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Understanding Scaling Laws for Recommendation Models., , , , and . CoRR, (2022)HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning., , , , , , and . CoRR, (2024)Exploiting Parallelism Opportunities with Deep Learning Frameworks., , , , and . CoRR, (2019)MCM-GPU: Multi-Chip-Module GPUs for Continued Performance Scalability., , , , , , , , and . ISCA, page 320-332. ACM, (2017)Energy-Efficient Mapping for a Network of DNN Models at the Edge., , , , , and . SMARTCOMP, page 25-30. IEEE, (2021)AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning., and . MICRO, page 183-198. ACM, (2021)Hercules: Heterogeneity-Aware Inference Serving for At-Scale Personalized Recommendation., , , , , and . HPCA, page 141-154. IEEE, (2022)DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference., , , , , , , , and . ISCA, page 982-995. IEEE, (2020)Understanding data storage and ingestion for large-scale deep recommendation model training: industrial product., , , , , , , , , and 7 other author(s). ISCA, page 1042-1057. ACM, (2022)Carbon Explorer: A Holistic Framework for Designing Carbon Aware Datacenters., , , , , , , and . ASPLOS (2), page 118-132. ACM, (2023)