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.