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Thus, all methods of moments allow a significant decrease of memory usage in simulations.
if specified, estimated memory usage (in Megabytes) is X + NY, where N is the number of CPUs used.
The experiments show improved results in terms of accuracy, speed and memory usage in comparison with other existing approaches.
It is shown that the regularization method combined with Kirchhoff rod theory contributes substantially to the reduction of computation time and efficient memory usage in comparison to the gIB method.
Efficiency is in less computer memory usage, in fast computation of the equations, in singularity-free representation of task space, in robustness to numerical errors, and in compactness of the representations.
We use large real-life problems (up to several million equations and several hundred million nonzeros) to show that we can significantly reduce the core memory usage in parallel (on up to 128 processors), with a time performance comparable to that of a parallel in-core solver.
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However, because it is currently the best-performing (both in terms of memory usage and in terms of wall-time seconds8) library for the computation of PH with the VR complex, we include it in our study.
Subfigure (a) shows the Memory Usage, and in subfigure (b) shows the CPU Usage.
The memory usage increases in proportion to the total number of the metadata.
This figure indicates that the memory usage increases in proportion to the total number of the metadata.
The memory usage presented in Table 10 is the maximum memory.
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