- Introduction
- Compiling and installing the library
- Math library reference
- Quad-precision math library reference
- DFT library reference
- Other tools included in the package
- Benchmark results
- Additional notes
- Frequently asked questions
- Vectorizing calls to scalar functions
- About the GNUABI version of the library
- Using link time optimization
- Using header files of inlinable functions
- Utilizing SLEEF for WebAssembly
- How the dispatcher works
- About libsleefscalar
- ULP, gradual underflow and flush-to-zero mode
- Explanatory source code for the modified Payne Hanek reduction method
- About the logo

**Q1:** Is the scalar functions in SLEEF faster than the
corresponding functions in the standard C library?

**A1:** No. Todays standard C libraries are very well optimized,
and there is small room for further optimization. The reason why
SLEEF is fast is that it computes directly with SIMD registers and
ALUs. This is not simple as it sounds, because conditional branches
have to be eliminated in order to take full advantage of SIMD
computation. If the algorithm requires conditional branches
according to the argument, it must prepare for the cases where the
elements in the input vector contain both values that would make a
branch happen and not happen. This would spoil the advantage of SIMD
computation, because each element in a vector would require a
different code path.

**Q2:** Do the trigonometric functions (e.g. sin) in SLEEF return
correct values for the whole range of inputs?

**A2:** Yes. SLEEF does implement a vectorized version of Payne Hanek range
reduction, and all the trigonometric functions return a correct
value with the specified accuracy.

**Q3:** What can I do to make sleef run faster?

**A3:** The most important thing is to choose the fastest
available vector extension. SLEEF is optimized for computers with
FMA
instructions, and it runs slow on Ivy Bridge or older CPUs and Atom,
that do not have FMA instructions. If you are not sure, use the
dispatcher. The dispatcher in
SLEEF is not slow. If you want to further speed up computation,
try using LTO. By using LTO, the
compiler fuses the code within the library to the code calling the
library functions, and this sometimes results in considerable
performance boost. In this case, you should not use the dispatcher,
and you should use the same compiler with the same version to build
SLEEF and the program against which SLEEF is linked.

Recent x86_64 gcc can auto-vectorize
calls to functions. In order to utilize this functionality, OpenMP
SIMD pragmas can be added to declarations of scalar functions
like **Sleef_sin_u10** by defining
**SLEEF_ENABLE_OMP_SIMD** macro before including **sleef.h** on
x86_64 computers. With these pragmas, gcc can use its auto-vectorizer
to vectorize calls to these scalar functions. For
example, the following
code can be vectorized by gcc-10.

`#include <stdio.h>`

`#define SLEEF_ENABLE_OMP_SIMD`

`#include "sleef.h"`

`#define N 65536`

`#define M (N + 3)`

`static double func(double x) { return Sleef_pow_u10(x, -x); }`

`double int_simpson(double a, double b) {`

`double h = (b - a) / M;`

`double sum_odd = 0.0, sum_even = 0.0;`

`for(int i = 1;i <= M-3;i += 2) {`

`sum_odd += func(a + h * i);`

`sum_even += func(a + h * (i + 1));`

`}`

`return h / 3 * (func(a) + 4 * sum_odd + 2 * sum_even + 4 * func(b - h) + func(b));`

`}`

`int main() {`

`double sum = 0;`

`for(int i=1;i<N;i++) sum += Sleef_pow_u10(i, -i);`

`printf("%g %g\n", int_simpson(0, 1), sum);`

`}`

$ gcc-10 -fopenmp -ffast-math -mavx2 -O3 sophomore.c -lsleef -S -o- | grep _ZGV call _ZGVdN4vv_Sleef_pow_u10@PLT call _ZGVdN4vv_Sleef_pow_u10@PLT call _ZGVdN4vv_Sleef_pow_u10@PLT call _ZGVdN4vv_Sleef_pow_u10@PLT call _ZGVdN4vv_Sleef_pow_u10@PLT call _ZGVdN4vv_Sleef_pow_u10@PLT call _ZGVdN4vv_Sleef_pow_u10@PLT $ █

The GNUABI version of the library (libsleefgnuabi.so) is built for x86 and aarch64 architectectures. This library provides an API compatible with libmvec in glibc, and the API comforms to the x86 vector ABI, AArch64 vector ABI and Power Vector ABI. The auto-vectorizer in x86_64 gcc is capable of vectorizing calls to the standard math functions and generates calls to libmvec. The GNUABI version of SLEEF library can be used as a substitute for libmvec.

