# Extracting Clean Performance Models from Tainted Programs

arXiv: https://arxiv.org/abs/2012.15592 · HTML: https://mlsanity.com/paper/2012.15592/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Application Performance Modeling via Tensor Completion](https://mlsanity.com/paper/2210.10184/) (2022)
- [An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks](https://mlsanity.com/paper/2104.09075/) (2021)

## Abstract (arXiv)

Performance models are well-known instruments to understand the scaling behavior of parallel applications. They express how performance changes as key execution parameters, such as the number of processes or the size of the input problem, vary. Besides reasoning about program behavior, such models can also be automatically derived from performance data. This is called empirical performance modeling. While this sounds simple at the first glance, this approach faces several serious interrelated challenges, including expensive performance measurements, inaccuracies inflicted by noisy benchmark data, and overall complex experiment design, starting with the selection of the right parameters. The more parameters one considers, the more experiments are needed and the stronger the impact of noise. In this paper, we show how taint analysis, a technique borrowed from the domain of computer security, can substantially improve the modeling process, lowering its cost, improving model quality, and help validate performance models and experimental setups.