Problem allocating cond. likelihood array

[Zhao LEI]

Dear All,

I recently met this problem when using Garli to run a big nuclear dataset with partitioned data using GTR+I+G and codon model (Please see the log below). 

I though it maybe the issue of memory. However, after change the memory into great level, this problem still appear... 

I am wondering is there anyone has met the same problem before and does anybody know how to resolve it? 

Thanks a lot!

###################################################

NOTE: Unlike many programs, the amount of system memory that Garli will

use can be controlled by the user.

(This comes from the availablememory setting in the configuration file.

Availablememory should NOT be set to more than the actual amount of 

physical memory that your computer has installed)

For this dataset:

 Mem level availablememory setting

  great     >= 782286 MB

  good approx 782285 MB to 494724 MB

  low approx 494723 MB to 201366 MB

  very low approx 201365 MB to 150663 MB

the minimum required availablememory is 150663 MB

You specified that Garli should use at most 100000000.0 MB of memory.

Garli will actually use approx. 1173429.0 MB of memory

**Your memory level is: great (you don't need to change anything)**

#######################################################

ERROR: Problem allocating cond. likelihood array (len = 75747680). Out of mem?

-Press enter to close program.-

[Derrick Zwickl]

Hello,

The minimum amount of memory that this dataset would require (150663 MB = 150 GB) is much more than almost any system will have.  The message is arising because GALRI tries to allocate some memory, and the operating system doesn't have that much available.  In general if you get that message you'll want to decrease the availablememory setting to near the minimum required, in which case it will try to allocate less memory.  The amount that you set it to should be somewhat less than whatever amount of RAM your computer has.  So, unfortunately in this case you'll need access to a very high memory machine or need to reduce the size of your dataset.

Let me know if you have other questions.
Best,
Derrick

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