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Smart, adaptive mapping of parallelism in the presence of external workload

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Smart, adaptive mapping of parallelism in the presence of external workload. / Emani, Murali Krishna; Wang, Zheng; O'Boyle, Michael.
2013 International Symposium on Code Generation and Optimization (CGO). IEEE, 2013. p. 1-10.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Emani, MK, Wang, Z & O'Boyle, M 2013, Smart, adaptive mapping of parallelism in the presence of external workload. in 2013 International Symposium on Code Generation and Optimization (CGO). IEEE, pp. 1-10. https://doi.org/10.1109/CGO.2013.6495010

APA

Emani, M. K., Wang, Z., & O'Boyle, M. (2013). Smart, adaptive mapping of parallelism in the presence of external workload. In 2013 International Symposium on Code Generation and Optimization (CGO) (pp. 1-10). IEEE. https://doi.org/10.1109/CGO.2013.6495010

Vancouver

Emani MK, Wang Z, O'Boyle M. Smart, adaptive mapping of parallelism in the presence of external workload. In 2013 International Symposium on Code Generation and Optimization (CGO). IEEE. 2013. p. 1-10 doi: 10.1109/CGO.2013.6495010

Author

Emani, Murali Krishna ; Wang, Zheng ; O'Boyle, Michael. / Smart, adaptive mapping of parallelism in the presence of external workload. 2013 International Symposium on Code Generation and Optimization (CGO). IEEE, 2013. pp. 1-10

Bibtex

@inproceedings{666426949e7c4f2998afdbb2c2829a7a,
title = "Smart, adaptive mapping of parallelism in the presence of external workload",
abstract = "Given the wide scale adoption of multi-cores in main stream computing, parallel programs rarely execute in isolation and have to share the platform with other applications that compete for resources. If the external workload is not considered when mapping a program, it leads to a significant drop in performance. This paper describes an automatic approach that combines compile-time knowledge of the program with dynamic runtime workload information to determine the best adaptive mapping of programs to available resources. This approach delivers increased performance for the target application without penalizing the existing workload. This approach is evaluated on NAS and SpecOMP parallel bench-mark programs across a wide range of workload scenarios. On average, our approach achieves performance gain of 1.5× over a state-of-art scheme on a 12 core machine.",
author = "Emani, {Murali Krishna} and Zheng Wang and Michael O'Boyle",
year = "2013",
month = feb,
doi = "10.1109/CGO.2013.6495010",
language = "English",
isbn = "9781467355247",
pages = "1--10",
booktitle = "2013 International Symposium on Code Generation and Optimization (CGO)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Smart, adaptive mapping of parallelism in the presence of external workload

AU - Emani, Murali Krishna

AU - Wang, Zheng

AU - O'Boyle, Michael

PY - 2013/2

Y1 - 2013/2

N2 - Given the wide scale adoption of multi-cores in main stream computing, parallel programs rarely execute in isolation and have to share the platform with other applications that compete for resources. If the external workload is not considered when mapping a program, it leads to a significant drop in performance. This paper describes an automatic approach that combines compile-time knowledge of the program with dynamic runtime workload information to determine the best adaptive mapping of programs to available resources. This approach delivers increased performance for the target application without penalizing the existing workload. This approach is evaluated on NAS and SpecOMP parallel bench-mark programs across a wide range of workload scenarios. On average, our approach achieves performance gain of 1.5× over a state-of-art scheme on a 12 core machine.

AB - Given the wide scale adoption of multi-cores in main stream computing, parallel programs rarely execute in isolation and have to share the platform with other applications that compete for resources. If the external workload is not considered when mapping a program, it leads to a significant drop in performance. This paper describes an automatic approach that combines compile-time knowledge of the program with dynamic runtime workload information to determine the best adaptive mapping of programs to available resources. This approach delivers increased performance for the target application without penalizing the existing workload. This approach is evaluated on NAS and SpecOMP parallel bench-mark programs across a wide range of workload scenarios. On average, our approach achieves performance gain of 1.5× over a state-of-art scheme on a 12 core machine.

U2 - 10.1109/CGO.2013.6495010

DO - 10.1109/CGO.2013.6495010

M3 - Conference contribution/Paper

SN - 9781467355247

SP - 1

EP - 10

BT - 2013 International Symposium on Code Generation and Optimization (CGO)

PB - IEEE

ER -