superlu_dist options
Fredrik Bengzon
fredrik.bengzon at math.umu.se
Fri May 8 10:59:55 CDT 2009
Hi,
Here is the output from the KSP and EPS objects, and the log summary.
/ Fredrik
Reading Triangle/Tetgen mesh
#nodes=19345
#elements=81895
#nodes per element=4
Partitioning mesh with METIS 4.0
Element distribution (rank | #elements)
0 | 19771
1 | 20954
2 | 20611
3 | 20559
rank 1 has 257 ghost nodes
rank 0 has 127 ghost nodes
rank 2 has 143 ghost nodes
rank 3 has 270 ghost nodes
Calling 3D Navier-Lame Eigenvalue Solver
Assembling stiffness and mass matrix
Solving eigensystem with SLEPc
KSP Object:(st_)
type: preonly
maximum iterations=100000, initial guess is zero
tolerances: relative=1e-08, absolute=1e-50, divergence=10000
left preconditioning
PC Object:(st_)
type: lu
LU: out-of-place factorization
matrix ordering: natural
LU: tolerance for zero pivot 1e-12
EPS Object:
problem type: generalized symmetric eigenvalue problem
method: krylovschur
extraction type: Rayleigh-Ritz
selected portion of the spectrum: largest eigenvalues in magnitude
number of eigenvalues (nev): 4
number of column vectors (ncv): 19
maximum dimension of projected problem (mpd): 19
maximum number of iterations: 6108
tolerance: 1e-05
dimension of user-provided deflation space: 0
IP Object:
orthogonalization method: classical Gram-Schmidt
orthogonalization refinement: if needed (eta: 0.707100)
ST Object:
type: sinvert
shift: 0
Matrices A and B have same nonzero pattern
Associated KSP object
------------------------------
KSP Object:(st_)
type: preonly
maximum iterations=100000, initial guess is zero
tolerances: relative=1e-08, absolute=1e-50, divergence=10000
left preconditioning
PC Object:(st_)
type: lu
LU: out-of-place factorization
matrix ordering: natural
LU: tolerance for zero pivot 1e-12
LU: factor fill ratio needed 0
Factored matrix follows
Matrix Object:
type=mpiaij, rows=58035, cols=58035
package used to perform factorization: superlu_dist
total: nonzeros=0, allocated nonzeros=116070
SuperLU_DIST run parameters:
Process grid nprow 2 x npcol 2
Equilibrate matrix TRUE
Matrix input mode 1
Replace tiny pivots TRUE
Use iterative refinement FALSE
Processors in row 2 col partition 2
Row permutation LargeDiag
Column permutation PARMETIS
Parallel symbolic factorization TRUE
Repeated factorization SamePattern
linear system matrix = precond matrix:
Matrix Object:
type=mpiaij, rows=58035, cols=58035
total: nonzeros=2223621, allocated nonzeros=2233584
using I-node (on process 0) routines: found 4695 nodes,
limit used is 5
------------------------------
Number of iterations in the eigensolver: 1
Number of requested eigenvalues: 4
Stopping condition: tol=1e-05, maxit=6108
Number of converged eigenpairs: 8
Writing binary .vtu file /scratch/fredrik/output/mode-0.vtu
Writing binary .vtu file /scratch/fredrik/output/mode-1.vtu
Writing binary .vtu file /scratch/fredrik/output/mode-2.vtu
Writing binary .vtu file /scratch/fredrik/output/mode-3.vtu
Writing binary .vtu file /scratch/fredrik/output/mode-4.vtu
