Introduction

Aerobus is a C++-20 pure header library for general algebra on polynomials, discrete rings and associated structures.

Everything in Aerobus is expressed as types.

We say that again as it is the most fundamental characteristic of Aerobus :

Everything is expressed as types

The library serves two main purposes :

Express algebra structures and associated operations in type arithmetic, compile-time;
Provide portable and fast evaluation functions for polynomials.

It is designed to be 'quite easily' extensible.

Given these functions are "generated" at compile time and do not rely on inline assembly, they are actually platform independent, yielding exact same results if processors have same capabilities (such as Fused-Multiply-Add instructions).

HOW TO

Clone or download the repository somewhere, or just download aerobus.h
In your code, add : #include "aerobus.h"
Compile with -std=c++20 (at least) -I<install_location>

Aerobus provides a definition for low-degree (up to 997) Conway polynomials. To use them, define AEROBUS_CONWAY_IMPORTS before including aerobus.h.

Unit Test

Install Cmake Install a recent compiler (supporting c++20), such as MSVC, G++ or Clang++

Move to the top directory then :

cmake -S . -B build
cmake --build build
cd build && ctest

Terminal should write :

100% tests passed, 0 tests failed out of 48

Alternate way :

make tests

From top directory.

Benchmarks

Benchmarks are written for Intel CPUs having AVX512f and AVX512vl flags, they work only on Linux operating system using g++.

In addition of Cmake and compiler, install OpenMP. And Google's Benchmark library. Then move to top directory :

rm -rf build 
mkdir build
cd build
cmake ..
make benchmarks
./benchmarks

Structures

Predefined discrete euclidean domains

Aerobus predefines several simple euclidean domains, such as :

aerobus::i32 : integers (32 bits)
aerobus::i64 : integers (64 bits)
aerobus::zpz<p> : integers modulo p (prime number) on 32 bits

All these types represent the Ring, meaning the algebraic structure. They have a nested type val<i> where i is a scalar native value (int32_t or int64_t) to represent actual values in the ring. They have the following "operations", required by the IsEuclideanDomain concept :

add_t : a type (specialization of val), representing addition between two values
sub_t : a type (specialization of val), representing subtraction between two values
mul_t : a type (specialization of val), representing multiplication between two values
div_t : a type (specialization of val), representing division between two values
mod_t : a type (specialization of val), representing modulus between two values

and the following "elements" :

one : the neutral element for multiplication, val<1>
zero : the neutral element for addition, val<0>

Polynomials

Aerobus defines polynomials as a variadic template structure, with coefficient in an arbitrary discrete euclidean domain. As i32 or i64, they are given same operations and elements, which make them a euclidean domain by themselves. Similarly, aerobus::polynomial represents the algebraic structure, actual values are in aerobus::polynomial::val.

In addition, values have an evaluation function :

template<typename valueRing> static constexpr valueRing eval(const valueRing& x) {...}

Which can be used at compile time (constexpr evaluation) or runtime.

Known polynomials

Aerobus predefines some well known families of polynomials, such as Hermite or Bernstein :

using B23 = aerobus::known_polynomials::bernstein<2, 3>; // 3X^2(1-X)
constexpr float x = B32::eval(2.0F); // -12

They have their coefficients either in aerobus::i64 or aerobus::q64. Complete list is (but is meant to be extended):

chebyshev_T
chebyshev_U
laguerre
hermite_prob
hermite_phys
bernstein
legendre
bernoulli

Conway polynomials

When the tag AEROBUS_CONWAY_IMPORTS is defined at compile time (-DAEROBUS_CONWAY_IMPORTS), aerobus provides definition for all Conway polynomials CP(p, n) for p up to 997 and low values for n (usually less than 10).

They can be used to construct finite fields of order $p^n$ ($\mathbb{F}_{p^n}$):

using F2 = zpz<2>;
using PF2 = polynomial<F2>;
using F4 = Quotient<PF2, ConwayPolynomial<2, 2>::type>;

Taylor series

Aerobus provides definition for Taylor expansion of known functions. They are all templates in two parameters, degree of expansion (size_t) and Integers (typename). Coefficients then live in FractionField<Integers>.

