A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model

Wendy K.Tam Cho; Yan Liu

doi:10.1145/3605731.3608930

A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model

Wendy K.Tam Cho, Yan Liu

Computational Urban Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We develop a GPU-accelerated machine learning generative adversarial network model that can be used with observational data for the purpose of constructing causal inferences. The theoretical basis of our machine learning model is novel and is conceptualized to be operable and scalable for high performance computing platforms. Our GPU-accelerated code enables large-scale parallelization of the computation within a common and accessible computing environment. This will expand the reach of our model and empower research in new substantive domains while maintaining the underlying theoretical properties.

Original language	English
Title of host publication	52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings
Publisher	Association for Computing Machinery
Pages	167-171
Number of pages	5
ISBN (Electronic)	9798400708435
DOIs	https://doi.org/10.1145/3605731.3608930
State	Published - Aug 7 2023
Event	52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings - Salt Lake City, United States Duration: Aug 7 2023 → Aug 10 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings
Country/Territory	United States
City	Salt Lake City
Period	08/7/23 → 08/10/23

Funding

This work is in part supported by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL), managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725.

Funders	Funder number
U.S. Department of Energy	DE-AC05-00OR22725
Oak Ridge National Laboratory
UT-Battelle

Keywords

Causal Inference
Optimization
Subset Selection

Access to Document

10.1145/3605731.3608930

Cite this

Cho, W. K. T., & Liu, Y. (2023). A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model. In 52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings (pp. 167-171). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3605731.3608930

@inproceedings{e8a647626c504eb6a04f647bd3b7dbec,

title = "A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model",

abstract = "We develop a GPU-accelerated machine learning generative adversarial network model that can be used with observational data for the purpose of constructing causal inferences. The theoretical basis of our machine learning model is novel and is conceptualized to be operable and scalable for high performance computing platforms. Our GPU-accelerated code enables large-scale parallelization of the computation within a common and accessible computing environment. This will expand the reach of our model and empower research in new substantive domains while maintaining the underlying theoretical properties.",

keywords = "Causal Inference, Optimization, Subset Selection",

author = "Cho, {Wendy K.Tam} and Yan Liu",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings ; Conference date: 07-08-2023 Through 10-08-2023",

year = "2023",

month = aug,

day = "7",

doi = "10.1145/3605731.3608930",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "167--171",

booktitle = "52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings",

}

Cho, WKT & Liu, Y 2023, A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model. in 52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 167-171, 52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings, Salt Lake City, United States, 08/7/23. https://doi.org/10.1145/3605731.3608930

A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model. / Cho, Wendy K.Tam; Liu, Yan.
52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings. Association for Computing Machinery, 2023. p. 167-171 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model

AU - Cho, Wendy K.Tam

AU - Liu, Yan

PY - 2023/8/7

Y1 - 2023/8/7

N2 - We develop a GPU-accelerated machine learning generative adversarial network model that can be used with observational data for the purpose of constructing causal inferences. The theoretical basis of our machine learning model is novel and is conceptualized to be operable and scalable for high performance computing platforms. Our GPU-accelerated code enables large-scale parallelization of the computation within a common and accessible computing environment. This will expand the reach of our model and empower research in new substantive domains while maintaining the underlying theoretical properties.

AB - We develop a GPU-accelerated machine learning generative adversarial network model that can be used with observational data for the purpose of constructing causal inferences. The theoretical basis of our machine learning model is novel and is conceptualized to be operable and scalable for high performance computing platforms. Our GPU-accelerated code enables large-scale parallelization of the computation within a common and accessible computing environment. This will expand the reach of our model and empower research in new substantive domains while maintaining the underlying theoretical properties.

KW - Causal Inference

KW - Optimization

KW - Subset Selection

UR - http://www.scopus.com/inward/record.url?scp=85175025340&partnerID=8YFLogxK

U2 - 10.1145/3605731.3608930

DO - 10.1145/3605731.3608930

M3 - Conference contribution

AN - SCOPUS:85175025340

T3 - ACM International Conference Proceeding Series

SP - 167

EP - 171

BT - 52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings

PB - Association for Computing Machinery

T2 - 52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings

Y2 - 7 August 2023 through 10 August 2023

ER -

A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this