README.md updated: 2024/09/07

LIB-Dekker-Float contains a variety of Dekker-Float implementations to boost precision and performance in your C/C++ program.

This library is still under development, so it may or may not work with your program out of the box quite yet. Additionally, some math.h functions are unimplemented or rely on casting to other types.

The floating point implementations are tested on GCC and Clang with -Wall -Wextra -Wpedantic.

.hpp files are for C++11 or later, and .h files are for C99 or later and C++11 or later. The C++ implementations support operator overloading.

Available Precisions:

• Float32x2
• Float64x2
• Float64x4
• Float80x2 (x86/x86_x64)

SIMD Types:

• Float64x2 AVX (x86/x86_x64)
• Float64x4 AVX (x86/x86_x64)

Under Development:

• Float128x2 (Requires quadmath)
• Float64x2 SSE2 (x86/x86_x64)
• Float64x4 SSE2 (x86/x86_x64)
• Float32x4

Attribution

Some of the code is based on the quad-double, double-double, and double-quad implementations from libQD, libDDFUN, and libDQFUN. They can be found at https://www.davidhbailey.com/dhbsoftware/.

libQD is licensed under a modifed BSD license which can be obtained from: https://www.davidhbailey.com/dhbsoftware/LBNL-BSD-License.docx. A copy of the LBNL-BSD-License is can also be found at LIB-Dekker-Float/libQD/LBNL-BSD-License.txt.

libDDFUN and libDQFUN are licensed under a limited BSD license which can be obtained from: https://www.davidhbailey.com/dhbsoftware/DHB-License.docx. A copy of the Limited-BSD-License can also be found at LIB-Dekker-Float/libDDFUN/DISCLAIMER_and_Limited-BSD-License.txt for libDDFUN, and at LIB-Dekker-Float/libDQFUN/DISCLAIMER_and_Limited-BSD-License.txt for libQDFUN.

Test Code

The ./test_CXX11 and ./test_C99 folders contain some basic code to test the libraries. However, no tests/asserts are configured or setup yet.

LDF namespace

The LDF namespace (LIB-Dekker-Float) in C++ provides templates for add, sub, mul, div, square, recip, mul_pwr2, and bitwise operations. The templates follow this pattern: <Ret_Type, OP1_Type, OP2_Type, ...>.

The LDF templates expose some functions that can't be accessed through operator overloads. For example, double / double can't be overloaded to return Float64x4 instead of double; so instead, one would call LDF::div<Float64x4, double, double>(x, y). This allows double / double to be calculated to Float64x4 precision. As another example, LDF::square<Float64x2, double>(x) is able to efficiently square a double to Float64x2 precision.

String Operations

snprintf, stringTo, std::cout, and std::cin functions are provided for converting to and from a string.

To convert a Float64x2 to a string, you can use snprintf_Float64x2(buf, sizeof(buf), "%.20" PRIFloat64x2 "f"), which will print 20 digits after the decimal point.

To convert a string to a Float64x2, you can use Float64x2 value = stringTo_Float64x2("1.2345e3"). In C++, you can also use string literals Float64x2 value = "1.2345e3"_FP64X2.

snprintf specifiers:

• PRIFloat32x2 "DS"
• PRIFloat32x4 "QS"
• PRIFloat64x2 "DD"
• PRIFloat64x4 "QD"
• PRIFloat80x2 "DX"
• PRIFloat128x2 "DQ"

An extended list of snprintf specifiers can be found at LIB-Dekker-Float/docs/Proposed-type-names.txt

MPFR Conversion Functions

Header files to convert to and from mpfr_t are available, and follow a familiar syntax:

#include <mpfr.h>
// Sets a mpfr_t to a Float64x4 value
int mpfr_set_float64x4(mpfr_t rop, Float64x4 op, mpfr_rnd_t rnd);

// Returns a Float64x4 value from a mpfr_t
Float64x4 mpfr_get_float64x4(mpfr_srcptr op, mpfr_rnd_t rnd);

C++ template functions are also included:

#include "util_mpfr/mpfr_convert.hpp"

// Sets a mpfr_t to a Float64x4 value
template <> int mpfr_set_type<Float64x4>(mpfr_t rop, const Float64x4& op, mpfr_rnd_t rnd);

// Returns a Float64x4 value from a mpfr_t
template <> Float64x4 mpfr_get_type<Float64x4>(mpfr_srcptr op, mpfr_rnd_t rnd);

Math Functions

Dekker floats have a very small epsilon. This allows them to represent values such as 1.0 + FLOAT_MIN, which would otherwise be difficult to represent with non-Dekker floats. For example, 1.0 + FLOAT32_MIN would require a ~128bit mantissa to represent with a standard floating point type, while 1.0 + FLOAT128_MIN requires a ~16384bit mantissa to represent without Dekker floats.

Some math functions may offer multiple variants to balance between speed and accuracy:

• quick Follows a cray style error bound.
• accurate Follows a ieee style error bound.