They can be used and evaluated:

using namespace aerobus;
using aero_atanh = atanh<i64, 6>;
constexpr float val = aero_atanh::eval(0.1F); // approximation of arctanh(0.1) using taylor expansion of degree 6

Exposed functions are:

exp
expm1 $e^x - 1$
lnp1 $\ln(x+1)$
geom $\frac{1}{1-x}$
sin
cos
tan
sh
cosh
tanh
asin
acos
acosh
asinh
atanh

Having the capacity of specifying the degree is very important, as users may use other formats than float64 or float32 which require higher or lower degree to achieve correct or acceptable precision.

It's possible to define Taylor expansion by implementing a coeff_at structure which must meet the following requirement :

Being template in Integers (typename) and index (size_t);
Exposing a type alias type, some specialization of FractionField<Integers>::val.

For example, to define the serie $1+x+x^2+x^3+\ldots$, users may write:

template<typename Integers, size_t i>
struct my_coeff_at {
    using type = typename FractionField<Integers>::one;
};

template<typename Integers, size_t degree>
using my_serie = taylor<Integers, my_coeff_at, degree>;

static constexpr double x = my_serie<i64, 3>::eval(3.0);

On x86-64 and CUDA platforms at least, using proper compiler directives, these functions yield very performant assembly, similar or better than standard library implementation in fast math. For example, this code:

double compute_expm1(const size_t N, double* in, double* out) {
    using V = aerobus::expm1<aerobus::i64, 13>;
    for (size_t i = 0; i < N; ++i) {
        out[i] = V::eval(in[i]);
    }
}

Yields this assembly (clang 17, -mavx2 -O3) where we can see a pile of Fused-Multiply-Add vector instructions, generated because we unrolled completely the Horner evaluation loop:

compute_expm1(unsigned long, double const*, double*):
  lea     rax, [rdi-1]
  cmp     rax, 2
  jbe     .L5
  mov     rcx, rdi
  xor     eax, eax
  vxorpd  xmm1, xmm1, xmm1
  vbroadcastsd    ymm14, QWORD PTR .LC1[rip]
  vbroadcastsd    ymm13, QWORD PTR .LC3[rip]
  shr     rcx, 2
  vbroadcastsd    ymm12, QWORD PTR .LC5[rip]
  vbroadcastsd    ymm11, QWORD PTR .LC7[rip]
  sal     rcx, 5
  vbroadcastsd    ymm10, QWORD PTR .LC9[rip]
  vbroadcastsd    ymm9, QWORD PTR .LC11[rip]
  vbroadcastsd    ymm8, QWORD PTR .LC13[rip]
  vbroadcastsd    ymm7, QWORD PTR .LC15[rip]
  vbroadcastsd    ymm6, QWORD PTR .LC17[rip]
  vbroadcastsd    ymm5, QWORD PTR .LC19[rip]
  vbroadcastsd    ymm4, QWORD PTR .LC21[rip]
  vbroadcastsd    ymm3, QWORD PTR .LC23[rip]
  vbroadcastsd    ymm2, QWORD PTR .LC25[rip]
.L3:
  vmovupd ymm15, YMMWORD PTR [rsi+rax]
  vmovapd ymm0, ymm15
  vfmadd132pd     ymm0, ymm14, ymm1
  vfmadd132pd     ymm0, ymm13, ymm15
  vfmadd132pd     ymm0, ymm12, ymm15
  vfmadd132pd     ymm0, ymm11, ymm15
  vfmadd132pd     ymm0, ymm10, ymm15
  vfmadd132pd     ymm0, ymm9, ymm15
  vfmadd132pd     ymm0, ymm8, ymm15
  vfmadd132pd     ymm0, ymm7, ymm15
  vfmadd132pd     ymm0, ymm6, ymm15
  vfmadd132pd     ymm0, ymm5, ymm15
  vfmadd132pd     ymm0, ymm4, ymm15
  vfmadd132pd     ymm0, ymm3, ymm15
  vfmadd132pd     ymm0, ymm2, ymm15
  vfmadd132pd     ymm0, ymm1, ymm15
  vmovupd YMMWORD PTR [rdx+rax], ymm0
  add     rax, 32
  cmp     rcx, rax
  jne     .L3
  mov     rax, rdi
  and     rax, -4
  vzeroupper

Operations

Field of fractions

Given a set (type) satisfies the IsEuclideanDomain concept, Aerobus allows to define its field of fractions.

