One week after the initial release of GS 2.0 we are pleased to introduce the updated version 2.1 that sets clear difference between 2.x and 1.x branches.
Here is a comparison of processing time measured for GS 1.x and 2.x. We used datasets of 100, 1'000, 3'000 and 6'500 rows and measured single CPU-core processing time of two GS versions using the same projects and settings.
The bar chart shows that 2.x branch is 6.3 times faster by a factor of 6.3 in case of 6.5k data rows and at least not slower on a dataset with 100 rows. The performance gap difference grows fast exponentially for larger datasets. For example, 2.x learns from 200'000 rows in merely 37 minutes while 1.x can't finish the same task even within one day.
The old GS 1.x.x spends most processing time on validation of a model structure hypothesis about the model structure while estimation of model coefficients takes only a small portion of time. In GS 2.x we have implemented the recurrent procedure for calculation of testing errors that make the model validation stage very cheap in terms of processing time, even for large datasets. So the latest version is significantly faster by an order of magnitude while validation results exactly matches the results of the old validation procedure.
Along with improvements in the processing speed implemented in GMDH Shell 2.1, we continue to improve embedded data exploration tools, user interface and fix the reported issues. You can read more about the latest changes in the program changelog.