[R] Query about computational demand
jgarcia at ija.csic.es
jgarcia at ija.csic.es
Mon Oct 6 16:46:39 CEST 2008
Hi all;
I've programmed a couple of C libraries which are loaded dynamically into
R (Linux). With one of these, I'm conducting Monte Carlo analysis, but
every individual execution of my model is about 15'. So, I'm running 1000
executions in about 11 days.
This is not enough for my needings, as I need about 50000 executions for a
sensible analisis of the parameter space. I'm not an expert programmer,
and so, I've got several doubts:
a) I leave R to manage all memory issues. Would a similar C code be faster
if it would be executed as an isolated code outside R?
b) I've been offered two options, to execute remotely the Monte Carlo runs.
Xeon x86_64: with 8 processors
Itanium: with 128 processors y 500Gb de RAM
With the common R programming (without any specific programming for
parallel computing), would my dynamically loaded C library and R benefit
of having several processors?
c) How could I report to the people who perhaps offer me the computational
resources the maximum amount of memory I need?
Thanks, and sorry if these are too basic questions.
Javier
------------------
> On 03/10/2008 7:19 PM, Tomas Lanczos wrote:
>> Thank You for Your answer, Duncan,
>>
>> Duncan Murdoch wrote:
>>> On 03/10/2008 4:33 AM, Tomas Lanczos wrote:
>>>> hello,
>>>>
>>>> I wish to create some 3d scatter diagrams visualising different
>>>> grouped data set by a given field in the database. I tried the
>>>> scatterplot3d package, as well as the plot3d and scatter3d functions
>>>> (both within the rgl resp. Rcmdr package). My first question is,
>>>> whether is it possibe to group data in the scatterplot3d and plot3d,
>>>> because I did not succeed to use the groups = ... function.
>>> There is no groups argument to plot3d, but you can set characteristics
>>> of each point separately. So if you can calculate a colour for each
>>> point yourself, you can do something like
>>>
>>> plot3d(br_scatter[,c("cl", "br", "hco3")], col=colour)
>> I see, but it is something new for me. So, if I understood You well, You
>> advice to prepare another column containing colour codes (colour names?)
>> for each point?
>
> Yes, though it needn't be a column of br_scatter. A vector of the right
> length will work.
>
> Duncan Murdoch
>>
>>> If you want different sizes for each point, you have to plot each
>>> group separately; the size= attribute can't be a vector. You could
>>> also use text3d to plot character labels, e.g.
>>>
>>> plot3d(br_scatter[,c("cl", "br", "hco3")], type="n")
>>> text3d(br_scatter[,c("cl", "br", "hco3")],
>>> text=br_scatter$stratigraphy)
>> In some cases it should be nice, but I have hundreds of points, no space
>> left for labels, but I can use it later.
>>
>> Tomas
>>> Duncan Murdoch
>>>
>>>> The scatter3d behaves a bit wierdly with the groups function: it
>>>> works well with data imported from a CSV file, but when I tried to
>>>> apply it to a data imported from a PostgreSQL database (using the
>>>> Rdbi and RdbiPgSQL packages) it gives me this error message:
>>>>
>>>> ERROR:
>>>> groups variable must be a factor.
>>>>
>>>> To be more clear here is a command I used with the scatter3d (exactly
>>>> the same for the both datasets):
>>>>
>>>> scatter3d(br_scatter$cl, br_scatter$br, br_scatter$hco3,
>>>> fit="linear", residuals=TRUE, bg="white", axis.scales=TRUE,
>>>> grid=TRUE, ellipsoid=FALSE, xlab="cl", ylab="br", zlab="hco3", groups
>>>> = br_scatter$stratigraphy)
>>>>
>>>> the dataset I used is here (the same is the data imported from the
>>>> CSV file and from a PostgreSQL table) looks like this (a part of it):
>>>>
>>>> stratigraphy br hco3 cl
>>>> 1 sarmat 0.2327793352 507.006513 262.781114
>>>> 2 sarmat 0.3741990388 1021.788317 214.254486
>>>> 3 baden 0.3354024830 1268.847582 253.639356
>>>> 4 sarmat 0.0938626352 46.514244 38.995620
>>>> 5 sarmat 0.1163896676 18.300686 72.984568
>>>> 6 sarmat 0.2090008010 77.777917 131.989947
>>>> 7 sarmat 0.2815879055 53.802018 146.804052
>>>> 8 panon 0.0450540649 81.590560 274.980467
>>>> 9 baden 0.5619243092 61.752316 275.978980
>>>> 10 karpat 0.4655586704 16.019351 179.537807
>>>> 11 mezozoikum 0.6244993993 133.442504 152.986938
>>>> 12 panon 0.1539347217 132.679975 65.994974
>>>> 13 sarmat 0.0375450541 19.825743 24.996686
>>>> 14 sarmat 0.0375450541 20.588272 26.280086
>>>> 15 baden 0.0463055667 19.063215 26.494456
>>>> 16 baden 0.1864737685 40.414016 93.992841
>>>> 17 sarmat 0.9236083300 90.740903 597.954458
>>>> 18 panon 0.8022126552 57.189645 499.961921
>>>> 19 panon 0.4830796956 68.627574 280.001241
>>>> 20 panon 0.1163896676 73.202745 53.995887
>>>>
>>>> So why the exactly same "stratigraphy" field is a factor in the
>>>> dataset imported from a CSV file and why is not a factor in the
>>>> dataset imported from a PostgreSQL table
>>>>
>>>> Many thanks in advance
>>>>
>>>> Tomas
>>>>
>>>> ______________________________________________
>>>> R-help at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>> PLEASE do read the posting guide
>>>> http://www.R-project.org/posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>> ______________________________________________
>>> R-help at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide
>>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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