`#include <stdio.h>`

`#include <math.h>`

`#define N 65536`

`#define M (N + 3)`

`static double func(double x) { return pow(x, -x); }`

`double int_simpson(double a, double b) {`

`double h = (b - a) / M;`

`double sum_odd = 0.0, sum_even = 0.0;`

`for(int i = 1;i <= M-3;i += 2) {`

`sum_odd += func(a + h * i);`

`sum_even += func(a + h * (i + 1));`

`}`

`return h / 3 * (func(a) + 4 * sum_odd + 2 * sum_even + 4 * func(b - h) + func(b));`

`}`

`int main() {`

`double sum = 0;`

`for(int i=1;i<N;i++) sum += pow(i, -i);`

`printf("%g %g\n", int_simpson(0, 1), sum);`

`}`

For example, the above
code can be linked against libsleefgnuabi as shown below. You
have to specify **-lsleefgnuabi** compiler option
before **-lm** option.

$ gcc-10 -ffast-math -O3 sophomore2.c -lsleefgnuabi -lm -L./lib $ ldd a.out linux-vdso.so.1 (0x00007ffd0c5ff000) libsleefgnuabi.so.3 => not found libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007f73f5f98000) libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007f73f5da6000) /lib64/ld-linux-x86-64.so.2 (0x00007f73f60fb000) $ LD_LIBRARY_PATH=./lib ./a.out 1.29127 1.29129 $ █

Link
time optimization (LTO) is a functionality implemented in gcc,
clang and other compilers for optimizing across translation units (or
source files.) This can sometimes dramatically improve the
performance of the code, because it is capable of fusing library
functions into the code calling those functions. The build system in
SLEEF supports LTO and thus it can be built with LTO support by just
specifying **-DSLEEF_ENABLE_LTO=TRUE** cmake option. However, there are a
few things to note in order to get the optimal performance. 1. You
should not use the dispatcher with LTO. Dispatchers prevent the
functions from being fused with LTO. 2. You have to use the same
compiler with the same version to build the library and your
code. 3. You cannot build shared libraries with LTO.

Although LTO is considered to be a smart technique for improving the
performance of the library functions, there are difficulties in using
this functionality in real situations. One of the reasons is that
people still need to use old compilers to build their projects. SLEEF
can generate header files in which the library functions are all
defined as inline functions. This can be compiled with old compilers.
In theory, inline functions should give similar performance to LTO,
but in reality, inline functions are better. In order to generate
those header files, specify **-DSLEEF_BUILD_INLINE_HEADERS=TRUE** cmake
option. Below is an example code utilizing the generated header files
for SSE2 and AVX2. You cannot use a dispatcher with these header
files.

`#include <stdio.h>`

`#include <stdint.h>`

`#include <string.h>`

`#include <x86intrin.h>`

`#include <sleefinline_sse2.h>`

`#include <sleefinline_avx2128.h>`

`int main(int argc, char **argv) {`

`__m128d va = _mm_set_pd(2, 10);`

`__m128d vb = _mm_set_pd(3, 20);`

`__m128d vc = Sleef_powd2_u10sse2(va, vb);`

`double c[2];`

`_mm_storeu_pd(c, vc);`

`printf("%g, %gn", c[0], c[1]);`

`__m128d vd = Sleef_powd2_u10avx2128(va, vb);`

`double d[2];`

`_mm_storeu_pd(d, vd);`

`printf("%g, %gn", d[0], d[1]);`

`}`

$ gcc-10 -ffp-contract=off -O3 -march=native helloinline.c -I./include $ ./a.out 1e+20, 8 1e+20, 8 $ nm -g a.out 00000000000036a0 R Sleef_rempitabdp 0000000000003020 R Sleef_rempitabsp 0000000000003000 R _IO_stdin_used w _ITM_deregisterTMCloneTable w _ITM_registerTMCloneTable 000000000000d010 D __TMC_END__ 000000000000d010 B __bss_start w __cxa_finalize@@GLIBC_2.2.5 000000000000d000 D __data_start 000000000000d008 D __dso_handle w __gmon_start__ 00000000000020a0 T __libc_csu_fini 0000000000002030 T __libc_csu_init U __libc_start_main@@GLIBC_2.2.5 U __printf_chk@@GLIBC_2.3.4 000000000000d010 D _edata 000000000000d018 B _end 00000000000020a8 T _fini 0000000000001f40 T _start 000000000000d000 W data_start 0000000000001060 T main $ █

Since Emscripten supports SSE2 intrinsics, the SSE2 inlinable function header can be used for WebAssembly.

