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doku:gpaw [2014/08/27 11:10] – markusdoku:gpaw [2014/08/27 11:27] (current) – jz
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 === running gpaw jobs === === running gpaw jobs ===
-== Job submission using all cores on the compute nodes ==+== Job submission using all cores on the compute nodes (VSC-1 and VSC-2) ==
 <code> <code>
 #!/bin/sh #!/bin/sh
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 </code> </code>
  
-== Job submission using half of the cores on the compute nodes on VSC 2 ==+== Job submission using half of the cores on the compute nodes on VSC-2. == 
 +If each of your processes require more than 2GB (and less than 4GB) of memory, you can use the parallel environment ''mpich8''. This will allocate only 8 processes on each node while still starting 256 processes but distributed over 32 nodes. It is necessary to use the variable ''$NSLOTS_REDUCED'' instead of ''$NSLOTS'' in that case.
 <code> <code>
 #!/bin/sh #!/bin/sh
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 mpirun -machinefile $TMPDIR/machines -np $NSLOTS_REDUCED gpaw-python static.py --domain=None --band=1 --sl_default=4,4,64 mpirun -machinefile $TMPDIR/machines -np $NSLOTS_REDUCED gpaw-python static.py --domain=None --band=1 --sl_default=4,4,64
 </code> </code>
 +If even more memory per process is required the environments ''mpich4'', ''mpich2'', and ''mpich1'' are also available, as discussed in [[doku:ompmpi|Hybrid OpenMP/MPI jobs]].
 +Alternatively, you can simply start your GPAW job with more processes which will reduce the amount of memory per process. GPAW usually scales well with the number of processes.
  
 Significant speed up is seen **in our test case** when --sl_default is set. First two parameters for the BLACS grid should be similar in size to get an optimal memory distribution on the nodes.  Significant speed up is seen **in our test case** when --sl_default is set. First two parameters for the BLACS grid should be similar in size to get an optimal memory distribution on the nodes. 
  • doku/gpaw.1409137831.txt.gz
  • Last modified: 2014/08/27 11:10
  • by markus