Writing binary .vtu file /scratch/fredrik/output/mode-5.vtu
Writing binary .vtu file /scratch/fredrik/output/mode-6.vtu
Writing binary .vtu file /scratch/fredrik/output/mode-7.vtu
************************************************************************************************************************
*** WIDEN YOUR WINDOW TO 120 CHARACTERS. Use 'enscript -r
-fCourier9' to print this document ***
************************************************************************************************************************
---------------------------------------------- PETSc Performance
Summary: ----------------------------------------------
/home/fredrik/Hakan/cmlfet/a.out on a linux-gnu named medusa1 with 4
processors, by fredrik Fri May 8 17:57:28 2009
Using Petsc Release Version 3.0.0, Patch 5, Mon Apr 13 09:15:37 CDT 2009
Max Max/Min Avg Total
Time (sec): 5.429e+02 1.00001 5.429e+02
Objects: 1.380e+02 1.00000 1.380e+02
Flops: 1.053e+08 1.05695 1.028e+08 4.114e+08
Flops/sec: 1.939e+05 1.05696 1.894e+05 7.577e+05
Memory: 5.927e+07 1.03224 2.339e+08
MPI Messages: 2.880e+02 1.51579 2.535e+02 1.014e+03
MPI Message Lengths: 4.868e+07 1.08170 1.827e+05 1.853e+08
MPI Reductions: 1.122e+02 1.00000
Flop counting convention: 1 flop = 1 real number operation of type
(multiply/divide/add/subtract)
e.g., VecAXPY() for real vectors of length N
--> 2N flops
and VecAXPY() for complex vectors of length
N --> 8N flops
Summary of Stages: ----- Time ------ ----- Flops ----- --- Messages
--- -- Message Lengths -- -- Reductions --
Avg %Total Avg %Total counts
%Total Avg %Total counts %Total
0: Main Stage: 5.4292e+02 100.0% 4.1136e+08 100.0% 1.014e+03
100.0% 1.827e+05 100.0% 3.600e+02 80.2%
------------------------------------------------------------------------------------------------------------------------
See the 'Profiling' chapter of the users' manual for details on
interpreting output.
Phase summary info:
Count: number of times phase was executed
Time and Flops: Max - maximum over all processors
Ratio - ratio of maximum to minimum over all processors
Mess: number of messages sent
Avg. len: average message length
Reduct: number of global reductions
Global: entire computation
Stage: stages of a computation. Set stages with PetscLogStagePush()
and PetscLogStagePop().
%T - percent time in this phase %F - percent flops in this
phase
%M - percent messages in this phase %L - percent message
lengths in this phase
%R - percent reductions in this phase
Total Mflop/s: 10e-6 * (sum of flops over all processors)/(max time
over all processors)
------------------------------------------------------------------------------------------------------------------------
##########################################################
# #
# WARNING!!! #
# #
# This code was compiled with a debugging option, #
# To get timing results run config/configure.py #
# using --with-debugging=no, the performance will #
# be generally two or three times faster. #
# #
##########################################################
Event Count Time (sec)
