, S. W. Chew et al.· 1 citation
Evaluating leading frontier and open-source code generation models on both intra-node NVLink and inter-node RDMA platforms reveals that even the strongest model, GPT-5.5, correctly implements and achieves competitive performance on only 30.7\% of the benchmark tasks.
Shuang Ma, Yu-Yi Li, Yihan Zhang et al.· 0 citations
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