Learning Independent Program and Architecture Representations for
  Generalizable Performance Modeling
Lingda Li Thomas Flynn Adolfy Hoisie
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.