TY - JOUR
T1 - GSGP-CUDA — A CUDA framework for Geometric Semantic Genetic Programming
AU - Trujillo, Leonardo
AU - Muñoz Contreras, Jose Manuel
AU - Hernandez, Daniel E.
AU - Castelli, Mauro
AU - Tapia, Juan J.
N1 - Publisher Copyright:
© 2022
PY - 2022/6
Y1 - 2022/6
N2 - Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently than operating at the syntax level like most GP systems. Efficient implementations of GSGP in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP and the most efficient, which exploits the intrinsic parallelism of GSGP using GPUs. Results show speedups greater than 1,000× relative to the state-of-the-art sequential implementation, during the model training process. Additionally, our implementation allows the user to seamlessly make inferences over new data through the best evolved model, opening the possibility of using GSGP on Big Data problems.
AB - Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently than operating at the syntax level like most GP systems. Efficient implementations of GSGP in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP and the most efficient, which exploits the intrinsic parallelism of GSGP using GPUs. Results show speedups greater than 1,000× relative to the state-of-the-art sequential implementation, during the model training process. Additionally, our implementation allows the user to seamlessly make inferences over new data through the best evolved model, opening the possibility of using GSGP on Big Data problems.
KW - CUDA
KW - GPU
KW - Genetic Programming
KW - Geometric Semantic Genetic Programming
UR - http://www.scopus.com/inward/record.url?scp=85129991202&partnerID=8YFLogxK
U2 - 10.1016/j.softx.2022.101085
DO - 10.1016/j.softx.2022.101085
M3 - Artículo
AN - SCOPUS:85129991202
SN - 2352-7110
VL - 18
JO - SoftwareX
JF - SoftwareX
M1 - 101085
ER -