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

by Wendy Cho, Yan Liu
Publication Type
Conference Paper
Book Title
ICPP Workshops '23: Proceedings of the 52nd International Conference on Parallel Processing Workshops
Publication Date
Page Numbers
167 to 171
Publisher Location
New York, New York, United States of America
Conference Name
The 13th International Workshop on Parallel and Distributed Algorithms for Decision Sciences (PDADS 2023)
Conference Location
Salt Lake City, Utah, United States of America
Conference Sponsor
ACM
Conference Date
-

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.