Hey all, I was planning on getting a new computer for the new year to help with my dissertation research, and recently had an opportunity to compare the performance of my 1.6 GHz Pentium M laptop, and a 2.8 GHz dual-core Pentium processor (both running WinXP professional 32-bit). I run a lot of long simulations, so I was hoping to get something that would speed them up. I ran a few quick computation tests (e.g. generating 500,000 normals), and found the performance increase of the 2.8 dual-core over my 1.6M laptop to be negligable (and in fact sometimes slower). One thing I did notice that if I look at the CPU usage of my laptop when it's performing the simulations the laptop is at about 100%, while the dual-core reaches 50% (and seems to refuse to go higher than 50% no matter what). Of course if I load up 2 instances of R I can get them to run the simulations simultaneously in about the same amount of time, but this doesn't help me get to the end of a very long simulation quicker. This got me thinking that no matter what kind of processor I get, I'm not going to be getting a large speed increase over what I already have. Does anyone have any insight into various setups/processors that would help me speed up my work (e.g. maybe run R through linux, Pentium extreme edition, etc)? Thanks in advance, Adam Petrie Rensselaer Polytechnic Institute
Prof Brian Ripley
2005-Dec-27 07:32 UTC
[R] No performance increase from dual-core processors?
R only runs multiple computational threads as part of a BLAS/LAPACK addon on Unix-alikes, so no speed-up is expected from dual processors (which includes Intel's HyperThreading, as well as dual-cored systems). A faster processor would give you considerable increases even single-core, and a 2.8GHz Pentium is quite slow compared to an Athlon64 or Opteron (as you have found: P4 systems are slow for their clock speeds compared to PIIIs (such as Pentium M) or almost anything else). My 2GHz Pentium M laptop is faster than my 2.8GHz P4 home desktop, and an Opteron 252 is considerably faster again (even running a 64-bit OS and build of R). BTW, testing 500,000 normals is a single R command and is for me fast enough to be hard to time accurately:> system.time(x <- rnorm(5e5))[1] 0.10 0.01 0.10 0.00 0.00 on an Opteron 252. On Mon, 26 Dec 2005, Adam Petrie wrote:> Hey all, > > I was planning on getting a new computer for the new year to help with > my dissertation research, and recently had an opportunity to compare the > performance of my 1.6 GHz Pentium M laptop, and a 2.8 GHz dual-core > Pentium processor (both running WinXP professional 32-bit). I run a lot > of long simulations, so I was hoping to get something that would speed > them up. I ran a few quick computation tests (e.g. generating 500,000 > normals), and found the performance increase of the 2.8 dual-core over > my 1.6M laptop to be negligable (and in fact sometimes slower). One > thing I did notice that if I look at the CPU usage of my laptop when > it's performing the simulations the laptop is at about 100%, while the > dual-core reaches 50% (and seems to refuse to go higher than 50% no > matter what). Of course if I load up 2 instances of R I can get them to > run the simulations simultaneously in about the same amount of time, but > this doesn't help me get to the end of a very long simulation quicker. > This got me thinking that no matter what kind of processor I get, I'm > not going to be getting a large speed increase over what I already > have. Does anyone have any insight into various setups/processors that > would help me speed up my work (e.g. maybe run R through linux, Pentium > extreme edition, etc)? > > Thanks in advance, > > Adam Petrie > Rensselaer Polytechnic Institute > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >-- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595