Using more than 32 cores in 3Ddeconvolve

[dglen]

Rick's point about memory is a good one. If you are running out of memory and not CPUs, then the speed can be limited by disk thrashing by swapping virtual memory to and from the disk. Check during the processing with top and ps or similar tools.

There are several previous threads with more good advice:

3dDeconvolve "killed" because out of memory

Hi AFNI team, I’m running 3dDeconvolve on my dataset and it often got killed. The dataset is quite large. It has 32 runs of 10 min scanning, each run is ~400MB. I run it on different computers and it seems not solely depend on the size of memory whether the process will be killed. Here are my questions: why is it that some computers have enough memory and hard drive but still got killed? For example, I ran this on two computers, both have 64GB memory and >500GB hard drive. One always ran s…

Modeling the BOLD5000 dataset

If you wish to analyze such a large dataset as a whole, there are some options in AFNI. Most of them will require running on a system with enough RAM to hold the entire dataset easily. However, if you are industrious enough, you can break the dataset into pieces to reduce the memory footprint required at any given point. No matter what you do, you will have to manage the analysis yourself, as I doubt that you can get afni_proc.py to do the work for you – unless you have access to a computer sys…