SLEEF libm has three kinds of testers, and each kind of testers has its own role.
The first kind of testers consists of a tester and an IUT (which stands for Implementation Under Test.) The role for this tester is to perform a perfunctory set of tests to check if the build is correct. In this test, the functions in the library are tested if the evaluation error is within the designed limit by comparing the returned values against high-precision evaluation using the GNU MPFR Library. The tester and IUT are built as separate executables, and communicate with each other using a pipe. Since these two are separate, the IUT can be implemented with an exotic languages or on an operating system that does not support libraries required for testing. It is also possible to perform a test over the network.
The second kind of testers are designed to run continuously. It repeats randomly generating arguments for each function, and comparing the results of each function to the results calculated with the corresponding function in the MPFR library. This tester is expected to find bugs if it is run for sufficiently long time. In these tests, we especially carefully check the error of the trigonometric functions with arguments close to an integral multiple of π/2.
The third kind of testers are for testing if bit-identical results are returned from the functions that are supposed to return such results. The MD5 hash value of all returned values from each function is calculated and checked if it matches the precomputed value.
The fourth kind of testers have the same role as the first kind of testers, but uses TLFloat instead of MPFR. It allows for post-build testing in a wider range of environments than the first tester.
SLEEF DFT has three kinds of testers. The first ones, named naivetest, compare the results computed by SLEEF DFT with those by a naive DFT implementation. These testers cannot be built with MSVC since complex data types are not supported. The second testers, named fftwtest, compare the results of computation between SLEEF DFT and FFTW. This test requires FFTW library. The third testers, named roundtriptest, executes a forward transform followed by a backward transform. Then, it compares the results with the original data. While this test does not require external library and it runs on all environment, there could be cases where this test does not find some flaw. The roundtrip testers are used only if FFTW is not available.
Gencoef is a small tool for generating the coefficients for polynomial approximation used in the kernels.
In order to change the configurations, please edit gencoefdp.c. In the beginning of the file, specifications of the parameters for generating coefficients are listed. Please enable one of them by changing #if. Then, run make to compile the source code. Run the gencoef, and it will show the generated coefficients in a few minutes. It may take longer time depending on the settings.
There are two phases of the program. The first phase is the regression for minimizing the maximum relative error. This problem can be reduced to a linear programming problem, and the Simplex method is used in this implementation. This requires multi-precision calculation, and the implementation uses the MPFR library. In this phase, it uses only a small number of values (specified by the macro S, usually less than 100) within the input domain of the kernel function to approximate the function. The function to approximate is given by FRFUNC function. Specifying higher values for S does not always give better results.
The second phase is to optimize the coefficients so that it gives good accuracy with double precision calculation. In this phase, it checks 10000 points (specified by the macro Q) within the specified argument range to see if the polynomial gives good error bounds. In some cases, the last few terms have to be calculated in higher precision in order to achieve 1 ULP or better overall accuracy, and this implementation can take care of that. The L parameter specifies the number of high precision coefficients.
In some cases, it is desirable to fix the last few coefficients to values like 1 or 0.5. This can be specified if you define FIXCOEF0 macro.
Finding a set of good parameters is not a straightforward process.