Enabling Efficient Large Recommendation Model Training with Near CXL Memory Processing

Personalized recommendation systems have become one of the most important Internet services nowadays. A critical challenge of training and deploying the recommendation models is their high memory capacity and bandwidth demands, with the embedding layers occupying hundreds of GBs to TBs of storage. T...

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Veröffentlicht in:	2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA) S. 382 - 395
Hauptverfasser:	Liu, Haifeng, Zheng, Long, Huang, Yu, Zhou, Jingyi, Liu, Chaoqiang, Wang, Runze, Liao, Xiaofei, Jin, Hai, Xue, Jingling
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 29.06.2024
Schlagworte:	Bandwidth Data transfer Degradation Hardware Memory management Prefetching Recommender systems Scalability Training Web and internet services
Online-Zugang:	Volltext
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