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detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models", "url": "https://arxiv.org/abs/2104.05158v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Chonglin Sun", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models", "url": "https://arxiv.org/abs/2104.05158v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Whitney Zhao", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models", "url": "https://arxiv.org/abs/2104.05158v1" } } ], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models", "url": "https://arxiv.org/abs/2104.05158v1" } }, { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-1", "kind": "source_reference", "source": { "citation": "Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models", "url": "https://arxiv.org/abs/2104.05158v7" } } ], "origin": "HUMAN_LEGACY", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "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.", "domain": "Distributed recommendation training" }, "sources": [ { "citation": "High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models", "url": "https://arxiv.org/abs/2104.05158v1" }, { "citation": "Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models", "url": "https://arxiv.org/abs/2104.05158v7" } ], "statement": "# Neo/ZionEX: co-designed distributed recommendation training\n\nCombined 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.\n\n## Scientific scope\n\nOne2021 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.\n\n## Reading status\n\nThis 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.\n\n## People and systems\n\nDheevatsa 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\n\nSource-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.\n\n\n\n## Original sources\n\n- [High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models](https://arxiv.org/abs/2104.05158v1)\n- [Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models](https://arxiv.org/abs/2104.05158v7)\n", "summary": "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.", "title": "Neo/ZionEX: co-designed distributed recommendation training" }, "record_id": "urn:uuid:400e5070-8da1-4a64-a592-13522eb9d09e", "version": 1, "source_ref": { "record_id": "urn:uuid:400e5070-8da1-4a64-a592-13522eb9d09e", "version": 1, "content_hash": "sha256:347fc2d5c78835d6bb68e06eb8dce68bf074d7699eed7a110dd37b11cc7e953e" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:9c909809a907515976137c950b24a8ec439a5af81325fcd95ea1e034f2ff4b5c" }, "target": { "record_id": "urn:uuid:400e5070-8da1-4a64-a592-13522eb9d09e", "version": 1, "content_hash": "sha256:347fc2d5c78835d6bb68e06eb8dce68bf074d7699eed7a110dd37b11cc7e953e" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "mudigere-et-al-neo-zionex-2021", "entry_id": "mudigere-et-al-neo-zionex-2021", "revision": 1, "title": "Neo/ZionEX: co-designed distributed recommendation training", "context": "Distributed recommendation training", "track": "Human-designed research method or resource", "band": "A", "modifier": null, "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "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.", "scope": "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. 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.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. 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. 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