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Reducing Training Time in a One-Shot Machine Learning-Based Compiler

机译：在基于一站式机器学习的编译器中减少培训时间

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摘要

Iterative compilation of applications has proved a popular and successful approach to achieving high performance. This however, is at the cost of many runs of the application. Machine learning based approaches overcome this at the expense of a large off-line training cost. This paper presents a new approach to dramatically reduce the training time of a machine learning based compiler. This is achieved by focusing on the programs which best characterize the optimization space. By using unsupervised clustering in the program feature space we are able to dramatically reduce the amount of time required to train a compiler. Furthermore, we are able to learn a model which dispenses with iterative search completely allowing integration within the normal program development cycle. We evaluated our clustering approach on the EEMBCv2 benchmark suite and show that we can reduce the number of training runs by more than a factor of 7. This translates into an average 1.14 speedup across the benchmark suite compared to the default highest optimization level.
机译：应用程序的迭代编译已证明是实现高性能的流行且成功的方法。但是，这是以许多应用程序运行为代价的。基于机器学习的方法克服了这一问题，但付出了大量的离线培训成本。本文提出了一种新方法，可以大大减少基于机器学习的编译器的训练时间。通过专注于最能体现优化空间特征的程序来实现这一目标。通过在程序功能空间中使用无监督的群集，我们可以大大减少训练编译器所需的时间。此外，我们能够学习一个完全不需要迭代搜索的模型，从而可以在正常程序开发周期内进行集成。我们在EEMBCv2基准套件上评估了群集方法，并表明我们可以将训练运行次数减少7倍以上。与默认的最高优化级别相比，该基准套件的平均加速速度为1.14。

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