The particulars of the multi-threading model required to make [ dynamic seeding] work means that you often won’t get full CPU usage with that option enabled (@rsmith will correct me if I’m wrong).
I think it gets full CPU usage up to a certain point. As in, an i7 will get 800%, but you might not get 3200% on a dual Xeon 16-core. @Carlotta_Fabris It would be worth running
top: If the slow execution is indeed due to inadequate RAM, the CPU usage should be significantly lower than (
100% x number_of_threads) for your system.
It does however require additional track-to-fixel mapping computations, which means that not all of that CPU usage is going to streamlines propagation.
I am trying to do the ISMRM tutorial, do you have any suggestion?
If changing from dynamic seeding to something else doesn’t solve the problem, you could alternatively try down-sampling the DWI / FOD data to a lower spatial resolution.
Another trick that might reduce RAM usage while preserving the high spatial resolution is
maskcrop. Sometimes DWIs have a lot of “dead space” on either side of the brain, that’s either zero-filled or effectively contains noise outside the brain. By reducing the image FoV to only extend as far as is required to encompass the brain, the un-compressed image size can be reduced by as much as 50%.