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Learning to Make Compiler Optimizations More Effective
Applying the transformations that our model deems most favorable prior to compilation yields an average speedup of 1. 14x.
Static Neural Compiler Optimization via Deep Reinforcement Learning
Provided with sub-sequences constituting LLVM's O3 sequence, our agent learns to outperform the O3 sequence on the set of source codes used for training and achieves competitive performance on the validation set, gaining up to 1. 32x speedup on previously-unseen programs.
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