GRACE: A Scalable Graph-Based Approach to Accelerating Recommendation Model Inference-Reference-Cited by-同舟云学术

GRACE: A Scalable Graph-Based Approach to Accelerating Recommendation Model Inference

Published:2023-03-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3
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Author:

Ye Haojie¹,Vedula Sanketh²,Chen Yuhan¹,Yang Yichen¹,Bronstein Alex²,Dreslinski Ronald¹,Mudge Trevor¹,Talati Nishil¹

Affiliation:

1. University of Michigan, USA

2. Technion, Israel

Funder

Defense Advanced Research Projects Agency

Binational Science Foundation

Publisher

ACM

Reference78 articles.

1. Muhammad Adnan , Yassaman Ebrahimzadeh Maboud , Divya Mahajan, and Prashant J Nair. 2021 . Accelerating recommendation system training by leveraging popular choices. arXiv preprint arXiv:2103.00686. Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan, and Prashant J Nair. 2021. Accelerating recommendation system training by leveraging popular choices. arXiv preprint arXiv:2103.00686.

2. My anime list. 2016. Anime recommendations database. https://www.kaggle.com/CooperUnion/anime-recommendations-database My anime list. 2016. Anime recommendations database. https://www.kaggle.com/CooperUnion/anime-recommendations-database

3. SNU Architecture and Code Optimization (ARC) Lab. 2021. MERCI Code Repository. https://github.com/SNU-ARC/MERCI SNU Architecture and Code Optimization (ARC) Lab. 2021. MERCI Code Repository. https://github.com/SNU-ARC/MERCI

4. FAFNIR: Accelerating Sparse Gathering by Using Efficient Near-Memory Intelligent Reduction

5. AsmDB

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