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Analytical Performance Estimation during Code Generation on Modern GPUs
[article]
2022
arXiv
pre-print
Automatic code generation is frequently used to create implementations of algorithms specifically tuned to particular hardware and application parameters. The code generation process involves the selection of adequate code transformations, tuning parameters, and parallelization strategies. We propose an alternative to time-intensive autotuning, scenario-specific performance models, or black-box machine learning to select the best-performing configuration. This paper identifies the relevant
arXiv:2204.14242v1
fatcat:csnykfll3bc5xmfmd3vrbugjmy