Flops --- Global --- --- Stage --- Total
Max Ratio Max Ratio Max Ratio Mess Avg len
Reduct %T %F %M %L %R %T %F %M %L %R Mflop/s
------------------------------------------------------------------------------------------------------------------------
--- Event Stage 0: Main Stage
STSetUp 1 1.0 1.0467e+01 1.0 0.00e+00 0.0 0.0e+00 0.0e+00
8.0e+00 2 0 0 0 2 2 0 0 0 2 0
STApply 28 1.0 5.1775e+02 1.0 3.15e+07 1.1 1.7e+02 4.2e+03
2.8e+01 95 30 17 0 6 95 30 17 0 8 0
EPSSetUp 1 1.0 1.0482e+01 1.0 0.00e+00 0.0 0.0e+00 0.0e+00
4.6e+01 2 0 0 0 10 2 0 0 0 13 0
EPSSolve 1 1.0 3.7193e+02 1.0 9.59e+07 1.1 3.5e+02 4.2e+03
9.7e+01 69 91 35 1 22 69 91 35 1 27 1
IPOrthogonalize 19 1.0 3.4406e-01 1.1 6.75e+07 1.1 2.3e+02 4.2e+03
7.6e+01 0 64 22 1 17 0 64 22 1 21 767
IPInnerProduct 153 1.0 3.1410e-01 1.0 5.63e+07 1.1 2.3e+02 4.2e+03
3.9e+01 0 53 23 1 9 0 53 23 1 11 700
IPApplyMatrix 39 1.0 2.4903e-01 1.1 4.38e+07 1.1 2.3e+02 4.2e+03
0.0e+00 0 42 23 1 0 0 42 23 1 0 687
UpdateVectors 1 1.0 4.2958e-03 1.2 4.51e+06 1.1 0.0e+00 0.0e+00
0.0e+00 0 4 0 0 0 0 4 0 0 0 4107
VecDot 1 1.0 5.6815e-04 4.7 2.97e+04 1.1 0.0e+00 0.0e+00
1.0e+00 0 0 0 0 0 0 0 0 0 0 204
VecNorm 8 1.0 2.5260e-03 3.2 2.38e+05 1.1 0.0e+00 0.0e+00
8.0e+00 0 0 0 0 2 0 0 0 0 2 368
VecScale 27 1.0 5.9605e-04 1.1 4.01e+05 1.1 0.0e+00 0.0e+00
0.0e+00 0 0 0 0 0 0 0 0 0 0 2629
VecCopy 53 1.0 4.0610e-03 1.4 0.00e+00 0.0 0.0e+00 0.0e+00
0.0e+00 0 0 0 0 0 0 0 0 0 0 0
VecSet 77 1.0 6.2165e-03 1.1 0.00e+00 0.0 0.0e+00 0.0e+00
0.0e+00 0 0 0 0 0 0 0 0 0 0 0
VecAXPY 38 1.0 2.7709e-03 1.7 1.13e+06 1.1 0.0e+00 0.0e+00
0.0e+00 0 1 0 0 0 0 1 0 0 0 1592
VecMAXPY 38 1.0 2.5925e-02 1.1 1.13e+07 1.1 0.0e+00 0.0e+00
0.0e+00 0 11 0 0 0 0 11 0 0 0 1701
VecAssemblyBegin 5 1.0 9.0070e-03 2.3 0.00e+00 0.0 3.6e+01 2.1e+04
1.5e+01 0 0 4 0 3 0 0 4 0 4 0
VecAssemblyEnd 5 1.0 3.4809e-04 1.1 0.00e+00 0.0 0.0e+00 0.0e+00
0.0e+00 0 0 0 0 0 0 0 0 0 0 0
VecScatterBegin 73 1.0 8.5931e-03 1.5 0.00e+00 0.0 4.6e+02 8.9e+03
0.0e+00 0 0 45 2 0 0 0 45 2 0 0
VecScatterEnd 73 1.0 2.2542e-02 2.2 0.00e+00 0.0 0.0e+00 0.0e+00
0.0e+00 0 0 0 0 0 0 0 0 0 0 0
VecReduceArith 76 1.0 3.0838e-02 1.1 1.24e+07 1.1 0.0e+00 0.0e+00
0.0e+00 0 12 0 0 0 0 12 0 0 0 1573
VecReduceComm 38 1.0 4.8040e-02 2.0 0.00e+00 0.0 0.0e+00 0.0e+00
3.8e+01 0 0 0 0 8 0 0 0 0 11 0
VecNormalize 8 1.0 2.7280e-03 2.8 3.56e+05 1.1 0.0e+00 0.0e+00
8.0e+00 0 0 0 0 2 0 0 0 0 2 511
MatMult 67 1.0 4.1397e-01 1.1 7.53e+07 1.1 4.0e+02 4.2e+03
0.0e+00 0 71 40 1 0 0 71 40 1 0 710
MatSolve 28 1.0 5.1757e+02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00
0.0e+00 95 0 0 0 0 95 0 0 0 0 0
MatLUFactorSym 1 1.0 3.6097e-04 1.1 0.00e+00 0.0 0.0e+00 0.0e+00
0.0e+00 0 0 0 0 0 0 0 0 0 0 0
MatLUFactorNum 1 1.0 1.0464e+01 1.0 0.00e+00 0.0 0.0e+00 0.0e+00
0.0e+00 2 0 0 0 0 2 0 0 0 0 0
MatAssemblyBegin 9 1.0 3.3842e-0146.7 0.00e+00 0.0 5.4e+01 6.0e+04
8.0e+00 0 0 5 2 2 0 0 5 2 2 0
MatAssemblyEnd 9 1.0 2.3042e-01 1.0 0.00e+00 0.0 3.6e+01 9.4e+02
3.1e+01 0 0 4 0 7 0 0 4 0 9 0
MatGetRow 5206 1.1 3.1164e-03 1.1 0.00e+00 0.0 0.0e+00 0.0e+00
