@inproceedings{9c683eac9c6c4649a539652267f1dec0,
title = "Legion-based scientific data analytics on heterogeneous processors",
abstract = "We present a study of scientific data analytics on heterogeneous architectures using the Legion runtime system. Legion is a new programming model and runtime system targeting distributed heterogeneous architectures. It introduces logical regions as a new abstraction for describing the structures and usages of program data. We describe how to leverage logical regions to express important properties of program data, such as locality and independence, for scientific data analytics that can consist of multiple operations with different data types. Our approach can help users simplify programming on the data partition, data organization, and data movement for distributed-memory heterogeneous architectures, thereby facilitating a simultaneous execution of multiple analytics operations on modern and future supercomputers. We demonstrate the scalability and the usability of our approach by a hybrid data partitioning and distribution scheme for different data types using both CPUs and GPUs on a heterogeneous system.",
keywords = "Legion, heterogeneous processors, scientific data analytics",
author = "Lina Yu and Hongfeng Yu",
year = "2016",
doi = "10.1109/BigData.2016.7840863",
language = "English (US)",
series = "Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2305--2314",
editor = "Ronay Ak and George Karypis and Yinglong Xia and Hu, {Xiaohua Tony} and Yu, {Philip S.} and James Joshi and Lyle Ungar and Ling Liu and Aki-Hiro Sato and Toyotaro Suzumura and Sudarsan Rachuri and Rama Govindaraju and Weijia Xu",
booktitle = "Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016",
note = "4th IEEE International Conference on Big Data, Big Data 2016 ; Conference date: 05-12-2016 Through 08-12-2016",
}