I'm using a Dell Latitude E7440 Laptop with Windows 7 Enterprise OS, 8GB RAM, 64-bit OS, Intel(R) Core(TM) i7-4600U CPU @ 2.10GHz Processor, 2701 Mhz, 2 Cores, 4 Logical Processors (that's 4 cores).
I'm using a Dell Precision Tower 7810 Desktop with Windows 7 Enterprise OS, 32GB RAM, 64-bit OS, Intel(R) Xeon(R) CPU E5-2620 v3 @ 2.40GHz 2 Processors, 2401 Mhz, 6 Cores, 12 Logical Processors (that's 24 cores).
A good demonstration of my use of R would be running binary classification using gbm
in RStudio on 100K-sized data with ~300 features. But whatever I do on my laptop R version (all other software closed, no use of parallelization), is considerably faster than on my Desktop R version. How can that be? What do I need to do to find out?
Laptop:
> sum <- 0; system.time(for (i in 1:1000000) sum <- sum + i)
user system elapsed
0.36 0.00 0.36
> memory.limit()
[1] 8097
> sessionInfo()
R version 3.3.1 (2016-06-21)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] tools_3.3.1
Desktop:
> sum <- 0; system.time(for (i in 1:1000000) sum <- sum + i)
user system elapsed
0.52 0.00 0.52
> memory.limit()
[1] 32684
> sessionInfo()
R version 3.3.1 (2016-06-21)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] tools_3.3.1