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Working with OpenCL to Speed Up a Genetic Programming Financial Forecasting Algorithm: Initial Results

Brookhouse, James and Otero, Fernando E.B. and Kampouridis, Michael (2014) Working with OpenCL to Speed Up a Genetic Programming Financial Forecasting Algorithm: Initial Results. In: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation. GECCO Genetic and Evolutionary Computation Conference . ACM, New York, USA, pp. 1117-1124. ISBN 978-1-4503-2881-4. (doi:10.1145/2598394.2605689)

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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.

Item Type: Book section
DOI/Identification number: 10.1145/2598394.2605689
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 07 Aug 2014 19:48 UTC
Last Modified: 23 Sep 2019 11:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/42144 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
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