- author = "Vinicius Veloso {de Melo} and Alvaro Luiz Fazenda and Leo Francoso Dal Piccol Sotto and Giovanni Iacca",
- title = "A {MIMD} Interpreter for Genetic Programming",
- booktitle = "23rd International Conference, EvoApplications 2020",
- year = "2020",
- month = "15-17 " # apr,
- editor = "Pedro A. Castillo and Juan Luis {Jimenez Laredo} and Francisco {Fernandez de Vega}",
- series = "LNCS",
- volume = "12104",
- publisher = "Springer Verlag",
- address = "Seville, Spain",
- pages = "645--658",
- organisation = "EvoStar, Species",
- keywords = "genetic algorithms, genetic programming, Genetic Programming Interpreter, parallel computing, Vectorization, Multiple Instruction",
- isbn13 = "978-3-030-43721-3",
-
video_url = "
https://www.youtube.com/watch?v=5rh56rZUO5w",
-
DOI = "
doi:10.1007/978-3-030-43722-0_41",
- size = "14 pages",
- abstract = "Most Genetic Programming implementations use an interpreter to execute an individual, in order to obtain its outcome. Usually, such interpreter is the main bottleneck of the algorithm, since a single individual may contain thousands of instructions that must be executed on a dataset made of a large number of samples. Although one can use SIMD (Single Instruction Multiple Data) intrinsics to execute a single instruction on a few samples at the same time, multiple passes on the dataset are necessary to calculate the result. To speed up the process, we propose using MIMD (Multiple Instruction Multiple Data) instruction sets. This way, in a single pass one can execute several instructions on the dataset. We employ AVX2 intrinsics to improve the performance even further, reaching a median peak of 7.5 billion genetic programming operations per second in a single CPU core.",
-
notes = "See also \cite{Oliveira:2020:ERAD-SP}
Federal University of Sao Paulo, Brazil
http://www.evostar.org/2020/ EvoApplications2020 held in conjunction with EuroGP'2020, EvoMusArt2020 and EvoCOP2020",
