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1
FasterMoE : modeling and optimizing training of large-sc..:
, In:
Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
,
He, Jiaao
;
Zhai, Jidong
;
Antunes, Tiago
... - p. 120-134 , 2022
Link:
https://dl.acm.org/doi/10.1145/3503221.3508418
RT T1 <cTitle>Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming</cTitle>: T1 <Title>FasterMoE : modeling and optimizing training of large-scale dynamic pre-trained models</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3508418&Exemplar=1&LAN=DE A1 He, Jiaao A1 Zhai, Jidong A1 Antunes, Tiago A1 Wang, Haojie A1 Luo, Fuwen A1 Shi, Shangfeng A1 Li, Qin PB ACM YR 2022 K1 distributed deep learning K1 parallelism K1 performance modeling K1 Computing methodologies K1 Parallel computing methodologies K1 Parallel algorithms K1 Massively parallel algorithms SP 120 OP 134 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3503221.3508418 DO https://dl.acm.org/doi/10.1145/3503221.3508418 SF ELIB - SuUB Bremen
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