[BioC] Reading 80 CEL files.

[olsen]

Weiwei,
1000 hgu133plus2 arrays take 1 hr by RMA, 16 hr by
GCRMA
the configuration is.
8 cores AMD opteron 885, 64 bit processor 1.8 Mhtz
64 GB RAM
SuSe Linux Version 9 SP3.
Any big institution has this kind of computer power.

Oscar Puig


--- Weiwei Shi <helprhelp at gmail.com> wrote:

> Oscar,
> 
> I am curious how long it runs and the configuration
> of the linux cluster.
> 
> 
> weiwei
> 
> On 11/17/07, olsen <olsen2002 at yahoo.com> wrote:
> >
> > >
> > > Hello~~~
> > >
> > >
> > >
> > > I am trying to read 80 HG-U133A arrays so I did
> as
> > > follows:
> > >
> > >
> > >
> > > > memory.limit(size=4095)
> > >
> > > > options(object.size=10000000000, digits=8,
> > > scipen=100, memory=3200483647,
> > > contrasts=c("contr.treatment", "contr.poly"))
> > >
> > > > library(affy)
> > >
> > > > cel.file <- ReadAffy(celfile.path = ¡¨D://CEL)
> > >
> > >
> > >
> > > However, it showed
> > >
> > >
> > >
> > > Error: cannot allocate vector of size 309.4 Mb
> > >
> > >
> > >
> > > I have tried adding the --max-mem-size=2Gb tag
> onto
> > > my shortcut.
> > >
> > > The machine has 2G RAM and a 3.30GHz processor.
> > >
> > > Is there any idea to let R use more memory
> anywhere?
> > >
> > > Thank you~
> > >
> > >
> > >
> > > Best Regards,
> > >
> > > Pure
> > >
> > Pure,
> >
> > My advice: run them in Linux. We have run up to
> 1000
> > arrays in a linux cluster.
> >
> > Oscar Puig
> >
> >
> >
> >
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> > >
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> >
>
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> >
> >
> >
> >      
>
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> 
> 
> -- 
> Weiwei Shi, Ph.D
> Research Scientist
> GeneGO, Inc.
> 
> "Did you always know?"
> "No, I did not. But I believed..."
> ---Matrix III
>