Should I use floating point's NaN, or floating point + bool for a data set that contains invalid values?

Question

I have a large amount of data to process with math intensive operations on each data set. Much of it is analogous to image processing. However, since this data is read direc

Answer 1

If invalid data is very common, you are of course wasting a lot of time on running this data through the processing. If the invalid data is common enough it is probably better to be running some kind of sparse datastructure of only the valid data. If it is not very common, you can of course keep a sparse datastructure of which data is invalid. That way you would not waste a bool for each value. But maybe memory is not a problem for you...

If you are doing operations such as multipling two possibly invalid data entries, I understand it is compelling to use NaNs instead of doing checks on both variables to see if they are valid and setting the same flag in the resultant.

How portable do you need to be? Will you ever need to be able to port it to an architecture with only fixed point support? If that is the case, I think your choice is clear.

Personally I would only use NaNs if it proved to be much faster. Otherwise I'd say the code gets more clear if you have explicit handling of invalid data.