Hi Markus,
I'm happy to participate in this, as I think I said previously.
I won't have time to look carefully at the draft until sometime next
week, but I remain puzzled about the high time listed for case 3 with
snow/Rmpi. It would be good to understand what is going on there --
the discrepan
I see. Thank you very much.
On Mon, Nov 24, 2008 at 10:12 AM, Stefan Evert <[EMAIL PROTECTED]> wrote:
>
>> I'm sorry but I don't quite understand what "not running solve() in
>> this process" means. I updated the code and it do show that the result
>> from clusterApply() are identical with the res
Hi,
there is a new mailing list for R and HPC: [EMAIL PROTECTED]
This is probably a better list for this question. Do not forget, first
of all you have to register: https://stat.ethz.ch/mailman/listinfo/r-sig-hpc
In this case the communication overhead is the problem. The data /
matrix is to big!
I'm sorry but I don't quite understand what "not running solve() in
this process" means. I updated the code and it do show that the result
from clusterApply() are identical with the result from lapply(). Could
you please explain more about this?
The point is that a parallel processing framewor
-- Forwarded message --
From: Hesen Peng <[EMAIL PROTECTED]>
Date: Mon, Nov 24, 2008 at 9:28 AM
Subject: Re: [R] More than doubling performance with snow
To: Prof Brian Ripley <[EMAIL PROTECTED]>
I'm sorry but I don't quite understand what "not runn
On Sun, 23 Nov 2008, Hesen Peng wrote:
Hey my R buddies,
I installed the "snow" and "rpvm" package on my Lenovo Thinkpad T400
today. The experiment below gave me a surprise. The time consumed by
serial processing was several times larger than that taken by parallel
processing. I'm very curious
Hey my R buddies,
I installed the "snow" and "rpvm" package on my Lenovo Thinkpad T400
today. The experiment below gave me a surprise. The time consumed by
serial processing was several times larger than that taken by parallel
processing. I'm very curious how this happened. Thank you very much.
>
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