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A Distributed Synchronous SGD Algorithm with Global Top-k Sparsification for Low Bandwidth Networks.

, , , , , , and . ICDCS, page 2238-2247. IEEE, (2019)

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MG-WFBP: Efficient Data Communication for Distributed Synchronous SGD Algorithms., and . CoRR, (2018)Mixed Precision Method for GPU-based FFT., , and . CSE, page 580-586. IEEE Computer Society, (2011)Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters., , , , , , , , , and 14 other author(s). MLSys, mlsys.org, (2021)Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters., , , , , , , , , and 8 other author(s). CoRR, (2022)Communication-Efficient Distributed Deep Learning with Merged Gradient Sparsification on GPUs., , , , , , and . INFOCOM, page 406-415. IEEE, (2020)FedImpro: Measuring and Improving Client Update in Federated Learning., , , , , , and . ICLR, OpenReview.net, (2024)Reliable and Efficient In-Memory Fault Tolerance of Large Language Model Pretraining., , , , , , , , , and . CoRR, (2023)FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training., , , , , , , , , and . CoRR, (2023)PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pipelining., , , and . INFOCOM, page 1-10. IEEE, (2023)Accelerating Distributed K-FAC with Efficient Collective Communication and Scheduling., , and . INFOCOM, page 1-10. IEEE, (2023)