@inproceedings{ea55600b4c214868b8f3ac8e5874a10a,
title = "Enabling composite applications through an asynchronous shared memory interface",
abstract = "In this work we address the growing need for mechanisms for intranode application composition. We provide a novel shared memory interface that allows composite applications, two or more coupled applications, to share internal data structures without blocking. This allows independent progress of the applications such that they can proceed in a parallel, overlapped fashion. Composite applications using in-node shared memory can reduce the amount of data to be communicated between nodes, allowing data reduction or analytics to be performed locally and in parallel. To validate our approach we implemented our solution in Linux and used two proxy-applications to demonstrate how applications can be coupled and compare the performance to a traditional solution. We also compared the impact of composite applications to the performance of their unmodified versions. Our solution incurs small overhead in HPC Linux environments and significantly outperforms preexisting approaches.",
keywords = "checkpoint, composite applications, memory management, operating systems, shared memory",
author = "Douglas Otstott and Noah Evans and Latchesar Ionkov and Ming Zhao and Michael Lang",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2nd IEEE International Conference on Big Data, IEEE Big Data 2014 ; Conference date: 27-10-2014 Through 30-10-2014",
year = "2014",
doi = "10.1109/BigData.2014.7004236",
language = "English (US)",
series = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "219--224",
editor = "Jimmy Lin and Jian Pei and Hu, {Xiaohua Tony} and Wo Chang and Raghunath Nambiar and Charu Aggarwal and Nick Cercone and Vasant Honavar and Jun Huan and Bamshad Mobasher and Saumyadipta Pyne",
booktitle = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",
}