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Fig. 2 Execution times for various input sizes B1 Fig. 3 Execution times for various input sizes B2 Fig. 4 Speedups for various input sizes.
No other properties are defined, such as performance variation due to branching and input sizes.
Fig. 9 Total run-time (sec) vs. different input sizes (GB) for different memory sizes (GB).
Fig. 10 Total run-time (sec) vs. different Memory and input sizes (GB).
Further, the design fits on one FPGA device for fairly large input sizes.
The total runtime for different input sizes and different configurations is constant about 362 seconds as shown in Fig. 2.
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a Variable input size.
Figure 10 Input size scaling of WordCount workload.
Let (N) be the input size of the problem.
In the machine learning domain, window size refers to the input size of the prediction algorithm.
Figure 4 Wordcount execution time with respect to requested input size.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com