Raul Miller writes: > Even then your dominant cost is reading the data off of disk. If the > data is converted on the way in from the disk to RAM, and most of your > CPU time is spent waiting for the disk, your conversion time might not > even be measurable.
My situation would be that the data is read to memory, ending up in a NumPy array, and then I have to copy it to a J array in a subsequent step. That's why I am interested in exploring the shared data approach. > Note that if your data structure occupies half of your physical > memory, J intermediate results might be expensive -- my rule of thumb > (just a guess based on raw data size, until I get real measurements) > is to expect 5x memory overhead from J. That's also a problem with NumPy, but there I know how to get around it. With J, that remains to be explored. > That said, have you seen the pages linked from > http://www.jsoftware.com/help/user/dlls.htm? Seen, yes, read in detail, no. Thanks for the suggestions, Konrad. ---------------------------------------------------------------------- For information about J forums see http://www.jsoftware.com/forums.htm
