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MIGPerf: A Comprehensive Benchmark for Deep Learning Training and Inference Workloads on Multi-Instance GPUs.

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Tux2: Distributed Graph Computation for Machine Learning., , , , , , , and . NSDI, page 669-682. USENIX Association, (2017)An empirical study on program failures of deep learning jobs., , , , , and . ICSE, page 1159-1170. ACM, (2020)EasyScale: Elastic Training with Consistent Accuracy and Improved Utilization on GPUs., , , , , , , , , and 2 other author(s). SC, page 55:1-55:14. ACM, (2023)Balanced Sparsity for Efficient DNN Inference on GPU., , , , and . CoRR, (2018)KV-Direct: High-Performance In-Memory Key-Value Store with Programmable NIC., , , , , , , and . SOSP, page 137-152. ACM, (2017)Efficient and Effective Sparse LSTM on FPGA with Bank-Balanced Sparsity., , , , , , , , and . FPGA, page 63-72. ACM, (2019)Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache., , , , , , , , , and 3 other author(s). CoRR, (2024)CoGNN: Efficient Scheduling for Concurrent GNN Training on GPUs., , , , , , , , , and 1 other author(s). SC, page 39:1-39:15. IEEE, (2022)SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization., , , , , , , and . CVPR, page 11216-11225. Computer Vision Foundation / IEEE, (2019)GraM: scaling graph computation to the trillions., , , , , , , , and . SoCC, page 408-421. ACM, (2015)