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Tux2: Distributed Graph Computation for Machine Learning., , , , , , , и . NSDI, стр. 669-682. USENIX Association, (2017)EasyScale: Elastic Training with Consistent Accuracy and Improved Utilization on GPUs., , , , , , , , , и 2 other автор(ы). SC, стр. 55:1-55:14. ACM, (2023)Crux: GPU-Efficient Communication Scheduling for Deep Learning Training., , , , , , , , и . SIGCOMM, стр. 1-15. ACM, (2024)An empirical study on program failures of deep learning jobs., , , , , и . ICSE, стр. 1159-1170. ACM, (2020)Balanced Sparsity for Efficient DNN Inference on GPU., , , , и . CoRR, (2018)Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache., , , , , , , , , и 3 other автор(ы). CoRR, (2024)CoGNN: Efficient Scheduling for Concurrent GNN Training on GPUs., , , , , , , , , и 1 other автор(ы). SC, стр. 39:1-39:15. IEEE, (2022)KV-Direct: High-Performance In-Memory Key-Value Store with Programmable NIC., , , , , , , и . SOSP, стр. 137-152. ACM, (2017)Efficient and Effective Sparse LSTM on FPGA with Bank-Balanced Sparsity., , , , , , , , и . FPGA, стр. 63-72. ACM, (2019)Balanced Sparsity for Efficient DNN Inference on GPU., , , , и . AAAI, стр. 5676-5683. AAAI Press, (2019)