[ncl-talk] Is there a way to run NCL on multiple processors?

Mon Oct 13 13:56:22 MDT 2014

I'd like to mention that there is work ongoing to enable multi-threading
via OpenMP in selected NCL bottleneck areas, including raster graphics for
high-res data, and some particular processing routines. Some of this should
be publicly available in NCL 6.3.0.
 -dave

On Mon, Oct 13, 2014 at 1:40 PM, Dennis Shea <shea at ucar.edu> wrote:

> This is often called  'task prallelism' or  'embarrasingly parallel'.
> From the WWW: "A problem that is obviously decomposable into many
> identical but separate subtasks is called 'embarrasingly parallel.'
>
> ==
> There is tool developed within our section called the CVDP (Climate
> Variability Diagnostics Processor). NCL is used to perform shell scripting
> type tasks and to do the diagnostics which include EOFs, assorted indices,
> etc, netCDF creation and graphics for 100+ ensemble  members. It can take
> 3+days to run in serial mode. Each ensemble run is independent of all other
> ensemble runs.
>
> I talked with a software engineer within NCAR's computing division and she
> proposed a python approach. She called it 'python light'. Here is a crude
> prototype of what it might look like. It uses NCL's 'command line
> arguments'.
>
> =====
> #!/usr/bin/env python
>
> import parUtils as par
> from mpi4py import MPI
> import os
>
> # the following is an ascii (text) file which contains the names of
> # ensembles members to process and the periods
> namelist_fn = 'namelist.CESMcomparison'
>
> # open the namelist file and store it in a file pointer variable
> # think of this as NCL's asciiread. Each line is a different ensemble
> member
> namelist_fp = open(namelist_fn,"r")
>
> # read the file and split the lines into an array
> # think of this as NCL's str_get_field
> nl_lines = namelist_fp.read().splitlines()
>
> # partition the lines up between the mpi tasks
> local_nl_lines = par.Partition(nl_lines)
>
> # for each line in the orig nl file that this task is responsible for,
> # parse the line and call ncl with the variables
>
> for line in local_nl_lines:
>   #split line at "|"
>   name,location,start,stop = line.split("|")
>   os.system("ncl 'name=\""+name+"\"' 'location=\""+location+"\"'
> 'start=\""+start+"\"' 'stop=\""+stop+"\"' CVDP.ncl")
>
> # Wait for all procs to get to this point
> par.Sync()
>
> =======
>
> The advantage is that  python  does the book keeping.
>
> =======
>
>
> Nothing much has gone beyond the above. I am just too busy to focus on
> this right now.
>
> D
>
>
>
>
> On Mon, Oct 13, 2014 at 12:29 PM, jbuzan <jbuzan at purdue.edu> wrote:
>
>> Yes, I agree.  I recommend using shell scripts that feed NCL the
>> specifics.  For example, I have scripts that will submit 100s of ncl
>> routines that are executed on individual processors on a cluster.  This
>> aides you in a couple of ways: 1) you have short NCL scripts, which reduce
>> programming time, 2) they are less likely to have errors script errors, and
>> 3) you speed up your throughput dramatically.
>>
>> -Jonathan
>>
>>
>> On Oct 13, 2014, at 2:22 PM, Colin Zarzycki <zarzycki at umich.edu> wrote:
>>
>> It's worth pointing out that for some applications, you can write shell
>> scripts which can pseudo-parallelize NCL by running multiple instances
>> (which are in serial) alongside one another.
>>
>> Ex: If you have a high-resolution file which contains 20 time records and
>> you would like to use NCL to calculate vorticity from U/V for each of the
>> 20 time records, you could spawn 20 instances of NCL which each operate on
>> 1 of the time records. Then concatenate the output in serial after each of
>> the 20 jobs finishes. This will be much faster than reading the entire
>> dataset into a single instance of NCL.
>>
>> I have found this technique highly useful when processing very large
>> datasets (or large numbers of files).
>>
>> -Colin
>>
>> On Sun, Oct 12, 2014 at 9:29 PM, 杨玉华 <yangyh at mail.typhoon.gov.cn> wrote:
>>
>>>  Dear Kyle Griffin,
>>>
>>> Thanks for your great information.
>>>
>>> Yuhua
>>>
>>>
>>> *您于 Sun, 12 Oct 2014 22:03:49 -0500 的来信中写到: *
>>>
>>>  There is the ParVis project, based on being able to run a select few
>>> (but crucial) functions from NCL on multiple processors:
>>>
>>> http://trac.mcs.anl.gov/projects/parvis/wiki
>>>
>>> Unfortunately, the only binaries provided are for system types not
>>> widely used in the atmospheric science community (as far as I'm aware) and
>>> the build instructions aren't particularly detailed. As such, I've never
>>> gotten the ncl included in the ParVis project to work. There is a separate
>>> mailing list for the discussion, should you have any problems and/or
>>> success with the software. Beyond this, though, I'm not aware of any other
>>> efforts to parallelize NCL.
>>>
>>>
>>> Kyle
>>>
>>>  ----------------------------------------
>>> Kyle S. Griffin
>>> Department of Atmospheric and Oceanic Sciences
>>> University of Wisconsin - Madison
>>> Room 1421
>>> 1225 W Dayton St, Madison, WI 53706
>>> Email: ksgriffin2 at wisc.edu
>>>
>>> 2014-10-12 21:56 GMT-05:00 杨玉华 <yangyh at mail.typhoon.gov.cn>:
>>>
>>>>  Hi all,
>>>> Is there a way to run NCL on multiple processors?
>>>> Thanks,
>>>> Yuhua
>>>>
>>>> ＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝
>>>> 名称： 中国气象局上海台风研究所
>>>> 地址： 中国上海市徐汇区蒲西路166号
>>>> 电话：（＋86）021-64391966
>>>> 传真：（＋86）021-64391966
>>>> ============================
>>>>
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>>>
>>> ＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝
>>> 名称： 中国气象局上海台风研究所
>>> 地址： 中国上海市徐汇区蒲西路166号
>>> 电话：（＋86）021-64391966
>>> 传真：（＋86）021-64391966
>>> ============================
>>>
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>>> --
>>> <http://mailman.ucar.edu/mailman/listinfo/ncl-talk>
>>> Colin M. Zarzycki, ASP postdoctoral fellow
>>> <http://mailman.ucar.edu/mailman/listinfo/ncl-talk>
>>> Atmospheric Modeling and Predictability
>>> National Center for Atmospheric Research
>>> <http://mailman.ucar.edu/mailman/listinfo/ncl-talk>
>>> http://www.colinzarzycki.com
>>>
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