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FastDimeNet++: Training DimeNet++ in 22 minutes., , , , , , , , , and 4 other author(s). ICPP, page 274-284. ACM, (2023)Learning-based analytical cross-platform performance prediction., , , and . SAMOS, page 52-59. IEEE, (2015)CAMP: Accurate modeling of core and memory locality for proxy generation of big-data applications., , , and . DATE, page 337-342. IEEE, (2018)Accurate phase-level cross-platform power and performance estimation., , and . DAC, page 4:1-4:6. ACM, (2016)LACross: Learning-Based Analytical Cross-Platform Performance and Power Prediction., , and . Int. J. Parallel Program., 45 (6): 1488-1514 (2017)Fine-grained power analysis of emerging graph processing workloads for cloud operations management., , , and . IEEE BigData, page 2121-2126. IEEE Computer Society, (2016)Genesys: Automatically generating representative training sets for predictive benchmarking., , , , , , and . SAMOS, page 116-123. IEEE, (2016)Statistical Pattern Based Modeling of GPU Memory Access Streams., , , , and . DAC, page 81:1-81:6. ACM, (2017)Accurate address streams for LLC and beyond (SLAB): A methodology to enable system exploration., , and . ISPASS, page 87-96. IEEE Computer Society, (2017)Sampling-based binary-level cross-platform performance estimation., , , , and . DATE, page 1709-1714. IEEE, (2017)