IEICE TRANSACTIONS on Information
DRDet: Efficiently Making Data Races Deterministic
Summary :
Strongly deterministic multithreading provides determinism for multithreaded programs even in the presence of data races. A common way to guarantee determinism for data races is to isolate threads by buffering shared memory accesses. Unfortunately, buffering all shared accesses is prohibitively costly. We propose an approach called DRDet to efficiently make data races deterministic. DRDet leverages the insight that, instead of buffering all shared memory accesses, it is sufficient to only buffer memory accesses involving data races. DRDet uses a sound data-race detector to detect all potential data races. These potential data races, along with all accesses which may access the same set of memory objects, are flagged as data-race-involved accesses. Unsurprisingly, the imprecision of static analyses makes a large fraction of shared accesses to be data-race-involved. DRDet employs two optimizations which aim at reducing the number of accesses to be sent to query alias analysis. We implement DRDet on CoreDet, a state-of-the-art deterministic multithreading system. Our empirical evaluation shows that DRDet reduces the overhead of CoreDet by an average of 1.6X, without weakening determinism and scalability.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.10 pp.2676-2684
- Publication Date
- 2014/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7067
- Type of Manuscript
- PAPER
- Category
- Software Engineering
Authors
Chen CHEN
National University of Defense Technology
Kai LU
National University of Defense Technology
Xiaoping WANG
National University of Defense Technology
Xu ZHOU
National University of Defense Technology
Zhendong WU
National University of Defense Technology
Keyword
Latest Issue
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Chen CHEN, Kai LU, Xiaoping WANG, Xu ZHOU, Zhendong WU, "DRDet: Efficiently Making Data Races Deterministic" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 10, pp. 2676-2684, October 2014, doi: 10.1587/transinf.2014EDP7067.
Abstract: Strongly deterministic multithreading provides determinism for multithreaded programs even in the presence of data races. A common way to guarantee determinism for data races is to isolate threads by buffering shared memory accesses. Unfortunately, buffering all shared accesses is prohibitively costly. We propose an approach called DRDet to efficiently make data races deterministic. DRDet leverages the insight that, instead of buffering all shared memory accesses, it is sufficient to only buffer memory accesses involving data races. DRDet uses a sound data-race detector to detect all potential data races. These potential data races, along with all accesses which may access the same set of memory objects, are flagged as data-race-involved accesses. Unsurprisingly, the imprecision of static analyses makes a large fraction of shared accesses to be data-race-involved. DRDet employs two optimizations which aim at reducing the number of accesses to be sent to query alias analysis. We implement DRDet on CoreDet, a state-of-the-art deterministic multithreading system. Our empirical evaluation shows that DRDet reduces the overhead of CoreDet by an average of 1.6X, without weakening determinism and scalability.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7067/_p
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@ARTICLE{e97-d_10_2676,
author={Chen CHEN, Kai LU, Xiaoping WANG, Xu ZHOU, Zhendong WU, },
journal={IEICE TRANSACTIONS on Information},
title={DRDet: Efficiently Making Data Races Deterministic},
year={2014},
volume={E97-D},
number={10},
pages={2676-2684},
abstract={Strongly deterministic multithreading provides determinism for multithreaded programs even in the presence of data races. A common way to guarantee determinism for data races is to isolate threads by buffering shared memory accesses. Unfortunately, buffering all shared accesses is prohibitively costly. We propose an approach called DRDet to efficiently make data races deterministic. DRDet leverages the insight that, instead of buffering all shared memory accesses, it is sufficient to only buffer memory accesses involving data races. DRDet uses a sound data-race detector to detect all potential data races. These potential data races, along with all accesses which may access the same set of memory objects, are flagged as data-race-involved accesses. Unsurprisingly, the imprecision of static analyses makes a large fraction of shared accesses to be data-race-involved. DRDet employs two optimizations which aim at reducing the number of accesses to be sent to query alias analysis. We implement DRDet on CoreDet, a state-of-the-art deterministic multithreading system. Our empirical evaluation shows that DRDet reduces the overhead of CoreDet by an average of 1.6X, without weakening determinism and scalability.},
keywords={},
doi={10.1587/transinf.2014EDP7067},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - DRDet: Efficiently Making Data Races Deterministic
T2 - IEICE TRANSACTIONS on Information
SP - 2676
EP - 2684
AU - Chen CHEN
AU - Kai LU
AU - Xiaoping WANG
AU - Xu ZHOU
AU - Zhendong WU
PY - 2014
DO - 10.1587/transinf.2014EDP7067
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2014
AB - Strongly deterministic multithreading provides determinism for multithreaded programs even in the presence of data races. A common way to guarantee determinism for data races is to isolate threads by buffering shared memory accesses. Unfortunately, buffering all shared accesses is prohibitively costly. We propose an approach called DRDet to efficiently make data races deterministic. DRDet leverages the insight that, instead of buffering all shared memory accesses, it is sufficient to only buffer memory accesses involving data races. DRDet uses a sound data-race detector to detect all potential data races. These potential data races, along with all accesses which may access the same set of memory objects, are flagged as data-race-involved accesses. Unsurprisingly, the imprecision of static analyses makes a large fraction of shared accesses to be data-race-involved. DRDet employs two optimizations which aim at reducing the number of accesses to be sent to query alias analysis. We implement DRDet on CoreDet, a state-of-the-art deterministic multithreading system. Our empirical evaluation shows that DRDet reduces the overhead of CoreDet by an average of 1.6X, without weakening determinism and scalability.
ER -
English
