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DLRM: mixed-feature recommendation and hybrid parallelism benchmark

Combined sparse categorical embeddings, dense-feature MLP processing, explicit feature interactions and a top predictor, with model-parallel embeddings and data-parallel dense layers for systems study.

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Combined sparse categorical embeddings, dense-feature MLP processing, explicit feature interactions and a top predictor, with model-parallel embeddings and data-parallel dense layers for systems study.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

Integrates inherited factorization/MLP techniques rather than inventing recommendation. Public model/benchmark is not the complete confidential production recommender or every later optimized implementation. native BigBasin benchmarks do not certify user benefit, fairness or universal hardware superiority.

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People and systems

Maxim Naumov; Dheevatsa Mudigere; Hao-Jun Michael Shi; Jianyu Huang; Narayanan Sundaraman; Jongsoo Park; Xiaodong Wang; Udit Gupta; Carole-Jean Wu; Alisson G. Azzolini; Dmytro Dzhulgakov; Andrey Mallevich; Ilia Cherniavskii; Yinghai Lu; Raghuraman Krishnamoorthi; Ansha Yu; Volodymyr Kondratenko; Stephanie Pereira; Xianjie Chen; Wenlin Chen; Vijay Rao; Bill Jia; Liang Xiong; Misha Smelyanskiy

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

DLRM: mixed-feature recommendation and hybrid parallelism benchmark

Combined sparse categorical embeddings, dense-feature MLP processing, explicit feature interactions and a top predictor, with model-parallel embeddings and data-parallel dense layers for systems study.

Scientific scope

Integrates inherited factorization/MLP techniques rather than inventing recommendation. Public model/benchmark is not the complete confidential production recommender or every later optimized implementation. native BigBasin benchmarks do not certify user benefit, fairness or universal hardware superiority.

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

Maxim Naumov; Dheevatsa Mudigere; Hao-Jun Michael Shi; Jianyu Huang; Narayanan Sundaraman; Jongsoo Park; Xiaodong Wang; Udit Gupta; Carole-Jean Wu; Alisson G. Azzolini; Dmytro Dzhulgakov; Andrey Mallevich; Ilia Cherniavskii; Yinghai Lu; Raghuraman Krishnamoorthi; Ansha Yu; Volodymyr Kondratenko; Stephanie Pereira; Xianjie Chen; Wenlin Chen; Vijay Rao; Bill Jia; Liang Xiong; Misha Smelyanskiy

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.

Original sources