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Exploiting Task-Parallelism on GPU Clusters via OmpSs and rCUDA Virtualization.

, , , and . TrustCom/BigDataSE/ISPA (3), page 160-165. IEEE, (2015)978-1-4673-7952-6.

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RED-SEA: Network Solution for Exascale Architectures., , , , , , , , , and 58 other author(s). DSD, page 712-719. IEEE, (2022)GLTO: On the Adequacy of Lightweight Thread Approaches for OpenMP Implementations., , , , , and . ICPP, page 60-69. IEEE Computer Society, (2017)Analysis of model parallelism for distributed neural networks., , , and . EuroMPI, page 7:1-7:10. ACM, (2019)A Flexible Research-Oriented Framework for Distributed Training of Deep Neural Networks., , , , and . IPDPS Workshops, page 730-739. IEEE, (2021)Automatic generation of ARM NEON micro-kernels for matrix multiplication., , , , , , and . J. Supercomput., 80 (10): 13873-13899 (July 2024)Automatic Generators for a Family of Matrix Multiplication Routines with Apache TVM., , , , , and . CoRR, (2023)Performance Analysis of Matrix Multiplication for Deep Learning on the Edge., , , and . CoRR, (2024)Reformulating the direct convolution for high-performance deep learning inference on ARM processors., , , , , , , and . J. Syst. Archit., (February 2023)Evaluation of MPI Allreduce for Distributed Training of Convolutional Neural Networks., , , , , and . PDP, page 109-116. IEEE, (2021)Anatomy of the BLIS Family of Algorithms for Matrix Multiplication., , and . PDP, page 92-99. IEEE, (2022)