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To verify our work, we compared our implementation with traditional CPU implementation for same algorithm.
Finally, we validate our implementation with an industrial test case on 8 heterogeneous nodes, each composed of 12 CPUs and 3 GPUs.
This work also presents computational tests using benchmark instances to compare our implementation with the most competitive methods from the literature.
More specifically, our implementation with 3969-word multiple-bank memory, 63 word-size comparators, 64 word-size multiplexers, and 63 word-size registers only requires some 8k clock cycles to sort an arbitrary 3969-word long array of random data, which arrive at the sorter and also depart it one item at a time.
During an internship at Neopost Inc., of 14 weeks, we developed the server component of a software bus, called the XBus, using formal methods during the design, validation and testing phase: we modeled our design of the XBus in the process algebra mCRL2, validated the design using the mCRL2-simulator, and fully automatically tested our implementation with the model-based test tool JTorX.
Our implementation with a software radio platform (explained in Section 6) is shown to achieve very low physical error rate.
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We compare our implementations with a popular message passing interface (MPI) parallel implementation.
We attempted to compare our implementations with commercial and open-source database systems, but unfortunately, most systems do not support sampling.
One of the fastest implementations, the Kakadu implementationb, even achieves 39.88 fps compared to 1.55 fps with our implementation (compression with the pyramidal decomposition, 2 bpp, 5 level wavelet decomposition and no quality layers), and thus is about 25 times faster.
In addition, our implementation starts with scheduling of result moves with an attempt to find all bypass destinations and create direct bypass moves - early bypassing.
Our implementation works with both sun grid engine (SGE being discontinued) and SLURM (simple linux utility for resource management [34]).
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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