This paper proposes PerfVec, a novel deep learning-based performance modeling framework that learns high-dimensional, independent/orthogonal program and microarchitecture representations. Once learned, a program representation can be used to predict its performance on any microarchitecture, and likewise, a microarchitecture representation can be applied in the performance prediction of any program. Additionally, PerfVec yields a foundation model that captures the performance essence of instructions, which can be directly used by developers in numerous performance modeling related tasks without incurring its training cost. The evaluation demonstrates that PerfVec is more general, efficient, and accurate than previous approaches.

翻译：本文提出PerfVec，一种基于深度学习的新型性能建模框架，能够学习高维、独立/正交的程序与微架构表示。学习完成后，任意程序表示可用于预测其在任何微架构上的性能，同样，微架构表示可应用于任意程序的性能预测。此外，PerfVec生成的基础模型能够捕获指令的性能本质，开发者可直接将其用于众多性能建模相关任务，而无需承担模型训练成本。评估表明，PerfVec比先前方法更具通用性、高效性和准确性。