0.0e+00 0 0 0 0 0 0 0 0 0 0 0
MatGetSubMatrice 5 1.0 8.7580e-01 1.2 0.00e+00 0.0 1.5e+02 1.1e+06
2.5e+01 0 0 15 88 6 0 0 15 88 7 0
MatZeroEntries 2 1.0 1.0233e-02 1.1 0.00e+00 0.0 0.0e+00 0.0e+00
0.0e+00 0 0 0 0 0 0 0 0 0 0 0
MatView 2 1.0 1.0149e-03 2.0 0.00e+00 0.0 0.0e+00 0.0e+00
2.0e+00 0 0 0 0 0 0 0 0 0 1 0
KSPSetup 1 1.0 2.8610e-06 1.5 0.00e+00 0.0 0.0e+00 0.0e+00
0.0e+00 0 0 0 0 0 0 0 0 0 0 0
KSPSolve 28 1.0 5.1758e+02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00
2.8e+01 95 0 0 0 6 95 0 0 0 8 0
PCSetUp 1 1.0 1.0467e+01 1.0 0.00e+00 0.0 0.0e+00 0.0e+00
8.0e+00 2 0 0 0 2 2 0 0 0 2 0
PCApply 28 1.0 5.1757e+02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00
0.0e+00 95 0 0 0 0 95 0 0 0 0 0
------------------------------------------------------------------------------------------------------------------------
Memory usage is given in bytes:
Object Type Creations Destructions Memory Descendants' Mem.
--- Event Stage 0: Main Stage
Spectral Transform 1 1 536 0
Eigenproblem Solver 1 1 824 0
Inner product 1 1 428 0
Index Set 38 38 1796776 0
IS L to G Mapping 1 1 58700 0
Vec 65 65 5458584 0
Vec Scatter 9 9 7092 0
Application Order 1 1 155232 0
Matrix 17 16 17715680 0
Krylov Solver 1 1 832 0
Preconditioner 1 1 744 0
Viewer 2 2 1088 0
========================================================================================================================
Average time to get PetscTime(): 1.90735e-07
Average time for MPI_Barrier(): 5.9557e-05
Average time for zero size MPI_Send(): 2.97427e-05
#PETSc Option Table entries:
-log_summary
-mat_superlu_dist_parsymbfact
#End o PETSc Option Table entries
Compiled without FORTRAN kernels
Compiled with full precision matrices (default)
sizeof(short) 2 sizeof(int) 4 sizeof(long) 8 sizeof(void*) 8
sizeof(PetscScalar) 8
Configure run at: Wed May 6 15:14:39 2009
Configure options: --download-superlu_dist=1 --download-parmetis=1
--with-mpi-dir=/usr/lib/mpich --with-shared=0
-----------------------------------------
Libraries compiled on Wed May 6 15:14:49 CEST 2009 on medusa1
Machine characteristics: Linux medusa1 2.6.18-6-amd64 #1 SMP Fri Dec 12
05:49:32 UTC 2008 x86_64 GNU/Linux
Using PETSc directory: /home/fredrik/Hakan/cmlfet/external/petsc-3.0.0-p5
Using PETSc arch: linux-gnu-c-debug
-----------------------------------------
Using C compiler: /usr/lib/mpich/bin/mpicc -Wall -Wwrite-strings
-Wno-strict-aliasing -g3
Using Fortran compiler: /usr/lib/mpich/bin/mpif77 -Wall
-Wno-unused-variable -g
-----------------------------------------
Using include paths:
-I/home/fredrik/Hakan/cmlfet/external/petsc-3.0.0-p5/linux-gnu-c-debug/include
-I/home/fredrik/Hakan/cmlfet/external/petsc-3.0.0-p5/include
-I/home/fredrik/Hakan/cmlfet/external/petsc-3.0.0-p5/linux-gnu-c-debug/include
-I/usr/lib/mpich/include
------------------------------------------
Using C linker: /usr/lib/mpich/bin/mpicc -Wall -Wwrite-strings
-Wno-strict-aliasing -g3
Using Fortran linker: /usr/lib/mpich/bin/mpif77 -Wall
-Wno-unused-variable -g
Using libraries:
