# Adaptive Asynchronous Work-Stealing for distributed load-balancing in heterogeneous systems

arXiv: https://arxiv.org/abs/2401.04494 · HTML: https://mlsanity.com/paper/2401.04494/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2024.

## Built on by (most cited first, 1 of 1)

- [Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster](https://mlsanity.com/paper/2509.02440/) (2025)

## Abstract (arXiv)

Supercomputers have revolutionized how industries and scientific fields process large amounts of data. These machines group hundreds or thousands of computing nodes working together to execute time-consuming programs that require a large amount of computational resources. Over the years, supercomputers have expanded to include new and different technologies characterizing them as heterogeneous. However, executing a program in a heterogeneous environment requires attention to a specific aspect of performance degradation: load imbalance. In this research, we address the challenges associated with load imbalance when scheduling many homogeneous tasks in a heterogeneous environment. To address this issue, we introduce the concept of adaptive asynchronous work-stealing. This approach collects information about the nodes and utilizes it to improve work-stealing aspects, such as victim selection and task offloading. Additionally, the proposed approach eliminates the need for extra threads to communicate information, thereby reducing overhead when implementing a fully asynchronous approach. Our experimental results demonstrate a performance improvement of approximately 10.1% compared to other conventional and state-of-the-art implementations.