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For (N = 1mbox024), we obtain a speed up of a factor 30 using 64 cores.
Its FPGA implementation offers a speed up of 18 over CPU.
(a) Speed up of the proposed two-way recursive Gaussian filter against one-way recursive Gaussian filter.
The strong scaling analysis shows good scalability with a speed up of 185 on 256 processors (20,000 particles/processor) for hybrid versus a speed up of 138 for a standalone MPI.
Experimental tests confirm the efficacy of these fast algorithms, demonstrating a speed up of approximately two to three orders of magnitude over direct decoding.
For handwritten character recognition task the complete system shows a speed up of 260 times over a sequential algorithm running on a Sun SPARC20 workstation.
Similar(50)
They obtain a speed-up of between 6x and 56x, depending on the encoding conditions.
We obtained a speed-up of 2.3 ×, that satisfies the requirement.
A speed-up of 180× faster is obtained compared to the sequential CPU version.
The proposed algorithm obtains a speed-up of over 92x (TR of 98.91%) on average, which means a speed-up of over 47x (TR of 97.89%) for the complete H.264/AVC encoder, when compared with full search.
The proposed algorithm obtains a speed-up of over 4x (TR of 76.11%) on average, which means a speed-up of over 2.7x (TR of 63.22%) for the complete H.264/AVC encoder, when compared with UMHexagonS search.
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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