论文标题
进度报告：深度学习的指导性探索仿射单型环变换
Progress Report: A Deep Learning Guided Exploration of Affine Unimodular Loop Transformations
论文作者
论文摘要
在本文中，我们介绍了一项关于基于深度学习的方法，用于多面体编译器中的自动代码优化。该提出的技术探讨了仿射和非伴随循环转换的组合，以找到最小化给定程序的执行时间的转换序列。这种探索以基于深度学习的成本模型为指导，该模型评估了每个转换序列都会产生的速度。初步结果表明，所提出的技术在最先进的多面体编译器（Pluto）上实现了2.35倍的几何速度。
In this paper, we present a work in progress about a deep learning based approach for automatic code optimization in polyhedral compilers. The proposed technique explores combinations of affine and non-affine loop transformations to find the sequence of transformations that minimizes the execution time of a given program. This exploration is guided by a deep learning based cost model that evaluates the speedup that each sequence of transformations would yield. Preliminary results show that the proposed techniques achieve a 2.35x geometric mean speedup over state of the art polyhedral compilers (Pluto).
