Narges Takhtkeshha, In my opinion, Dask is better for speeding up algorithms, parallelising computing, parallelising Pandas and NumPy and integrating with libraries like scikit-learn. Dask parallel collections like Dataframes, Bags and Arrays, enables it to store data that is larger than RAM. Dask can enable efficient parallel computations on single machines by leveraging their multi-core CPUs and streaming data efficiently from disk. It can run on a distributed cluster.
It depends on your task. Numba can compile for the GPU while Joblib is good at multi-threaded processing. Basic usage of both packages is quite user friendly, the examples on the Joblib are a great place to start.