 05 Feb, 2018 2 commits
 29 Jan, 2018 1 commit


Pavel Kus authored
executables were generated event though the functionality is not present in test.c

 18 Jan, 2018 1 commit


Andreas Marek authored

 08 Dec, 2017 1 commit


Andreas Marek authored

 07 Dec, 2017 2 commits


Andreas Marek authored
test.c currently does not support all the flags test.F90 does, so there were a lot of useless test cases generated.

Lorenz Huedepohl authored

 06 Dec, 2017 2 commits


Andreas Marek authored

Andreas Marek authored
 per default only some tests are run  by setting CHECK_LEVEL=extended all test jobs are run

 05 Dec, 2017 2 commits


Andreas Marek authored

Lorenz Huedepohl authored

 27 Nov, 2017 1 commit


Andreas Marek authored

 25 Nov, 2017 2 commits


Andreas Marek authored

Andreas Marek authored

 24 Nov, 2017 1 commit


Andreas Marek authored

 07 Nov, 2017 2 commits
 30 Oct, 2017 1 commit


Pavel Kus authored

 26 Oct, 2017 1 commit


Lorenz Huedepohl authored
To be used like this class(elpa_t), pointer :: e class(elpa_autotune_t), pointer :: tune_state e => elpa_allocate() call e%set(...) [...] assert_elpa_ok(e%setup()) tune_state => e%autotune_setup(ELPA_AUTOTUNE_FAST, ELPA_AUTOTUNE_DOMAIN_REAL) ! Autotuning loop, continues until all combinations have been tried do while (e%autotune_step(tune_state)) ! Do the steps that are representative of your calculation call e%eigenvectors(a, ev, z, error) end do ! Fix best parameters, and deallocate the autotune object call e%autotune_set_best(tune_state) call elpa_autotune_deallocate(tune_state)

 16 Oct, 2017 1 commit


Pavel Kus authored
both fortran and C tests generated in generate_automake_test_programs.py linker flags added by hand  FIX this

 03 Sep, 2017 1 commit


Pavel Kus authored

 01 Sep, 2017 1 commit


Pavel Kus authored

 29 Aug, 2017 1 commit


Pavel Kus authored
adding variants for single precision and complex math datatype for the scalapack test

 26 Aug, 2017 1 commit


Pavel Kus authored
taking into account, that computed eigenvectors can be arbitrarily "rotated" by multiplying by complex number. Hopefully dealt with in a robust way. Enabling analytic complex double and single

 25 Aug, 2017 1 commit


Pavel Kus authored
test analytic has been transformed to template to allow all real/complex and single/double variants. However, at this commit, only real double and real single variants are enabled

 24 Aug, 2017 1 commit


Andreas Marek authored

 21 Aug, 2017 1 commit


Andreas Marek authored

 18 Aug, 2017 2 commits


Andreas Marek authored

Andreas Marek authored

 17 Aug, 2017 3 commits


Andreas Marek authored

Andreas Marek authored

Andreas Marek authored

 10 Aug, 2017 2 commits


Lorenz Huedepohl authored
with obvious meaning

Pavel Kus authored
for easier comparisons of elpa and mkl, a test case using scalapack function pdsyevd has been added

 31 Jul, 2017 1 commit


Lorenz Huedepohl authored

 30 Jul, 2017 1 commit


Lorenz Huedepohl authored
We got reports from a user that there were troubles with certain domain decompositions. So far the tests only looked at (approximately) square decompositions in columnmajor process order. Now, a new class of tests loops over all possible decompositions (row * col) for a given number of total tasks. So far, we can not confirm that there are any problems, all possibilities work as expected.

 18 Jul, 2017 3 commits


Andreas Marek authored

Lorenz Huedepohl authored
The module is intended to be hidden, thus I moved it into the public part of the legacy API. Some test programs use it, thus the test programs have now also access to ELPA's private modules. This is of course anyway necessary to test ELPA internals that are not exposed via the public API.

Andreas Marek authored

 17 Jul, 2017 1 commit


Pavel Kus authored
Introducing new test in which matrix and its eigendecomposition is known and thus can be easily created and checked directly, without the need to use scalapack or any other communication (apart from reducing error). The test is based on the fact, that if L_A and S_A are eigenvalues and eigenvectors of matrix A, respectively, and L_B and S_B eigenvalues and eigenvectors of B, then kron(L_A, L_B) and kron (S_A, S_B) are eigenvalues and eigenvectors of kron(A, B). Since it is easy to know exact eigendecomposition of a small matrix (e.g. 2x2), and kron operator has very simple structure, we can construct arbitrarily large matrix and its eigendecomposition. We only have to select small matrices such that the resulting matrix has unique and ordered eigenvalues, so that the checking of the result is than easy. Each element of matrix, eigenvector matrix and eigenvalue vector can be quickly computed independently, just using its global coordinates. The test is currently limited to matrices of size 2^n, but by storing eigendecompositions of more small matrices (e.g. 3x3 and 5x5) we could construct any matrix of size 2^n*3^m*5^o, which would probably be sufficient, since most often used sizes (150, 1000, 5000, 2000, 60000) are of this form.
