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