- 05 Jun, 2018 1 commit
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Lorenz Huedepohl authored
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- 25 May, 2018 1 commit
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Lorenz Huedepohl authored
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- 17 Apr, 2018 1 commit
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Andreas Marek authored
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- 14 Mar, 2018 1 commit
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Andreas Marek authored
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- 07 Mar, 2018 1 commit
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Andreas Marek authored
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- 05 Mar, 2018 2 commits
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Andreas Marek authored
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Andreas Marek authored
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- 05 Feb, 2018 1 commit
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Pavel Kus authored
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- 06 Dec, 2017 1 commit
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Andreas Marek authored
- per default only some tests are run - by setting CHECK_LEVEL=extended all test jobs are run
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- 28 Nov, 2017 2 commits
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Andreas Marek authored
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Andreas Marek authored
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- 24 Nov, 2017 1 commit
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Andreas Marek authored
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- 23 Nov, 2017 3 commits
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Andreas Marek authored
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Andreas Marek authored
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Andreas Marek authored
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- 20 Nov, 2017 3 commits
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Andreas Marek authored
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Andreas Marek authored
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Andreas Marek authored
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- 19 Nov, 2017 1 commit
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Andreas Marek authored
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- 18 Nov, 2017 2 commits
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Andreas Marek authored
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Andreas Marek authored
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- 30 Oct, 2017 1 commit
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Pavel Kus authored
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- 26 Oct, 2017 1 commit
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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 de-allocate the autotune object call e%autotune_set_best(tune_state) call elpa_autotune_deallocate(tune_state)
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- 13 Oct, 2017 1 commit
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Pavel Kus authored
matrix for the test not prepared (called with zero matrix) list of gitlab tests temporarily reduced
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- 05 Sep, 2017 1 commit
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Andreas Marek authored
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- 29 Aug, 2017 1 commit
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Pavel Kus authored
adding variants for single precision and complex math datatype for the scalapack test
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- 25 Aug, 2017 1 commit
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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
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- 10 Aug, 2017 3 commits
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Pavel Kus authored
yet another attempt to improve single/double and real/complex unification more readable
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Lorenz Huedepohl authored
with obvious meaning
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Pavel Kus authored
for easier comparisons of elpa and mkl, a test case using scalapack function pdsyevd has been added
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- 03 Aug, 2017 2 commits
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Andreas Marek authored
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Lorenz Huedepohl authored
Anything if it makes Andreas happy :)
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- 25 Jul, 2017 2 commits
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Lorenz Huedepohl authored
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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.
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- 19 Jul, 2017 1 commit
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Andreas Marek authored
From now on call make check with TASKS=x, e.g. make check TASKS=2
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- 18 Jul, 2017 3 commits
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Andreas Marek authored
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Andreas Marek authored
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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.
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- 17 Jul, 2017 1 commit
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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.
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- 15 Jul, 2017 1 commit
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Andreas Marek authored
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