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Accelerator design with decoupled hardware customizations: benefits and challenges: invited., , , , , , , , , and 1 other author(s). DAC, page 1351-1354. ACM, (2022)Data-flow/dependence profiling for structured transformations., , , , , , and . PPoPP, page 173-185. ACM, (2019)SDSLc: a multi-target domain-specific compiler for stencil computations., , , , , , , , and . WOLFHPC@SC, page 6:1-6:10. ACM, (2015)PIPES: a language and compiler for task-based programming on distributed-memory clusters., , , and . SC, page 456-467. IEEE Computer Society, (2016)Automated derivation of parametric data movement lower bounds for affine programs., , , , and . PLDI, page 808-822. ACM, (2020)MARTA: Multi-configuration Assembly pRofiler and Toolkit for performance Analysis., , , and . ISPASS, page 79-89. IEEE, (2022)Using machine learning to improve automatic vectorization., , and . ACM Trans. Archit. Code Optim., 8 (4): 50:1-50:23 (2012)Beyond reuse distance analysis: Dynamic analysis for characterization of data locality potential., , , , , , , and . ACM Trans. Archit. Code Optim., 10 (4): 53:1-53:29 (2013)Beyond Reuse Distance Analysis: Dynamic Analysis for Characterization of Data Locality Potential., , , , , , , and . CoRR, (2014)High-performance code generation for stencil computations on GPU architectures., , and . ICS, page 311-320. ACM, (2012)