mlpack
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Profiling for parallelization and parallel stochastic optimization methods - Week 10 + 11
The past two weeks were spent on finishing up the implementation of SCD(adding the Greedy descent policy based on GS rule), adding more tests for the new code and making changes to existing functions to make them compatible with the ResolvableFunctionType
requirements. Some documentation outlining the various FunctionType
interfaces was also added to highlight the minor differences and applications of these abstractions.
A minor inconsistency needs to be resolved which would require some simple refactoring to make the layout of the decision variables consistent across various functions in mlpack
so that SCD could work on disjoint parts of the decision variable (required for parallelization).
I am planning to finish up the refactoring and parallelisation part within the next 1-2 days. The next steps would be to benchmark the implemenatation on a few datasets to get an overview of the performance and find any areas of improvement.
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