This new type is again a euclidean domain, especially a field, and therefore we can define polynomials over it.

For example, integers modulo p is not a field when p is not prime. We then can define its field of fraction and polynomials over it this way:

using namespace aerobus;
using ZmZ = zpz<8>;
using Fzmz = FractionField<ZmZ>;
using Pfzmz = polynomial<Fzmz>;

The same operation would stand for any set that users would have implemented in place of ZmZ.

For example, we can easily define rational functions by taking the ring of fractions of polynomials:

using namespace aerobus;
using RF64 = FractionField<polynomial<q64>>;

Which also have an evaluation function, as polynomial do.

Quotient

Given a ring R, Aerobus provides automatic implementation for quotient ring $R/X$ where X is a principal ideal generated by some element, as we know this kind of ideal is two-sided as long as R is commutative (and we assume it is).

For example, if we want R to be $\mathbb{Z}$ represented as aerobus::i64, we can express arithmetic modulo 17 using:

using namespace aerobus;
using ZpZ = Quotient<i64, i64::val<17>>;

As we could have using zpz<17>.

This is mainly used to define finite fields of order $p^n$ using Conway polynomials but may have other applications.

Misc

Continued Fractions

Aerobus gives an implementation for continued fractions. It can be used this way:

using namespace aerobus;
using T = ContinuedFraction<1,2,3,4>;
constexpr double x = T::val;

As practical examples, aerobus gives continued fractions of $\pi$, $e$, $\sqrt{2}$ and $\sqrt{3}$:

constexpr double A_SQRT3 = aerobus::SQRT3_fraction::val; // 1.7320508075688772935

CUDA

When compiled with nvcc and the flag WITH_CUDA_FP16, Aerobus provides some support of 16 bits integers and floats (aka __half).

Unfortunately, NVIDIA did not put enough constexpr in its cuda_fp16.h header, so we had to implement our own constexpr static_cast from int16_t to __half to make integers polynomials work with __half. See this bug.

More, it's (at this time), not easily possible to make it work for __half2 because of another bug.

A workaround is to modify cuda_fp16.h and add a constexpr modifier to line 5039. This works but only tested on Linux with CUDA 16.1.

Once done, nvcc generates splendid assembly, same as for double or float:

HFMA2.MMA R5, R6, RZ, 0.0013885498046875, 0.0013885498046875 ;    
HFMA2 R5, R6, R5, 0.008331298828125, 0.008331298828125 ;          
HFMA2.MMA R5, R6, R5, 0.041656494140625, 0.041656494140625 ;      
HFMA2 R5, R6, R5, 0.1666259765625, 0.1666259765625 ;              
HFMA2.MMA R5, R6, R5, 0.5, 0.5 ;                                  
HFMA2 R5, R6, R5, 1, 1 ;                                          
HFMA2.MMA R5, R6, R5, RZ ;                                        
HFMA2 R7, R5, RZ.H0_H0, 0.0013885498046875, 0.0013885498046875 ;  
HFMA2.MMA R7, R5, R7, 0.008331298828125, 0.008331298828125 ;      
HFMA2 R7, R5, R7, 0.041656494140625, 0.041656494140625 ;          
HFMA2.MMA R7, R5, R7, 0.1666259765625, 0.1666259765625 ;          
HFMA2 R7, R5, R7, 0.5, 0.5 ;                                      
HFMA2.MMA R7, R5, R7, 1, 1 ;                                      
HFMA2 R7, R5, R7, RZ.H0_H0 ;

Please push to make these bug fixed by NVIDIA.

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

README.md

README.md

Introduction

HOW TO

Unit Test

Benchmarks

Structures

Predefined discrete euclidean domains

Polynomials

Known polynomials

Conway polynomials

Taylor series

Operations

Field of fractions

Quotient

Misc

Continued Fractions

CUDA

Files

README.md

Latest commit

History

README.md

File metadata and controls

Introduction

HOW TO

Unit Test

Benchmarks

Structures

Predefined discrete euclidean domains

Polynomials

Known polynomials

Conway polynomials

Taylor series

Operations

Field of fractions

Quotient

Misc

Continued Fractions

CUDA