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Supercomputers have revolutionized how industries and scientific fieldsprocess large amounts of data. These machines group hundreds or thousands ofcomputing nodes working together to execute time-consuming programs thatrequire a large amount of computational resources. Over the years,supercomputers have expanded to include new and different technologiescharacterizing them as heterogeneous. However, executing a program in aheterogeneous environment requires attention to a specific aspect ofperformance degradation: load imbalance. In this research, we address thechallenges associated with load imbalance when scheduling many homogeneoustasks in a heterogeneous environment. To address this issue, we introduce theconcept of adaptive asynchronous work-stealing. This approach collectsinformation about the nodes and utilizes it to improve work-stealing aspects,such as victim selection and task offloading. Additionally, the proposedapproach eliminates the need for extra threads to communicate information,thereby reducing overhead when implementing a fully asynchronous approach. Ourexperimental results demonstrate a performance improvement of approximately10.1\% compared to other conventional and state-of-the-art implementations.

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