Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications

A Novel Data Race Detection Approach based on Buddy Memory Allocator

Authors
Zhengyang Liu, Hua Zhang
Corresponding Author
Zhengyang Liu
Available Online November 2016.
DOI
https://doi.org/10.2991/aiea-16.2016.88How to use a DOI?
Keywords
Data race, Memory model, Dynamic analysis, Computer security.
Abstract

As a common problem of multi-core parallel programs, the problem of data race has been paid more and more attention in recent years. In this paper, a dynamic detection approach for data race problem detection is proposed. By introducing a new metadata storage based on the buddy memory allocator, the metadata access performance is improved significantly. A specific implementation of the approach based on LLVM compiler infrastructure is made. The experimental results show that the proposed approach can reduce the time cost of dynamic race detection and achieve 2x-5x performance on the Olden benchmark.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
Series
Advances in Computer Science Research
Publication Date
November 2016
ISBN
978-94-6252-270-1
ISSN
2352-538X
DOI
https://doi.org/10.2991/aiea-16.2016.88How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhengyang Liu
AU  - Hua Zhang
PY  - 2016/11
DA  - 2016/11
TI  - A Novel Data Race Detection Approach based on Buddy Memory Allocator
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
PB  - Atlantis Press
SP  - 491
EP  - 495
SN  - 2352-538X
UR  - https://doi.org/10.2991/aiea-16.2016.88
DO  - https://doi.org/10.2991/aiea-16.2016.88
ID  - Liu2016/11
ER  -