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GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning.

, , , , , , , , , , , and . Proc. ACM Manag. Data, 1 (2): 193:1-193:25 (2023)

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EasyScale: Elastic Training with Consistent Accuracy and Improved Utilization on GPUs., , , , , , , , , and 2 other author(s). SC, page 55:1-55:14. ACM, (2023)GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning., , , , , , , , , and 2 other author(s). Proc. ACM Manag. Data, 1 (2): 193:1-193:25 (2023)Samsara: Efficient Deterministic Replay in Multiprocessor Environments with Hardware Virtualization Extensions., , , , and . USENIX Annual Technical Conference, page 551-564. USENIX Association, (2016)EasyScale: Accuracy-consistent Elastic Training for Deep Learning., , , , , , , , , and 2 other author(s). CoRR, (2022)Samsara: Efficient Deterministic Replay with Hardware Virtualization Extensions., , , and . APSys, page 9:1-9:7. ACM, (2015)Leveraging Hardware-Assisted Virtualization for Deterministic Replay on Commodity Multi-Core Processors., , , , and . IEEE Trans. Computers, 67 (1): 45-58 (2018)Phantasy: Low-Latency Virtualization-Based Fault Tolerance via Asynchronous Prefetching., , , and . IEEE Trans. Computers, 68 (2): 225-238 (2019)AntMan: Dynamic Scaling on GPU Clusters for Deep Learning., , , , , , , , and . OSDI, page 533-548. USENIX Association, (2020)