I am going to attend MSc Data Science in September, so I consider upgrading my system to be more efficient with my projects.I used RStudio for my Macroeconomics Undergraduate Thesis and I had a couple of loops which needed almost 30 minutes to occur. My system specifications are the following: - CPU: i7 4970k @ 4.0 GHz - RAM: 8GB DDR3 - Hard Drive: SSD M.2 950 PRO Since I have only used RStudio, is there any dramatic difference between R and RStudio in terms of system resources used? Does R utilizes all Cores? If it does, is it better to go for more cores or faster CPU?I know that RAM has a great impact. However, I don't really know how much do I really need for individual projects. Invest to 32GB or just go for fast 16GB?At the moment, my SSD seems fine in terms of speed. Sorry for the trouble but there is not any clear answer in the internet. Thank you in advance,William [[alternative HTML version deleted]]
Inline. Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Sun, Aug 20, 2017 at 7:39 AM, Vasilis Bardakos via R-help <r-help at r-project.org> wrote:> I am going to attend MSc Data Science in September, so I consider upgrading my system to be more efficient with my projects.I used RStudio for my Macroeconomics Undergraduate Thesis and I had a couple of loops which needed almost 30 minutes to occur. > My system specifications are the following: > > - CPU: i7 4970k @ 4.0 GHz > - RAM: 8GB DDR3 > - Hard Drive: SSD M.2 950 PRO > > Since I have only used RStudio, is there any dramatic difference between R and RStudio in terms of system resources used? > Does R utilizes all Cores? If it does, is it better to go for more cores or faster CPU?I know that RAM has a great impact. However, I don't really know how much do I really need for individual projects. Invest to 32GB or just go for fast 16GB?At the moment, my SSD seems fine in terms of speed. > Sorry for the trouble but there is not any clear answer in the internet.Nonsense!! Searching on "Does R use multiple cores on a multicore CPU" immediately brought this up: https://stackoverflow.com/questions/4775098/r-package-that-automatically-uses-several-cores This appears to answer all your questions! Note in particular the pointers to CRAN's "High Performance Computing" task view, which should provide up to date answers and packages. Cheers, Bert> Thank you in advance,William > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.
The actual question posed was whether R behaves differently than RStudio at parallel computation, which is like asking whether apple pies grow better than apple trees. The answer is that RStudio doesn't do any computation... it is a programming environment that hands off all computation to a separately-installed copy of the R software. Read the messages in the console right after you start RStudio instead of ignoring them. -- Sent from my phone. Please excuse my brevity. On August 20, 2017 10:43:52 AM PDT, Bert Gunter <bgunter.4567 at gmail.com> wrote:>Inline. >Bert Gunter > >"The trouble with having an open mind is that people keep coming along >and sticking things into it." >-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > > >On Sun, Aug 20, 2017 at 7:39 AM, Vasilis Bardakos via R-help ><r-help at r-project.org> wrote: >> I am going to attend MSc Data Science in September, so I consider >upgrading my system to be more efficient with my projects.I used >RStudio for my Macroeconomics Undergraduate Thesis and I had a couple >of loops which needed almost 30 minutes to occur. >> My system specifications are the following: >> >> - CPU: i7 4970k @ 4.0 GHz >> - RAM: 8GB DDR3 >> - Hard Drive: SSD M.2 950 PRO >> >> Since I have only used RStudio, is there any dramatic difference >between R and RStudio in terms of system resources used? >> Does R utilizes all Cores? If it does, is it better to go for more >cores or faster CPU?I know that RAM has a great impact. However, I >don't really know how much do I really need for individual projects. >Invest to 32GB or just go for fast 16GB?At the moment, my SSD seems >fine in terms of speed. >> Sorry for the trouble but there is not any clear answer in the >internet. > >Nonsense!! Searching on "Does R use multiple cores on a multicore CPU" >immediately brought this up: > >https://stackoverflow.com/questions/4775098/r-package-that-automatically-uses-several-cores > >This appears to answer all your questions! Note in particular the >pointers to CRAN's "High Performance Computing" task view, which >should provide up to date answers and packages. > >Cheers, >Bert > > > > >> Thank you in advance,William >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > >______________________________________________ >R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide >http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code.
The actual question posed was whether R behaves differently than RStudio at parallel computation, which is like asking whether apple pies grow better than apple trees. The answer is that RStudio doesn't do any computation... it is a programming environment that hands off all computation to a separately-installed copy of the R software. Read the messages in the console right after you start RStudio instead of ignoring them. -- Sent from my phone. Please excuse my brevity. On August 20, 2017 10:43:52 AM PDT, Bert Gunter <bgunter.4567 at gmail.com> wrote:>Inline. >Bert Gunter > >"The trouble with having an open mind is that people keep coming along >and sticking things into it." >-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > > >On Sun, Aug 20, 2017 at 7:39 AM, Vasilis Bardakos via R-help ><r-help at r-project.org> wrote: >> I am going to attend MSc Data Science in September, so I consider >upgrading my system to be more efficient with my projects.I used >RStudio for my Macroeconomics Undergraduate Thesis and I had a couple >of loops which needed almost 30 minutes to occur. >> My system specifications are the following: >> >> - CPU: i7 4970k @ 4.0 GHz >> - RAM: 8GB DDR3 >> - Hard Drive: SSD M.2 950 PRO >> >> Since I have only used RStudio, is there any dramatic difference >between R and RStudio in terms of system resources used? >> Does R utilizes all Cores? If it does, is it better to go for more >cores or faster CPU?I know that RAM has a great impact. However, I >don't really know how much do I really need for individual projects. >Invest to 32GB or just go for fast 16GB?At the moment, my SSD seems >fine in terms of speed. >> Sorry for the trouble but there is not any clear answer in the >internet. > >Nonsense!! Searching on "Does R use multiple cores on a multicore CPU" >immediately brought this up: > >https://stackoverflow.com/questions/4775098/r-package-that-automatically-uses-several-cores > >This appears to answer all your questions! Note in particular the >pointers to CRAN's "High Performance Computing" task view, which >should provide up to date answers and packages. > >Cheers, >Bert > > > > >> Thank you in advance,William >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > >______________________________________________ >R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide >http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code.-- Sent from my phone. Please excuse my brevity.