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PAI-FCNN: FPGA Based Inference System for Complex CNN Models., , , , , , , , , and 1 other author(s). ASAP, page 107-114. IEEE, (2019)DAPPLE: A Pipelined Data Parallel Approach for Training Large Models., , , , , , , , , and 3 other author(s). CoRR, (2020)Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform., , , , , , , , , and . CoRR, (2023)Optimizing distributed training deployment in heterogeneous GPU clusters., , , , , , , , and . CoNEXT, page 93-107. ACM, (2020)DISC: A Dynamic Shape Compiler for Machine Learning Workloads., , , , , , , , , and . EuroMLSys@EuroSys, page 89-95. ACM, (2021)Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment., , , , , and . CoRR, (2023)Accelerating large-scale distributed neural network training with SPMD parallelism., , , , and . SoCC, page 403-418. ACM, (2022)PAI-FCNN: FPGA Based CNN Inference System., , , , , , , , , and 1 other author(s). FPGA, page 184. ACM, (2019)FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads., , , , , , , , and . CoRR, (2020)HAP: SPMD DNN Training on Heterogeneous GPU Clusters with Automated Program Synthesis., , , , , and . EuroSys, page 524-541. ACM, (2024)