Project Name	Stars	Most Recent Commit	Open Issues	License	Language
Fractional_differencing_gpu	33	5 years ago	1	mit	Jupyter Notebook
Rapid large-scale fractional differencing with RAPIDS to minimize memory loss while making a time series stationary. 6x-400x speed up over CPU implementation.
Df Parallel	5	9 months ago		bsd-3-clause	Jupyter Notebook
Comparison of Dataframe libraries for parallel processing of large tabular files on CPU and GPU.

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Fractional_differencing_gpu ⭐ 33

Rapid large-scale fractional differencing with RAPIDS to minimize memory loss while making a time series stationary. 6x-400x speed up over CPU implementation.

most recent commit 5 years ago

Df Parallel ⭐ 5

Comparison of Dataframe libraries for parallel processing of large tabular files on CPU and GPU.

most recent commit 9 months ago

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Df Parallel vs Fractional_differencing_gpu

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