Working with OpenCL to speed up a genetic programming financial forecasting algorithm: initial results

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Brookhouse:2014:GECCOcomp,

• author = "James Brookhouse and Fernando E. B. Otero and Michael Kampouridis",
• title = "Working with {OpenCL} to speed up a genetic programming financial forecasting algorithm: initial results",
• booktitle = "GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft)",
• year = "2014",
• editor = "Stefan Wagner and Michael Affenzeller",
• isbn13 = "978-1-4503-2881-4",
• keywords = "genetic algorithms, genetic programming, GPU",
• pages = "1117--1124",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• URL = "https://kar.kent.ac.uk/42144/",
• URL = "http://doi.acm.org/10.1145/2598394.2605689",
• DOI = "doi:10.1145/2598394.2605689",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device.",
• notes = "Also known as \cite{2605689} Distributed at GECCO-2014.",

}

Genetic Programming entries for James Brookhouse Fernando Esteban Barril Otero Michael Kampouridis

Citations