1. 23 Nov, 2017 2 commits
2. 20 Nov, 2017 3 commits
3. 19 Nov, 2017 1 commit
4. 18 Nov, 2017 2 commits
5. 05 Sep, 2017 1 commit
6. 29 Aug, 2017 1 commit
• templating test_scalapack · cc0bf8f5
Pavel Kus authored
```adding variants for single precision and complex math datatype
for the scalapack test```
7. 25 Aug, 2017 1 commit
• templating test_analytic · 15a6cbc3
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```
8. 10 Aug, 2017 3 commits
9. 03 Aug, 2017 2 commits
10. 25 Jul, 2017 2 commits
11. 19 Jul, 2017 1 commit
12. 18 Jul, 2017 3 commits
13. 17 Jul, 2017 1 commit
• Introducing analytical test · 8a9c9df1
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.```
14. 15 Jul, 2017 6 commits
15. 05 Jul, 2017 1 commit
• Also distribute necessary C header files · 530c08f0
Lorenz Huedepohl authored
```Pavel noted that some header files were missing from the distribution.
Also, I moved the private generated header files to src/ in order to
more clearly separate them.```
16. 30 Jun, 2017 3 commits
17. 29 Jun, 2017 3 commits
18. 28 Jun, 2017 1 commit
• Python plotting tool for displaying matrices introduced · 9ed892af
Pavel Kus authored
```Introducing python script plot.py containing several classes used to
plot matrices stored in block-cyclic distribution as a 1 global matrix.
Docstrings in plot.py should explain the ussage. There are also two
use cases included, both with commented scripts (using classes from
plot.py) and a screenshot. One of the use cases contains also tarball
with data.

Apart from that, a VERY simple module matrix_plot.F90 was created. It
is really simplistic, a more systematic approach towards loging should
be designed. At the moment, matrix output has to be triggered explicitly
in the code by calling a macro SAVE_MATR(event_description, iteration).```
19. 14 Jun, 2017 3 commits