@inproceedings{1eb372b304194ced9c5dda10120521fd,
title = "A hierarchical work-stealing framework for multi-core clusters",
abstract = "Work-stealing has been widely used in task-based parallel programing for dynamic load balancing. The overhead of work-stealing on distributed memory systems is much higher than that on shared memory systems. To minimize the overhead of work-stealing on a multi-core cluster, we propose a hierarchical work-stealing framework, in which work-stealing is performed inside a node before across the node boundary. Two key techniques used in our framework to reduce the inter-node steals are: a) adaptive initial partitioning for different task parallel patterns, b) centralized control for inter-node work-stealing, which improves the efficiency of victim selection and termination detection. We compare our technique to the classical work-stealing scheme and a state-of-the-art work-stealing scheme for multi-core clusters. Our technique outperforms them by 19% and 8% respectively.",
keywords = "multi-core cluster, task scheduling, work-stealing",
author = "Yizhuo Wang and Weixing Ji and Qi Zuo and Feng Shi",
year = "2012",
doi = "10.1109/PDCAT.2012.17",
language = "English",
isbn = "9780769548791",
series = "Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings",
pages = "350--355",
booktitle = "Proceedings - 13th International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2012",
note = "13th International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2012 ; Conference date: 14-12-2012 Through 16-12-2012",
}