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After analyzing convergence and communication requirement of the algorithm, a set of numerical simulations is provided to confirm the effectiveness of the proposed approach.
The numerical results indicated that the results obtained by the presented algorithm have a good agreement with results produced by MCRT, meanwhile CPU time requirement of the algorithm is about a few seconds, much shorter than that of MCRT.
The fundamental Kirchhoff depth migration algorithm exhibits inherent parallelism however, when it comes to 3D data migration, as the data size increases the resource requirement of the algorithm also increases.
The overall space requirement of the algorithm is proportional to n + kmax + q and the running time is proportional to n log2 q.
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This underscores the requirement of the algorithms specifically designed for analysis of scanning and electron microscopy data which must use robust workload balancing tools that are resilient to errors in algorithmic execution, as well as data transfer.
Thus, to achieve efficient implementation of an algorithm a matching of the computing capabilities of processors with the computing requirements of the algorithm is required.
Profiling shows that matrix operations consume the largest portion of the computation requirements of the algorithm.
The CPU time requirements of the algorithm of the present paper are n43, and can be further reduced, making it considerably more practical for large scale problems.
Using the H.264/AVC Intra coding tool, we discuss the requirements of the algorithm and the impact on decoded image quality when these requirements are not met.
The computational requirements of the algorithm are reduced by restricting the transformation to the template edge points and using invariant information during the comparison process.
Nevertheless, the ideal requirements of the algorithm are for both fast convergence and low misadjustment.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com