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BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing.

, , , , , , , , , and . NSDI, page 103-118. USENIX Association, (2023)

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Improved Smoothed Round Robin Schedulers for High-Speed Packet Networks.. INFOCOM, page 906-914. IEEE, (2008)Generic Application-Level Protocol Analyzer and its Language, , , and . #NDSS07#, (2007)Pingmesh: A Large-Scale System for Data Center Network Latency Measurement and Analysis., , , , , , , , , and 2 other author(s). SIGCOMM, page 139-152. ACM, (2015)BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing., , , , , , , , , and . NSDI, page 103-118. USENIX Association, (2023)RDMA over Commodity Ethernet at Scale., , , , , , and . SIGCOMM, page 202-215. ACM, (2016)FAERY: An FPGA-accelerated Embedding-based Retrieval System., , , , , , , , and . OSDI, page 841-856. USENIX Association, (2022)Predicting GPU Failures With High Precision Under Deep Learning Workloads., , , , , , and . SYSTOR, page 124-135. ACM, (2023)SRR: An O(1) time complexity packet scheduler for flows in multi-service packet networks.. SIGCOMM, page 211-222. ACM, (2001)Tiresias: A GPU Cluster Manager for Distributed Deep Learning., , , , , , , and . NSDI, page 485-500. USENIX Association, (2019)Optimus: an efficient dynamic resource scheduler for deep learning clusters., , , , and . EuroSys, page 3:1-3:14. ACM, (2018)