# Bridging the Architecture Gap: Abstracting Performance-Relevant Properties of Modern Server Processors

## TL;DR

This paper presents a universal modeling approach for predicting the performance of server processors by differentiating application and machine models, achieving high accuracy across diverse architectures.

## Contribution

It introduces a generic method for deriving machine models that accurately predict runtime on various modern server CPUs, with minimal feature sets.

## Key findings

- Average prediction error of 5%
- Maximum error of 10%
- Effective across multiple architectures

## Abstract

We describe a universal modeling approach for predicting single- and multicore runtime of steady-state loops on server processors. To this end we strictly differentiate between application and machine models: An application model comprises the loop code, problem sizes, and other runtime parameters, while a machine model is an abstraction of all performance-relevant properties of a CPU. We introduce a generic method for determining machine models and present results for relevant server-processor architectures by Intel, AMD, IBM, and Marvell/Cavium. Considering this wide range of architectures, the set of features required for adequate performance modeling is surprisingly small. To validate our approach, we compare performance predictions to empirical data for an OpenMP-parallel preconditioned CG algorithm, which includes compute- and memory-bound kernels. Both single- and multicore analysis shows that the model exhibits average and maximum relative errors of 5% and 10%. Deviations from the model and insights gained are discussed in detail.