Efficient Parallelization Techniques for Ridge Regression in Large-Scale Brain Encoding with fMRI Data

This paper evaluates different parallelization techniques to reduce the training time of brain encoding with ridge regression on a large-scale fMRI dataset, demonstrating that batch parallelization using Dask provides substantial speed-ups compared to single-threaded and multi-threaded approaches.