If you hit issues with DoubleDouble and/or generic math algorithms in Julia's stdlib, please do file issues. We want to get to the point where *everything* just works.
On Mon, Jun 20, 2016 at 4:31 PM, Chris Rackauckas <[email protected]> wrote: > I see. I surely can't wait for native bigs, but there are definitely more > pressing issues to work on first. Tim's proof of principle results look > really nice. > > I tried DoubleDouble and ran into some errors. I don't think it worked > with all standard library math. I'll probably take another look at it in a > month. That is definitely the right direction to go for now. > > On Monday, June 20, 2016 at 7:09:57 PM UTC+1, Stefan Karpinski wrote: >> >> I think it might actually be easier for BigFloat since BigFloats are >> fixed-size, whereas BigInts are variable-size. >> >> Chris, there is a DoubleDouble package >> <https://github.com/simonbyrne/DoubleDouble.jl>, which implements >> efficient higher-precision floating-point arithmetic, albeit not IEEE >> 128-bit floats. As soon as hardware and LLVM support 128-bit IEEE floats, >> Julia can easily support them as well – as I'm sure you realize, much more >> easily than any other system. >> >> Nobody wants BigFloats to be inefficient; the current state of the >> compiler's ability to reuse them and eliminate allocations simply isn't as >> good as it could potentially be. That doesn't mean that this won't be >> improved in the future – it will be, although it's hard to say when since >> there are a lot of competing priorities and a limited number of people who >> can do the kind of compiler work necessary to improve this situation. >> Fortunately, the problem is closely related to a number of other >> performance issues that we also need to address (strings, array views). >> >
