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Exploiting Parallelism Opportunities with Deep Learning Frameworks., , , , and . CoRR, (2019)HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning., , , , , , and . CoRR, (2024)Understanding Scaling Laws for Recommendation Models., , , , and . CoRR, (2022)MCM-GPU: Multi-Chip-Module GPUs for Continued Performance Scalability., , , , , , , , and . ISCA, page 320-332. ACM, (2017)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)Energy-Efficient Mapping for a Network of DNN Models at the Edge., , , , , and . SMARTCOMP, page 25-30. IEEE, (2021)Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity., , , , , and . RecSys, page 156-167. ACM, (2022)Understanding the Power of Evolutionary Computation for GPU Code Optimization., , , , and . IISWC, page 185-198. IEEE, (2022)Carbon Explorer: A Holistic Framework for Designing Carbon Aware Datacenters., , , , , , , and . ASPLOS (2), page 118-132. ACM, (2023)