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This work proposes an autotuning framework that designs a machine learning-based ensemble LLVM Intermediate Representation ranker, Neural Configuration Scorer (NCS), which ranks the performance of IRs sampled by a transfer-learning-based autotuner, improving the efficiency of the tuning process by reducing tuning overheads and circumventing subpar evaluations.