  Performance modeling is an essential tool in many areas, including
performance characterization/optimization, design space exploration, and
resource allocation problems, to name a few. However, existing performance
modeling approaches have limitations, such as high computational cost for
discrete-event simulators, narrow flexibility of hardware emulators, or
restricted accuracy/generality of analytical/data-driven models. To address
these limitations, this paper proposes PerfVec, a novel deep learning-based
performance modeling framework that learns high-dimensional and
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 and efficient than previous
approaches.
