Hello Xi,
Have you tried replacing the for loop by an apply construct, e.g., lapply or
sapply? In my experience these functions are more efficient than for. At any
rate, if you succeed with, say, lapply, there are some R packages that support
parallel processing versions. I believe the packa
Hi Xi,
Maybe you should try to "parallelize" your calculations.
See package "parallel".
http://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf
Arnaud
On Mon, Jun 25, 2012 at 8:07 PM, Xi wrote:
> Dear All,
>
> I have been searching online for help increasing my R code more
effici
Hello Christopher,
If a process has data to write to hard disk, the data is usually
written to a buffer in memory, and from there it is written to the
hard disk independently of the CPU. Since writing to memory is much
faster than writing to hard disk, this allows the process to run
faster.
On 26-06-2012 16:33, Oliver Ruebenacker wrote:
Hello Xi,
If a program has input or output to disk or network, this may cause
it to wait and not use the available CPU.
Output is usually buffered, but may cause delay if the buffer gets
full (I'm not sure though whether this is an issu
Hello Xi,
If a program has input or output to disk or network, this may cause
it to wait and not use the available CPU.
Output is usually buffered, but may cause delay if the buffer gets
full (I'm not sure though whether this is an issue with plenty of
memory available)
Take care
See the vignette for package 'parallel' to make use of your 4 cores.
On 26/06/2012 01:07, Xi wrote:
Dear All,
I have been searching online for help increasing my R code more efficiently
for almost a whole day, however, there is no solution to my case. So if
anyone could give any clue to solve m
Dear All,
I have been searching online for help increasing my R code more efficiently
for almost a whole day, however, there is no solution to my case. So if
anyone could give any clue to solve my problem, I would be very appreciate
for you help. Thanks in advance.
Here is my issue:
My desktop i
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