For python the packages 'xarray` and `dask` can parallelize lots of data crunching tasks either on your computer or on a hpc.

A good starting point is this (long) video:

https://www.youtube.com/watch?v=mecN-Ph_-78 (3:21:30 for dask)

or the `dask` Tutorial on git: https://github.com/dask/dask-tutorial

Dear Asif Adil

Follow these links:

https://www.machinelearningplus.com/python/parallel-processing-python/#:~:text=In%20python%2C%20the%20multiprocessing%20module,in%20completely%20separate%20memory%20locations.

https://wiki.python.org/moin/ParallelProcessing

https://stackoverflow.com/questions/20548628/how-to-do-parallel-programming-in-python

https://stackoverflow.com/questions/9786102/how-do-i-parallelize-a-simple-python-loop

https://stackoverflow.com/questions/7207309/how-to-run-functions-in-parallel

Dear Asif,

this is a good tutorial for parallel computing in R:

https://nceas.github.io/oss-lessons/parallel-computing-in-r/parallel-computing-in-r.html

HTH,

Ákos

Thank you Mr. Ali Golbaf and Mr. Ákos Bede-Fazekas

This thread is open for all the good suggestions so that all the beginners here would get benifitted from the expert recommendations. Thank you.

Thank you Mr. M. Alvioli for sharing your experience with both the languages.

Maybe it is a little bit out of scope, but I found this info some time ago, and it was quite useful when optimising time-consuming processes in R. I hope it will help you too.

https://towardsdatascience.com/getting-started-with-parallel-programming-in-r-d5f801d43745

https://nceas.github.io/oss-lessons/parallel-computing-in-r/parallel-computing-in-r.html

Best regards,

Ivan

It's a good source https://dbader.org/blog/python-parallel-computing-in-60-seconds

For python the packages 'xarray` and `dask` can parallelize lots of data crunching tasks either on your computer or on a hpc.

A good starting point is this (long) video:

https://www.youtube.com/watch?v=mecN-Ph_-78 (3:21:30 for dask)

or the `dask` Tutorial on git: https://github.com/dask/dask-tutorial

Hi, Follow this book

Hi, Asif. I think the aim of parallel computing is to improve the computional efficiency, this is important in numerical simulation or massive cluster computing. Different programming languages more or less have their own advantages in supportting parallel. What I want to express is that the selection of Python or R or other open source languages is not important except your scientific&practical aims. In our experiences, at nowadays, the hardware is more important in parallel computing, so the language should be more compatiable to such hard ware's capability.