The agent, using a learned graph-DNN policy, produces a vectorization scheme that: * has a 22.6% higher average reduction in cost compared to LLVM compiler (as

The agent, using a learned graph-DNN policy, produces a vectorization scheme that: * has a 22.6% higher average reduction in cost compared to LLVM compiler (as measured with its own cost model); and * achieves a geometric mean runtime speedup of 1.015× on the NAS benchmark suite when compared to LLVM’s SLP vectorizer. In short, the DNN achieves better performance in an NP-Hard problem than human-tuned heuristics and human-designed algorithms.