DLBCM 2021

A Deep Learning Based Cost Model for Automatic Code Optimization

Posted by Treaseven on February 26, 2025

Motivation

  • 选择代码转换的正确顺序的问题可以被建模为一个搜索问题,分为三步:1.定义搜索空间 2.检查每种候选的有效性 3.评价每种有效候选并选择一个能最少执行时间,引出问题检查有效性都直接在硬件上测量需要大量时间,为了解决这个问题,提出利用代价模型来预测加速
  • 设计代价模型的挑战:代码转换的复杂交互会使问题变得很复杂,提出用深度学习,但是只考虑组合基本块的输出没有考虑完整程序,或者需要繁重的特征工程

Data Generation

  • random code generation
  • dataset construction

Program characterization and model architectures

program charcterization

detailed list of features composing the computation vector

hardware characterization 只针对CPU这一种硬件

model architecture post-dlbcm-cost-model.png

level of feature extraction

Search space exploration

Reference

A Deep Learning Based Cost Model for Automatic Code Optimization