`#include <stdio.h>`

`#include <emmintrin.h>`

`#include "sleefinline_sse2.h"`

`int main(int argc, char **argv) {`

`double a[] = {2, 10};`

`double b[] = {3, 20};`

`__m128d va, vb, vc;`

`va = _mm_loadu_pd(a);`

`vb = _mm_loadu_pd(b);`

`vc = Sleef_powd2_u10sse2(va, vb);`

`double c[2];`

`_mm_storeu_pd(c, vc);`

`printf("pow(%g, %g) = %gn", a[0], b[0], c[0]);`

`printf("pow(%g, %g) = %gn", a[1], b[1], c[1]);`

`}`

$ emcc -O3 -msimd128 -msse2 hellowasm.c $ ../node-v15.7.0-linux-x64/bin/node --experimental-wasm-simd ./a.out.js pow(2, 3) = 8 pow(10, 20) = 1e+20 $ █

SLEEF implements versions of functions that are implemented with each vector extension of the architecture. A dispatcher is a function that dynamically selects the fastest implementatation for the computer it runs. The dispatchers in SLEEF are designed to have very low overhead.

Fig. 7.1 shows a simplified code of our dispatcher. There is only
one exported function **mainFunc**. When
**mainFunc** is called for the first
time, **dispatcherMain** is called internally,
since *funcPtr* is initialized to the pointer to
**dispatcherMain** (line 14). It then detects if the
CPU supports SSE 4.1 (line 7), and
rewrites *funcPtr* to a pointer to the function
that utilizes SSE 4.1 or SSE 2, depending on the result of CPU
feature detection (line 10). When
**mainFunc** is called for the second time, it does
not execute the
**dispatcherMain**. It just executes the function
pointed by the pointer stored in *funcPtr* during
the execution of
**dispatcherMain**.

There are advantages in our dispatcher. The first advantage is that it does not require any compiler-specific extension. The second advantage is simplicity. There are only 18 lines of simple code. Since the dispatchers are completely separated for each function, there is not much room for bugs to get in.

The third advantage is low overhead. You might think that the overhead is one function call including execution of the prologue and the epilogue. However, modern compilers are smart enough to eliminate redundant execution of the prologue, epilogue and return instruction. The actual overhead is just one jmp instruction, which has very small overhead since it is not conditional. This overhead is likely hidden by out-of-order execution.

The fourth advantage is thread safety. There is only one variable
shared among threads, which is *funcPtr*. There are
only two possible values for this pointer variable. The first value
is the pointer to the **dispatcherMain**, and the
second value is the pointer to either **funcSSE2**
or **funcSSE4**, depending on the availability of
extensions. Once *funcPtr* is substituted with the
pointer to **funcSSE2**
or **funcSSE4**, it will not be changed in the
future. It should be easy to confirm that the code works in all the
cases.

`static double (*funcPtr)(double arg);`

`static double dispatcherMain(double arg) {`

`double (*p)(double arg) = funcSSE2;`

`#if the compiler supports SSE4.1`

`if (SSE4.1 is available on the CPU) p = funcSSE4;`

`#endif`

`funcPtr = p;`

`return (*funcPtr)(arg);`

`}`

`static double (*funcPtr)(double arg) = dispatcherMain;`

`double mainFunc(double arg) {`

`return (*funcPtr)(arg);`

`}`

Fig. 7.1: Simplified code of our dispatcher

The scalar functions like **Sleef_sin_u10** were the
functions implemented in sleefdp.c and sleefsp.c. These functions are
provided to make it easier to understand how each sleef function
works. These functions are now moved to **libsleefscalar**, because
they run slower than the scalar functions implemented in sleefsimddp.c
and sleefsimdsp.c. As of version 3.6, the scalar functions whose names
do not end with **purec** or **purecfma** are dispatchers that
choose from the scalar functions whose names end with **purec**
or **purecfma**. For example, **Sleef_sin_u10**
in **libsleef** is now a dispatcher that chooses
from **Sleef_sind1_u10purec**
and **Sleef_sind1_u10purecfma**.

ULP stands for "unit in the last place", which is sometimes used for representing accuracy of calculation. 1 ULP is the distance between the two closest floating point number, which depends on the exponent of the FP number. The accuracy of calculation by reputable math libraries is usually between 0.5 and 1 ULP. Here, the accuracy means the largest error of calculation. SLEEF math library provides multiple accuracy choices for most of the math functions. Many functions have 3.5-ULP and 1-ULP versions, and 3.5-ULP versions are faster than 1-ULP versions. If you care more about execution speed than accuracy, it is advised to use the 3.5-ULP versions along with -ffast-math or "unsafe math optimization" options for the compiler.

