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
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 result from
-- 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 running solve() in
this process means. I
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 framework
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
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.
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 how
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