 |
 |
Hi Nicola I think that manually handling the number of fragments won't bring you to a sensible solution. I would suggest a very practical approach by creating a swap space as large as you need memory. In my personal experience I've partitioned a 250GB SSD as swap to make it through a similar situation. If you can't dedicate a large partition you can create a swap file within the disk you have available (it would be awkward to have a 250GB file but it will work). See you! Stefano Orsolini 2017-10-08 15:59 GMT+02:00 Nicola Toschi <[log in to unmask]>: > Hi List, > > I am trying to run a randomise analysis on a dataset of about 1500 volumes > in MNI space (with brain mask). On a single machine, i need about 70 GB RAM > for randomise to be able to load the data which drops to about 50 GB for > carrying out permutations. > > Given that, if I understand correctly, every instance of randomise > launched by randomise_parallel will have the same memory requirements (i.e. > it will have to load the whole dataset), I can't just throw this on a > cluster which has, say 150 GB RAM per node with default settings. > > Ideally I would like to set the number of fragments to 2 or 3 (better than > 1!) and have everything follow from that (time, number of permutations). > > Is this easily controllable in randomise_parallel (didn't look like it was > when perusing the script)? > > Thanks a lot in advance! > > Nicola >