**This package will not be updated for Julia 1.0 / 0.7. It is recommended to use DoubleFloats.jl instead.**

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HigherPrecision defines the following subtypes of `AbstractFloat`

`DoubleFloat64`

- A 128 bit number type with around 30 digits of precision.

These types are intended as a drop-in replacement for `Float64`

and `BigFloat`

.
Besides the basic arithmetic functions the following mathematical functions are defined:

`sin`

, `cos`

, `inv`

, `rem`

, `divrem`

, `mod`

,
`sqrt`

, `exp`

, `log`

, `sin`

, `cos`

, `tan`

,
`asin`

, `acos`

, `atan`

, `atan2`

,
`sinh`

, `cosh`

, `sincos`

, `sincosh`

, `tanh`

Basic arithmetic operations are significantly faster than `BigFloat`

, but the above
mathematical functions can be slower than the corresponding `BigFloat`

methods. In general
this still should yield a significant performance boost.

This library needs FMA instructions. If your processor supports these, you probably still need to rebuild your Julia system image. This can be done as follows

```
include(joinpath(dirname(JULIA_HOME),"share","julia","build_sysimg.jl")); build_sysimg(force=true)
```

If you are on Windows you need to run the following code first

```
Pkg.add("WinRPM");
WinRPM.install("gcc", yes=true)
WinRPM.install("winpthreads-devel", yes=true)
```

```
# Simply convert an irrational number to a DoubleFloat64
double_π = DoubleFloat64(π)
# y is again a DoubleFloat64
y = rand() * double_π
# You can also create a DoubleFloat64 from a Float64
x = DoubleFloat64(0.42)
# And use the usual functions
sin(x * y)
```

This library is a port of the QD library from Yozo Hida (U.C. Berkeley), Xiaoye S. Li (Lawrence Berkeley National Lab) and David H. Bailey (Lawrence Berkeley National Lab) from C++ to Julia. See COPYING for the original modified BSD license. Also see this paper for some background informations.

10/09/2017

17 days ago

42 commits