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DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce Pipelining.

, , , , , and . ICDCS, page 142-153. IEEE, (2023)

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Decoupling the All-Reduce Primitive for Accelerating Distributed Deep Learning., , , , , and . CoRR, (2023)Benchmarking the Memory Hierarchy of Modern GPUs., , , and . NPC, volume 8707 of Lecture Notes in Computer Science, page 144-156. Springer, (2014)The Design and Implementation of OMPit: An OpenMP Compiler Characterized by Logs for Parallel and Work-Sharing., , , and . PAAP, page 350-355. IEEE Computer Society, (2011)Efficient Data Loading for Deep Neural Network Training., , , , and . BIGCOM, page 211-218. IEEE, (2023)DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce Pipelining., , , , , and . ICDCS, page 142-153. IEEE, (2023)MAP-numa: Access Patterns Used to Characterize the NUMA Memory Access Optimization Techniques and Algorithms., , , and . NPC, volume 7513 of Lecture Notes in Computer Science, page 208-216. Springer, (2012)Towards more efficient ophthalmic disease classification and lesion location via convolution transformer., , , , , , , , and . Comput. Methods Programs Biomed., (2022)GPGPU performance estimation for frequency scaling using cross-benchmarking., , and . GPGPU@PPoPP, page 31-40. ACM, (2020)The System of Distribution Network Live Working Robot Based on Multi-level Insulation Design and Human-machine Collaboration., , , and . RICAI, page 272-276. ACM, (2022)A Quantitative Survey of Communication Optimizations in Distributed Deep Learning., , , , , and . IEEE Netw., 35 (3): 230-237 (2021)