MaxPlanck/shallow_water1
shallow water modelling, Max-Planck Inst. of Meteorology
| Name | shallow_water1 | 
| Group | MaxPlanck | 
| Matrix ID | 2261 | 
| Num Rows | 81,920 | 
| Num Cols | 81,920 | 
| Nonzeros | 327,680 | 
| Pattern Entries | 327,680 | 
| Kind | Computational Fluid Dynamics Problem | 
| Symmetric | Yes | 
| Date | 2009 | 
| Author | K. Leppkes, U. Naumann | 
| Editor | T. Davis | 
 
 
| Structural Rank | 81,920 | 
| Structural Rank Full | true | 
| Num Dmperm Blocks | 1 | 
| Strongly Connect Components | 1 | 
| Num Explicit Zeros | 0 | 
| Pattern Symmetry | 100% | 
| Numeric Symmetry | 100% | 
| Cholesky Candidate | yes | 
| Positive Definite | yes | 
| Type | real | 
 
 
| Download | MATLAB
Rutherford Boeing
Matrix Market | 
| Notes | 
The two shallow_water* matrices arise from weather shallow water equations   
(see http://www.icon.enes.org), from the Max-Plank Institute of Meteorology. 
The problem gives rise to an automatic differentiation problem.  An iterative
solver is used for the larger problem, but a direct sovler is used for       
finding the adjoints of a linear problem.  The velocity field is integrated  
over a time loop with a semi-implicit method.  The implicit part leads to    
a linear problem A*x=b, whose entries vary with time.  Two of these matrices 
A are in this collection, with shallow_water1 at dtime=100 and shallow_water2
at dtime=200.  The nonzero patterns of the two matrices are the same, but    
shallow_water1 is much slower.  The reason is that many denormals appear     
during factorization, which greatly slows down the BLAS.  This can be solved 
by compiling with gcc -ffast-math, to flush denormals to zero. |