使用图神经网络学习中间表示用于NUMA和预取器优化
Learning Intermediate Representations using Graph Neural Networks for NUMA and Prefetchers Optimization
摘要：
NUMA和硬件预取器配置的空间很大，可能会显著影响应用程序的性能。以前的研究已经展示了模型如何根据代码的动态属性自动选择配置以实现加速。本文演示了代码的静态中间表示（IR）如何指导NUMA/预取器优化，而无需性能分析的高昂成本。我们提出了一种方法来创建一个全面的数据集，其中包括一组不同的中间表示以及最佳配置。然后，我们应用图神经网络模型，以验证这个数据集。我们表明，我们的静态中间表示为基础的模型实现了80%的性能增益所提供的昂贵的动态性能分析为基础的战略。我们进一步开发了一个混合模型，同时使用静态和动态信息。我们的混合模型实现了与动态模型相同的收益，但成本降低，仅分析30%的程序。
英文摘要：
There is a large space of NUMA and hardware prefetcher configurations that can significantly impact the performance of an application. Previous studies have demonstrated how a model can automatically select configurations based on the dynamic properties of the code to achieve speedups. This paper demonstrates how the static Intermediate Representation (IR) of the code can guide NUMA/prefetcher optimizations without the prohibitive cost of performance profiling. We propose a method to create a comprehensive dataset that includes a diverse set of intermediate representations along with optimum configurations. We then apply a graph neural network model in order to validate this dataset. We show that our static intermediate representation based model achieves 80% of the performance gains provided by expensive dynamic performance profiling based strategies. We further develop a hybrid model that uses both static and dynamic information. Our hybrid model achieves the same gains as the dynamic models but at a reduced cost by only profiling 30% of the programs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
