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LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization., , , , and . CoRR, (2024)POSTER: LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization., , , , and . PPoPP, page 460-462. ACM, (2024)CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs., , , , , , and . EuroSys, page 1054-1074. ACM, (2024)3D reconstruction of complex geometric solids from 2D line drawings., and . SIGGRAPH ASIA Posters, page 9. ACM, (2013)CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs., , , , , , and . CoRR, (2023)Dive into Deep Learning for Natural Language Processing., , , , , , and . EMNLP/IJCNLP (2), Association for Computational Linguistics, (2019)Hi-Speed DNN Training with Espresso: Unleashing the Full Potential of Gradient Compression with Near-Optimal Usage Strategies., , , and . EuroSys, page 867-882. ACM, (2023)SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training., , , , , , , and . NeurIPS, (2022)Temporal-Contextual Recommendation in Real-Time., , , and . KDD, page 2291-2299. ACM, (2020)MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs., , , , , , , , , and 22 other author(s). NSDI, page 745-760. USENIX Association, (2024)