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The benchmark tests (evaluation of more than 108 integrals using the STO-3G basis set) of our GPU (NVIDIA GTX 780) implementation showed up to 750-fold speedup compared to a single core of Athlon II X4 635 CPU.
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Several system errors showed up during the implementation phase and required repair.
(They never showed up).
Campers showed up gradually.
I showed up early.
The results indicate that for large problems a single GPU is up to 45 times faster than a CPU for this application, and the parallel implementation shows linear speed up on up to 4 GPUs.
The FPGA and ASIC implementations of PLA show up to 18% and 12% improvement in average delay, respectively, when compared to existing RRAs in literature.
The geographic prioritization seems appropriate, for the fact that most of regions picked up for programme implementation showed having lower sanitation coverage than the national average.
The proposed approach is highly tailored to the considered class of problem instances and, in our MATLAB implementation, shows a speed-up factor of up to 8.55 compared with a commercial ILP solver.
If one of the effects presented in Table 9 is estimated to show up by 'Reconstruction' implementation, then, at the final stage, an EAS considering the two alternatives of 'Reconstruction' and 'New Alignment' takes place.
Our experimental results on real world embedded applications show up to 32.15% lower area and up to 5.1% higher speed compared to traditional implementation using VMT.
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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