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A Simple Yet Effective Graph Partition Model for GPU Computing.. ICPP Workshops, стр. 35:1-35:2. ACM, (2018)Exploiting statistical correlations for proactive prediction of program behaviors., , , , , , и . CGO, стр. 248-256. ACM, (2010)Decoding CUDA Binary., , , , и . CGO, стр. 229-241. IEEE, (2019)A Pulse Generation Framework with Augmented Program-aware Basis Gates and Criticality Analysis., , , , , , и . HPCA, стр. 773-786. IEEE, (2023)On-the-fly elimination of dynamic irregularities for GPU computing., , , , и . ASPLOS, стр. 369-380. ACM, (2011)GPU Taint Tracking., , , , , и . USENIX ATC, стр. 209-220. USENIX Association, (2017)Is Reuse Distance Applicable to Data Locality Analysis on Chip Multiprocessors?, , , и . CC, том 6011 из Lecture Notes in Computer Science, стр. 264-282. Springer, (2010)A Graph-based Model for GPU Caching Problems., , , , , и . CoRR, (2016)Orion: A Framework for GPU Occupancy Tuning., , , , и . Middleware, стр. 18. ACM, (2016)Streamlining GPU applications on the fly: thread divergence elimination through runtime thread-data remapping., , , и . ICS, стр. 115-126. ACM, (2010)