 08 Jun, 2018 1 commit


Andreas Marek authored

 05 Jun, 2018 1 commit


Lorenz Huedepohl authored

 25 May, 2018 1 commit


Lorenz Huedepohl authored

 17 Apr, 2018 1 commit


Andreas Marek authored

 14 Mar, 2018 1 commit


Andreas Marek authored

 07 Mar, 2018 1 commit


Andreas Marek authored

 05 Mar, 2018 2 commits


Andreas Marek authored

Andreas Marek authored

 05 Feb, 2018 1 commit


Pavel Kus authored

 06 Dec, 2017 1 commit


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

 28 Nov, 2017 2 commits


Andreas Marek authored

Andreas Marek authored

 24 Nov, 2017 1 commit


Andreas Marek authored

 23 Nov, 2017 3 commits


Andreas Marek authored

Andreas Marek authored

Andreas Marek authored

 20 Nov, 2017 3 commits


Andreas Marek authored

Andreas Marek authored

Andreas Marek authored

 19 Nov, 2017 1 commit


Andreas Marek authored

 18 Nov, 2017 2 commits


Andreas Marek authored

Andreas Marek authored

 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)

 13 Oct, 2017 1 commit


Pavel Kus authored
matrix for the test not prepared (called with zero matrix) list of gitlab tests temporarily reduced

 05 Sep, 2017 1 commit


Andreas Marek authored

 29 Aug, 2017 1 commit


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

 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

 10 Aug, 2017 3 commits


Pavel Kus authored
yet another attempt to improve single/double and real/complex unification more readable

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

 03 Aug, 2017 2 commits


Andreas Marek authored

Lorenz Huedepohl authored
Anything if it makes Andreas happy :)

 25 Jul, 2017 2 commits


Lorenz Huedepohl authored

Lorenz Huedepohl authored
The new Makefile rule just dumped more and more lines into the test_x.sh script, as the ">>" operator was used always.

 19 Jul, 2017 1 commit


Andreas Marek authored
From now on call make check with TASKS=x, e.g. make check TASKS=2

 18 Jul, 2017 3 commits


Andreas Marek authored

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.

 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.
