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FuncyTuner: Auto-tuning Scientific Applications With Per-loop Compilation., , , , , and . ICPP, page 91:1-91:10. ACM, (2019)Extending OpenMP for Machine Learning-Driven Adaptation., , , , , , and . WACCPD@SC, volume 13194 of Lecture Notes in Computer Science, page 49-69. Springer, (2021)Preparation and optimization of a diverse workload for a large-scale heterogeneous system., , , , , , , , , and 69 other author(s). SC, page 32:1-32:17. ACM, (2019)RAJA: Portable Performance for Large-Scale Scientific Applications., , , , , , , , , and . P3HPC@SC, page 71-81. IEEE, (2019)Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems., , , , , and . CoRR, (2023)Performance portable C++ programming with RAJA., , , and . PPoPP, page 455-456. ACM, (2019)Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning., , , , , , , and . ISC, volume 12728 of Lecture Notes in Computer Science, page 453-472. Springer, (2021)CodeSeer: input-dependent code variants selection via machine learning., , , , , and . ICS, page 43:1-43:11. ACM, (2020)