Conclusions
This paper applied a straightforward and highly parallelized
mechanism to investigate parallel performance of five catego-
ries of typical algorithms in remote sensing based mapping on
two multi-core computers. According to these experimental
results, conclusions can be drawn that parallel computing on
a multi-core computer for these algorithms not only yields
high speedups but also efficiently leverages the computational
resources of the state-of-the-art hardware platforms. The
achieved performance of each algorithm on two multi-core
computers both with two
CPUs
is improved significantly. The
highest speedups of these algorithms range from 4.8× to 13.6×.
The parallel method adopted in this paper is quite easy to
implement and is seamlessly integrated with the five catego-
ries of typical algorithms. The method has a broad applicabil-
ity. Because these algorithms require reading and writing a
large amount of data and the component dealing with reading
and writing is less efficient than other components in a multi-
core computer, a good parallel strategy is that the computa-
tion and disk
I/O
operations occur simultaneously resulting in
decrease of overall runtime. The adopted parallel method not
only enables multiple blocks to be processed concurrently on
multiple cores but also implements the overlap between the
computational time and the time of reading and writing. Thus,
the parallel method is optimized for a multi-core computer.
Parallel performance on a multi-core computer is deter-
mined by these factors: the amount of required computation
per image size, the amount of required disk
I/O
per image
size, and the performance of the computing platform. The
larger the amount of required computation, the higher the
achieved maximum speedup. Hence, an algorithm requir-
ing a large amount of computation can be scaled. The large
amount of required disk
I/O
per image size is a key factor that
prevents improving the speedup to an even higher level for
some algorithms. In order to decrease the time of reading and
writing, high performance disk should be in high demand in
a computing platform. The number of
CPU
cores in the latest
computers with symmetrical multi-processing (
SMP
) and hy-
per-threading technology can meet the requirements of most
algorithms in remote sensing based mapping to some extent.
The testing results and contributions can guide ordinary
users in remote sensing community to promote processing
speed using desktop computing platforms. In the course of
parallel processing of typical algorithms in remote sensing
based mapping on a multi-core computer, optimal selections
are recommended as follows. The number of processors rang-
es from 8 to 12 for computers with high-frequency
CPU
(s) or
from 12 to 16 for computers with low-frequency
CPU
(s); high
performance disk, e.g., solid-state drive disk is in demand as
high as possible; and either Windows 7 OS or Linux OS is
appropriate.
Acknowledgments
This work was supported by the National Natural Science
Foundation of China under Grant No. 40901229, 863 Program
under Grant No. 2011AA120401, and the Basic Research
Fund of Chinese Academy of Surveying and Mapping under
Grant No. G7771413. Special thanks are given to the data
providers for the provision of the stereo data sets, especially
Euromap for the Cartosat-1 data. The authors thank the
anonymous reviewers for their valuable comments.
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