-Wl,-rpath,/home/fredrik/Hakan/cmlfet/external/petsc-3.0.0-p5/linux-gnu-c-debug/lib
-L/home/fredrik/Hakan/cmlfet/external/petsc-3.0.0-p5/linux-gnu-c-debug/lib
-lpetscts -lpetscsnes -lpetscksp -lpetscdm -lpetscmat -lpetscvec
-lpetsc -lX11
-Wl,-rpath,/home/fredrik/Hakan/cmlfet/external/petsc-3.0.0-p5/linux-gnu-c-debug/lib
-L/home/fredrik/Hakan/cmlfet/external/petsc-3.0.0-p5/linux-gnu-c-debug/lib
-lsuperlu_dist_2.3 -llapack -lblas -lparmetis -lmetis -lm
-L/usr/lib/mpich/lib -L/usr/lib/gcc/x86_64-linux-gnu/4.1.2 -L/usr/lib64
-L/lib64 -ldl -lmpich -lpthread -lrt -lgcc_s -lg2c -lm
-L/usr/lib/gcc/x86_64-linux-gnu/3.4.6 -L/lib -lm -ldl -lmpich -lpthread
-lrt -lgcc_s -ldl
------------------------------------------
real 9m10.616s
user 0m23.921s
sys 0m6.944s
Satish Balay wrote:
> Just a note about scalability: its a function of the hardware as
> well.. For proper scalability studies - you'll need a true distributed
> system with fast network [not SMP nodes..]
>
> Satish
>
> On Fri, 8 May 2009, Fredrik Bengzon wrote:
>
>
>> Hong,
>> Thank you for the suggestions, but I have looked at the EPS and KSP objects
>> and I can not find anything wrong. The problem is that it takes longer to
>> solve with 4 cpus than with 2 so the scalability seems to be absent when using
>> superlu_dist. I have stored my mass and stiffness matrix in the mpiaij format
>> and just passed them on to slepc. When using the petsc iterative krylov
>> solvers i see 100% workload on all processors but when i switch to
>> superlu_dist only two cpus seem to do the whole work of LU factoring. I don't
>> want to use the krylov solver though since it might cause slepc not to
>> converge.
>> Regards,
>> Fredrik
>>
>> Hong Zhang wrote:
>>
>>> Run your code with '-eps_view -ksp_view' for checking
>>> which methods are used
>>> and '-log_summary' to see which operations dominate
>>> the computation.
>>>
>>> You can turn on parallel symbolic factorization
>>> with '-mat_superlu_dist_parsymbfact'.
>>>
>>> Unless you use large num of processors, symbolic factorization
>>> takes ignorable execution time. The numeric
>>> factorization usually dominates.
>>>
>>> Hong
>>>
>>> On Fri, 8 May 2009, Fredrik Bengzon wrote:
>>>
>>>
>>>> Hi Petsc team,
>>>> Sorry for posting questions not really concerning the petsc core, but when
>>>> I run superlu_dist from within slepc I notice that the load balance is
>>>> poor. It is just fine during assembly (I use Metis to partition my finite
>>>> element mesh) but when calling the slepc solver it dramatically changes. I
>>>> use superlu_dist as solver for the eigenvalue iteration. My question is:
>>>> can this have something to do with the fact that the option 'Parallel
>>>> symbolic factorization' is set to false? If so, can I change the options
>>>> to superlu_dist using MatSetOption for instance? Also, does this mean that
>>>> superlu_dist is not using parmetis to reorder the matrix?
>>>> Best Regards,
>>>> Fredrik Bengzon
>>>>
>>>>
>>>>
>>
>
>
>
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