# Multitask and Transfer Learning for Autotuning Exascale Applications

arXiv: https://arxiv.org/abs/1908.05792 · HTML: https://mlsanity.com/paper/1908.05792/

## Key facts

- Cited by 7 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 15 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [BaCO: A Fast and Portable Bayesian Compiler Optimization Framework](https://mlsanity.com/paper/2212.11142/) (2022)
- [Non-smooth Bayesian Optimization in Tuning Problems](https://mlsanity.com/paper/2109.07563/) (2021)
- [Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization](https://mlsanity.com/paper/2206.01409/) (2022)
- [HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization](https://mlsanity.com/paper/2210.00798/) (2022)
- [Transfer-Learning-Based Autotuning Using Gaussian Copula](https://mlsanity.com/paper/2401.04669/) (2024)
- [HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach](https://mlsanity.com/paper/2501.01057/) (2025)
- [Topological Learning for Motion Data via Mixed Coordinates](https://mlsanity.com/paper/2310.19960/) (2023)

## Abstract (arXiv)

Multitask learning and transfer learning have proven to be useful in the field of machine learning when additional knowledge is available to help a prediction task. We aim at deriving methods following these paradigms for use in autotuning, where the goal is to find the optimal performance parameters of an application treated as a black-box function. We show comparative results with state-of-the-art autotuning techniques. For instance, we observe an average 1.5x improvement of the application runtime compared to the OpenTuner and HpBandSter autotuners. We explain how our approaches can be more suitable than some state-of-the-art autotuners for the tuning of any application in general and of expensive exascale applications in particular.