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Based on responses to this SO question, R was single-threaded in 2016.
How can I have R utilize more of the processing power on my PC?
Is that still true at the end of 2019?
I have a very large project which will require expanding R to multiple threads and high use of CPU, assuming it's possible.
Advice, suggestions and/or workarounds would be most appreciated!
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Introduction. From its inception, R was designed to use only a single thread (processor) at a time. Even today, R works that way unless linked with multi-threaded BLAS/LAPACK libraries. The multi-core machines of today offer parallel processing power.
And Revolution R uses a multi-threaded BLAS. You're free to use whatever BLAS you want, including multi-threaded BLAS, you just have to follow the instructions.
Node JS Platform does not follow Request/Response Multi-Threaded Stateless Model. It follows Single Threaded with Event Loop Model. Node JS Processing model mainly based on Javascript Event based model with Javascript callback mechanism.
Python is NOT a single-threaded language. Python processes typically use a single thread because of the GIL. Despite the GIL, libraries that perform computationally heavy tasks like numpy, scipy and pytorch utilise C-based implementations under the hood, allowing the use of multiple cores.
The are many ways of doing parallel computation in R, you can start from here:
https://www.r-bloggers.com/r-with-parallel-computing-from-user-perspectives/
In addition, some libraries such as data.table are coming with multi thread features.
However, depending on the size and weight of you project, you might want to go to another language or code the critical part of your program in C++, using: http://www.rcpp.org/
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