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We achieve up to 240× speedup compared with the optimized shared memory implementation of Libtensor.
By applying the newest GPU hardware, we achieve up to 180× speedup compared to a similar but serial CPU implementation.
Exploiting a gel agarose micro-beads filtration technique we achieve up to 70% m-SWNTs and over 90% s-SWNTs, independent of the source material.
Using the profiling tool with these optimizations on Nvidia's CUDA framework, we achieve up to 60× speedup over a single-threaded CPU implementation of the model.
The occlusion detection-based fusion allows a rate reduction of 6.6%, while using adaptive validation, we achieve up to more than 20% rate reduction and obtain an average of more than 9%.
We achieve up to 10,000-fold range in constitutive gene expression and 100-fold regulation of gene expression with inducible promoters and use these parts to record DNA-encoded memory in the genome.
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Under the conditions described here, we achieved up to 56% targeted integration efficiency with up to a six-nucleotide insertion in HEK293 cells.
We achieved up to 34× better throughput than the serial CPU implementation of LZSS algorithm and up to 2.21× better than the parallelized version.
We achieved up to 12% Mg incorporation (with 12% S incorporation) as well as reproducible growth of lattice matched Zn0.94Mg0.06S0.11Se0.89 layers.
For example, we achieved up to 66.3%, 83.3%, 91.2%, and 94.2% savings in EDP when adjusting all the parameters properly in applications FFT, MG, LU, and CG, respectively.
Using iso-molded graphite rod as the anode, we achieved up to 86% of exfoliation with the majority of the products as graphene flakes in addition to smaller quantities of carbonaceous particles and rolled sheets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com