xref: /petsc/src/benchmarks/results/lap2d.m (revision 56fec9231109c070b7f39c4a5b71e46affc607fc)
1%
2%  Performance results for PETSc on the 2d Laplacian 5 pt stencil using GMRES(30) and no
3% preconditioning. Run to give an indication of scalability for
4%
5%  * matrix vector product for very sparse matrix, and
6%  * scalability of inner products and vector updates.
7%
8% NOTE: one would NEVER use these particular solvers in practice.
9%
10% --------------------------------------------------------------------------
11% For a 1000 by 1000 grid, with -ksp_max_it 500 -mat_aij_no_inode
12%
13p1000 = [ 2 4 8 16 32];
14%
15%  m and s contain the times for matrix vector multiply and SLES solve for
16%  IBM SP2, Cray T3E and Origin2000 in that order.
17%
18m1000 = [ 95.54 51.87 25.74 13.63 6.88; 134.6 67.59 33.93 16.77 9.03];
19s1000 = [ 455.5 245.2 123.9 72.41 38.63; 432.3 220 111.4 56.24 30.16];
20%
21% --------------------------------------------------------------------------
22% For a 100 by 100 grid, with -mat_aij_no_inode
23%
24p100 = [ 1 2 3 4 5 6 7 8];
25%
26%  m and s contain the times for matrix vector multiply and SLES solve for
27%  IBM SP2, Cray T3E and Origin2000 in that order.
28%
29m100 = [ 1.602 1.116 .793 .638 .5297 .4633 .4181 .5808; 2.233 1.434 1.064 .8116 .6748 .6095 \
30         .5338 .4873];
31s100 = [ 9.425 6.036 4.32 3.334 3.634 3.656 3.657 4.248; 9.566 5.704 4.260 3.080 3.000 2.696
32         2.614 2.6061];
33%
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