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Improvements in grid resolution can be achieved without significant increases in execution time or computer memory.
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The GPU version of random forest guarantees an increase in execution speed by 2 20 times.
We can see a significant increase in execution rate with the increase in problem size (N values).
As can be seen, there is a significant increase in execution time as the number of queries increases.
Ignoring communication, it is expected that doubling N results roughly in a 16 times) increase in execution time.
This shows an optimized threshold to operate Memcached within below 32 threads to avoid exponential increase in execution time.
In summary, a 2x increase in input size will result in about 1.9x increase in execution time regardless of memory size.
This performance evaluation highlights that it is not always valid to assume that porting algorithms to Big Data frameworks will result in an increase in execution speed.
We conducted performance tests for our MATLAB implementation using both models, and there was less than 1% increase in execution time for demapping of 100 frames using the bivariate Gaussian model compared to the circularly symmetric Gaussian model.
However, as the saving in cost for using Linux based instances was at least 20% this increase in execution time was considered acceptable as it was felt that the increase in the number of instances which could be run offset the increased execution time.
In the I/O poor setting, VN mode with grid map reuse resulted in only a small increase in execution speed over VN OMP = 1, gm = reload), while SMP mode (OMP = 4, gm = reuse) execution time was decreased by 33% (Figure 2b).
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