What is recorded here?
Combined table/row/column/data-parallel embedding placement, fused sparse operators, hierarchical memory and a dedicated scale-out network in a PyTorch-based training stack for very large recommendation models.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
One2021 preprint/2022 publication family, with the expanded2023-stamped v7 byline retained separately. Four-dimensional embedding parallelism is not WLB-LLM's DP/TP/PP/CP scheme. Placement uses cost models and heuristics rather than globally optimal observed runtime; column sharding can affect row-wise optimizer semantics. Reported speed and model capacity are configuration-specific.
Read the original work
- High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models
- Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Dheevatsa Mudigere; Yuchen Hao; Jianyu Huang; Andrew Tulloch; Srinivas Sridharan; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie (Amy) Yang; Leon Gao; Dmytro Ivchenko; Aarti Basant; Yuxi Hu; Jiyan Yang; Ehsan K. Ardestani; Xiaodong Wang; Rakesh Komuravelli; Ching-Hsiang Chu; Serhat Yilmaz; Huayu Li; Jiyuan Qian; Zhuobo Feng; Yinbin Ma; Junjie Yang; Ellie Wen; Hong Li; Lin Yang; Chonglin Sun; Whitney Zhao; Krishna Dhulipala; KR Kishore; Tyler Graf; Assaf Eisenman; Kiran Kumar Matam; Adi Gangidi; Pallab Bhattacharya; Guoqiang Jerry Chen; Manoj Krishnan; Krishnakumar Nair; Petr Lapukhov; Maxim Naumov; Lin Qiao; Mikhail Smelyanskiy; Bill Jia; Vijay Rao; Zhihao Jia; Dimitry Melts; Avinash Nayak; Bharath Muthiah; Mahmoud khorashadi; Ajit Mathews
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Neo/ZionEX: co-designed distributed recommendation training
Combined table/row/column/data-parallel embedding placement, fused sparse operators, hierarchical memory and a dedicated scale-out network in a PyTorch-based training stack for very large recommendation models.
Scientific scope
One2021 preprint/2022 publication family, with the expanded2023-stamped v7 byline retained separately. Four-dimensional embedding parallelism is not WLB-LLM's DP/TP/PP/CP scheme. Placement uses cost models and heuristics rather than globally optimal observed runtime; column sharding can affect row-wise optimizer semantics. Reported speed and model capacity are configuration-specific.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Dheevatsa Mudigere; Yuchen Hao; Jianyu Huang; Andrew Tulloch; Srinivas Sridharan; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie (Amy) Yang; Leon Gao; Dmytro Ivchenko; Aarti Basant; Yuxi Hu; Jiyan Yang; Ehsan K. Ardestani; Xiaodong Wang; Rakesh Komuravelli; Ching-Hsiang Chu; Serhat Yilmaz; Huayu Li; Jiyuan Qian; Zhuobo Feng; Yinbin Ma; Junjie Yang; Ellie Wen; Hong Li; Lin Yang; Chonglin Sun; Whitney Zhao; Krishna Dhulipala; KR Kishore; Tyler Graf; Assaf Eisenman; Kiran Kumar Matam; Adi Gangidi; Pallab Bhattacharya; Guoqiang Jerry Chen; Manoj Krishnan; Krishnakumar Nair; Petr Lapukhov; Maxim Naumov; Lin Qiao; Mikhail Smelyanskiy; Bill Jia; Vijay Rao; Zhihao Jia; Dimitry Melts; Avinash Nayak; Bharath Muthiah; Mahmoud khorashadi; Ajit Mathews
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.