Just out of curiosity, can you please post some benchmarks of Float32 vs Float in Julia for your algorithm when you finish what you are working on?
My experience on modern x86 architectures is that CPU handles both at the approximately same speeds, and when I have big matrices the speed benefit of single float comes from using less memory, but that is not worth dealing with the subtle problems that come from loss of precision (in particular, it is very easy to run into conditioning problems with single float). Best, Tamas On Wed, Oct 15 2014, Hubert Soyer <[email protected]> wrote: > Sorry for coming back to this so late. I forgot to subscribe to get updates > via email and thought nobody had replied yet. > Thank you for all the comments, I think I get the point. > > I am coming from a python background and was and am still working with a > module called Theano that provides automatic differentiation and is used a > lot for neural networks. > This module offers an environment variable (floatX) that lets me specify > whether I want to use Float64 or Float32 all the way. > > My workflow would then be: > Prototype with Float32 to get the speed benefit. > When I want to have a more serious look at my results, I switch to Float64 > to be safe. > > So I thought I'd ask this question and if it turns out that there is a > switch like that in Julia, I could just use it. > I do understand that this was just a convenient "hack" for me and it will > definitely work without that functionality. > But in case it existed but just wasn't documented, I thought I'd ask. > > Thank you a lot for your comments, Pontus' solution seems to cover my use > case just fine, I think I'll go with that. > > Best, > > Hubert
