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Our implementation is fast and displays good performance in retrospective ligand-based virtual screening experiments.
The Java implementation is fast and extensible, and includes a graphical user interface.
Moreover, we demonstrated in this article that our implementation is fast enough for realistic applications.
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And DocStoc claims its implementation is faster.
Our MapReduce implementation is faster than the MPI implementation by an order of magnitude.
In this case, the GPU implementation is faster even on smaller images.
The test results indicated that the GPU-based implementation is faster than that of CPU-based one.
However, for even more particles, the degree of parallelization becomes important and hence GPGPU implementation is faster than one-core and quad-core implementations.
We conclude with a comparison of our best performing code to two other implementations of the polyphase filter, showing that our implementation is faster in nearly all cases.
We provide performance tests for both linear and nonlinear PDEs which show that our cell-based implementation is faster than sparse matrix vector products for polynomial order two and higher on hexahedral elements and yields ten times higher Gflops rates.
The approximations outlined above do not harm the robustness as shown by a comparison between the rows 6 and 7 of Tables 2 and 3, but this implementation is faster because GD imputation is more efficient than MAP and the computation of the posterior probabilities are simplified.
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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