[Mesa-users] Problem with MESA version 10398.

[Evan Bauer]

Hi Ian,

How is your disk usage? Caching the PTEH files can require several GB of disk space, so I wonder if this is a symptom of the container running out of available space to write the cache files?

Cheers,
Evan


> On Apr 12, 2018, at 9:18 PM, Ian Foley via Mesa-users <mesa-users at lists.mesastar.org> wrote:
> 
> Hi,
> 
> I am running docker for windows on Windows 10 Pro on a machine with 4 cores and 8GB RAM. I have been working on developing inlists to evolve 1M to 15M stars from pre_ms to end (wd or cc). I have been doing this since version 7624 and always if the run failed MESA would give a reason related to the inlist chosen e.g. timestep limit.
> 
> However, with version 10398, the run has failed quite often by either just hanging leaving screen displays intact, but CPU drops to <10% instead of >50% (program in loop?) or the operating system kills the run. If the process has been killed I am confident that it is not a shortage of memory problem since I increased docker for windows allocation from 3GB to 3.5GB and still had the same problem with the same inlist.
> 
> In all these cases, the run environment was one with a large envelope and very low surface pressure and density and the run was requiring to cache new eosPTEH files.
> 
> I have attached all the files necessary for users to execute a rerun and hopefully reproduce the error and locate the problem. I have also attached all the terminal output (11M,txt) and two files showing the last screens from pgstar. This run hung and it can be seen by looking at the final output in 11M.txt.
> 
> It perhaps should be noted that by changing the inlist to set
> 
>       use_eosPTEH_for_low_density = .false. ! default .true.
>       use_eosPTEH_for_high_Z = .false. ! default .true.
> 
> I was able to complete the run with the final outcome being wd - just. The He core was 1.33M.
> 
> Please note that run_star_extras.f does quite a few things whose intention is to try to get a successful run without having to stop mid-way and change parameters. One of the strategies here is to dynamically change var_control according to the number of retries in 10 models and thus allow larger delta changes in parameters and let var_control the size of logdt. This usually works, but can fail when there are a large number of retries in 10 models and var_control does not adapt fast enough.
> 
> Kind regards
> Ian
> 
> 
> <11M.txt><history_columns.list><ian40r.net><inlist_11M.0><profile_columns.list><run_star_extras.f><grid6_001290.png><profile_Panels3_001290.png>_______________________________________________
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