Note that 3.5 ULPs of error is small enough in many applications. If you do not manage the error of computation by carefully ordering floating point operations in your code, you would easily have that amount of error in the computation results.

In IEEE 754 standard, underflow does not happen abruptly when the exponent becomes zero. Instead, when a number to be represented is smaller than a certain value, a denormal number is produced which has less precision. This is sometimes called gradual underflow. On some processor implementation, a flush-to-zero mode is used since it is easier to implement by hardware. In flush-to-zero mode, numbers smaller than the smallest normalized number are replaced with zero. FP operations are not IEEE-754 conformant if a flush-to-zero mode is used. A flush-to-zero mode influences the accuracy of calculation in some cases. The smallest normalized precision number can be referred with DBL_MIN for double precision, and FLT_MIN for single precision. The naming of these macros is a little bit confusing because DBL_MIN is not the smallest double precision number.

You can see known maximum errors in math functions in glibc at this page.

In order to evaluate a trigonometric function with a large argument,
an argument reduction method is used to find an FP remainder of
dividing the argument *x* by π. We devised a
variation of the Payne-Hanek argument reduction method which is
suitable for vector computation. Fig. 7.2
shows an explanatory source
code for this method. See our paper for
the details.

`#include <stdio.h>`

`#include <stdlib.h>`

`#include <math.h>`

`#include <mpfr.h>`

`typedef struct { double x, y; } double2;`

`double2 dd(double d) { double2 r = { d, 0 }; return r; }`

`int64_t d2i(double d) { union { double f; int64_t i; } tmp = {.f = d }; return tmp.i; }`

`double i2d(int64_t i) { union { double f; int64_t i; } tmp = {.i = i }; return tmp.f; }`

`double upper(double d) { return i2d(d2i(d) & 0xfffffffff8000000LL); }`

`double clearlsb(double d) { return i2d(d2i(d) & 0xfffffffffffffffeLL); }`

`double2 ddrenormalize(double2 t) {`

`double2 s = dd(t.x + t.y);`

`s.y = t.x - s.x + t.y;`

`return s;`

`}`

`double2 ddadd(double2 x, double2 y) {`

`double2 r = dd(x.x + y.x);`

`double v = r.x - x.x;`

`r.y = (x.x - (r.x - v)) + (y.x - v) + (x.y + y.y);`

`return r;`

`}`

`double2 ddmul(double x, double y) {`

`double2 r = dd(x * y);`

`r.y = fma(x, y, -r.x);`

`return r;`

`}`

`double2 ddmul2(double2 x, double2 y) {`

`double2 r = ddmul(x.x, y.x);`

`r.y += x.x * y.y + x.y * y.x;`

`return r;`

`}`

`// This function computes remainder(a, PI/2)`

`double2 modifiedPayneHanek(double a) {`

`double table[4];`

`int scale = fabs(a) > 1e+200 ? -128 : 0;`

`a = ldexp(a, scale);`

`// Table genration`

`mpfr_set_default_prec(2048);`

`mpfr_t pi, m;`

`mpfr_inits(pi, m, NULL);`

`mpfr_const_pi(pi, GMP_RNDN);`

`mpfr_d_div(m, 2, pi, GMP_RNDN);`

`mpfr_set_exp(m, mpfr_get_exp(m) + (ilogb(a) - 53 - scale));`

`mpfr_frac(m, m, GMP_RNDN);`

`mpfr_set_exp(m, mpfr_get_exp(m) - (ilogb(a) - 53));`

`for(int i=0;i<4;i++) {`

`table[i] = clearlsb(mpfr_get_d(m, GMP_RNDN));`

`mpfr_sub_d(m, m, table[i], GMP_RNDN);`

`}`

`mpfr_clears(pi, m, NULL);`

`// Main computation`

`double2 x = dd(0);`

`for(int i=0;i<4;i++) {`

`x = ddadd(x, ddmul(a, table[i]));`

`x.x = x.x - round(x.x);`

`x = ddrenormalize(x);`

`}`

`double2 pio2 = { 3.141592653589793*0.5, 1.2246467991473532e-16*0.5 };`

`x = ddmul2(x, pio2);`

`return fabs(a) < 0.785398163397448279 ? dd(a) : x;`

`}`

Fig. 7.2: Explanatory source code for our modified Payne Hanek reduction method

It is a soup ladle. A sleef means a soup ladle in Dutch.