Doc. Ing. Zdeněk Vašíček, Ph.D.
| ŠIMEK Václav, VAŠÍČEK Zdeněk a SLANÝ Karel. Can the performance of GPGPU really beat CPU in evolutionary design task?. In: 4th Doctoral Workshop on Mathematical and Engineering Methods in Computer Science. Znojmo: Masarykova universita, 2008, s. 264-264. ISBN 978-80-7355-082-0. | | Jazyk publikace: | angličtina |
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| Název publikace: | Can the performance of GPGPU really beat CPU in evolutionary design task? |
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| Název (cs): | Can the performance of GPGPU really beat CPU in evolutionary design task? |
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| Strany: | 264-264 |
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| Sborník: | 4th Doctoral Workshop on Mathematical and Engineering Methods in Computer Science |
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| Konference: | MEMICS'08 -- 4th Doctoral Workshop on Mathematical and Engineering Methods in Computer Science |
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| Místo vydání: | Znojmo, CZ |
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| Rok: | 2008 |
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| ISBN: | 978-80-7355-082-0 |
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| Vydavatel: | Masarykova universita |
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| Klíčová slova |
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GPU, CUDA, CGP, acceleration
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| Anotace |
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With the appearance of modern general purpose graphical processor units
(GPU), a powerful and cheap architecture has entered the field of
scientific computation. This highly parallel architecture, formerly
designed for floating point graphical operation acceleration, is now
being used for the acceleration of
various algorithms.
During
the past few years, various papers dealing with the utilization of GPUs
in general purpose computing have been published. Even evolutionary
algorithms have been accelerated [1, 3], among them genetic programming
and its variants. In order to achieve maximal performance of genome
evaluation, various approaches of candidate solution evaluation have
been proposed. The genome can be evaluated as a program which can be
directly downloaded into the GPU [1] or interpreted by using an
interpreter program running on the GPU [2]. Due to the architectural
limitations, the second method appears to be more promising in
comparison with the previous one.
The GPUs are accessible via
special frameworks providing an interface between GPU and CPU. The
purpose of these frameworks is to provide a comfortable programming
interface for rapid application development at different abstraction
level. Thus, the utilized framework has a serious impact on the
application's performance, since the higher abstraction the lower
performance.
In this work [4] we focus on the acceleration of
CGP, which will be utilized for the evolutionary design of image
filters. The application is written by using the nVidia CUDA framework,
which allows a low-level access to the GPU resources. Several different
ways, how to implement the candidate solution evaluation, with various
performance impacts are discussed. Obtained results are compared with a
CPU-based implementation. The experimental results show, that the
accelerated application does not exhibit the desired performance and
even in some cases is outperformed by a CPU-based application. |
| BibTeX: |
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@INPROCEEDINGS{
author = {V{\'{a}}clav {\v{S}}imek and Zden{\v{e}}k
Va{\v{s}}{\'{i}}{\v{c}}ek and Karel Slan{\'{y}}},
title = {Can the performance of GPGPU really beat CPU in evolutionary
design task?},
pages = {264--264},
booktitle = {4th Doctoral Workshop on Mathematical and Engineering
Methods in Computer Science},
year = {2008},
location = {Znojmo, CZ},
publisher = {Masaryk University},
ISBN = {978-80-7355-082-0},
language = {english},
url = {http://www.fit.vutbr.cz/research/view_pub.php.cs.iso-8859-2?id=8804}
} |
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