I basically used 'runex56' and set '-ne' be compatible with np. Then I used option '-matptap_via scalable' '-matptap_via hypre' '-matptap_via nonscalable' I attached a job script below. In master branch, I set default as 'nonscalable' for small - medium size matrices, and automatically switch to 'scalable' when matrix size gets larger. Petsc solver uses MatPtAP, which does local RAP to reduce communication and accelerate computation. I suggest you simply use default setting. Let me know if you encounter trouble. Hong job.ne174.n8.np125.sh: runjob --np 125 -p 16 --block $COBALT_PARTNAME --verbose=INFO : ./ex56 -ne 174 -alpha 1.e-3 -ksp_type cg -pc_type gamg -pc_gamg_agg_nsmooths 1 -pc_gamg_reuse_interpolation true -ksp_converged_reason -use_mat_nearnullspace -mg_levels_esteig_ksp_type cg -mg_levels_esteig_ksp_max_it 10 -pc_gamg_square_graph 1 -mg_levels_ksp_max_it 1 -mg_levels_ksp_type chebyshev -mg_levels_ksp_chebyshev_esteig 0,0.2,0,1.05 -gamg_est_ksp_type cg -gamg_est_ksp_max_it 10 -pc_gamg_asm_use_agg true -mg_levels_sub_pc_type lu -mg_levels_pc_asm_overlap 0 -pc_gamg_threshold -0.01 -pc_gamg_coarse_eq_limit 200 -pc_gamg_process_eq_limit 30 -pc_gamg_repartition false -pc_mg_cycle_type v -pc_gamg_use_parallel_coarse_grid_solver -mg_coarse_pc_type jacobi -mg_coarse_ksp_type cg -ksp_monitor -log_view -matptap_via scalable > log.ne174.n8.np125.scalable runjob --np 125 -p 16 --block $COBALT_PARTNAME --verbose=INFO : ./ex56 -ne 174 -alpha 1.e-3 -ksp_type cg -pc_type gamg -pc_gamg_agg_nsmooths 1 -pc_gamg_reuse_interpolation true -ksp_converged_reason -use_mat_nearnullspace -mg_levels_esteig_ksp_type cg -mg_levels_esteig_ksp_max_it 10 -pc_gamg_square_graph 1 -mg_levels_ksp_max_it 1 -mg_levels_ksp_type chebyshev -mg_levels_ksp_chebyshev_esteig 0,0.2,0,1.05 -gamg_est_ksp_type cg -gamg_est_ksp_max_it 10 -pc_gamg_asm_use_agg true -mg_levels_sub_pc_type lu -mg_levels_pc_asm_overlap 0 -pc_gamg_threshold -0.01 -pc_gamg_coarse_eq_limit 200 -pc_gamg_process_eq_limit 30 -pc_gamg_repartition false -pc_mg_cycle_type v -pc_gamg_use_parallel_coarse_grid_solver -mg_coarse_pc_type jacobi -mg_coarse_ksp_type cg -ksp_monitor -log_view -matptap_via hypre > log.ne174.n8.np125.hypre runjob --np 125 -p 16 --block $COBALT_PARTNAME --verbose=INFO : ./ex56 -ne 174 -alpha 1.e-3 -ksp_type cg -pc_type gamg -pc_gamg_agg_nsmooths 1 -pc_gamg_reuse_interpolation true -ksp_converged_reason -use_mat_nearnullspace -mg_levels_esteig_ksp_type cg -mg_levels_esteig_ksp_max_it 10 -pc_gamg_square_graph 1 -mg_levels_ksp_max_it 1 -mg_levels_ksp_type chebyshev -mg_levels_ksp_chebyshev_esteig 0,0.2,0,1.05 -gamg_est_ksp_type cg -gamg_est_ksp_max_it 10 -pc_gamg_asm_use_agg true -mg_levels_sub_pc_type lu -mg_levels_pc_asm_overlap 0 -pc_gamg_threshold -0.01 -pc_gamg_coarse_eq_limit 200 -pc_gamg_process_eq_limit 30 -pc_gamg_repartition false -pc_mg_cycle_type v -pc_gamg_use_parallel_coarse_grid_solver -mg_coarse_pc_type jacobi -mg_coarse_ksp_type cg -ksp_monitor -log_view -matptap_via nonscalable > log.ne174.n8.np125.nonscalable runjob --np 125 -p 16 --block $COBALT_PARTNAME --verbose=INFO : ./ex56 -ne 174 -alpha 1.e-3 -ksp_type cg -pc_type gamg -pc_gamg_agg_nsmooths 1 -pc_gamg_reuse_interpolation true -ksp_converged_reason -use_mat_nearnullspace -mg_levels_esteig_ksp_type cg -mg_levels_esteig_ksp_max_it 10 -pc_gamg_square_graph 1 -mg_levels_ksp_max_it 1 -mg_levels_ksp_type chebyshev -mg_levels_ksp_chebyshev_esteig 0,0.2,0,1.05 -gamg_est_ksp_type cg -gamg_est_ksp_max_it 10 -pc_gamg_asm_use_agg true -mg_levels_sub_pc_type lu -mg_levels_pc_asm_overlap 0 -pc_gamg_threshold -0.01 -pc_gamg_coarse_eq_limit 200 -pc_gamg_process_eq_limit 30 -pc_gamg_repartition false -pc_mg_cycle_type v -pc_gamg_use_parallel_coarse_grid_solver -mg_coarse_pc_type jacobi -mg_coarse_ksp_type cg -ksp_monitor -log_view > log.ne174.n8.np125 On Wed, May 3, 2017 at 2:08 PM, Mark Adams <[email protected]> wrote:
Hong,the input files do not seem to be accessible. What are the command line option? (I don't see a "rap" or "scale" in the source).
On Wed, May 3, 2017 at 12:17 PM, Hong <[email protected]> wrote:
Mark, Below is the copy of my email sent to you on Feb 27:
I implemented scalable MatPtAP and did comparisons of three implementations using ex56.c on alcf cetus machine (this machine has small memory, 1GB/core): - nonscalable PtAP: use an array of length PN to do dense axpy - scalable PtAP: do sparse axpy without use of PN array - hypre PtAP.
The results are attached. Summary: - nonscalable PtAP is 2x faster than scalable, 8x faster than hypre PtAP - scalable PtAP is 4x faster than hypre PtAP - hypre uses less memory (see job.ne399.n63.np1000.sh)
Based on above observation, I set the default PtAP algorithm as 'nonscalable'. When PN > local estimated nonzero of C=PtAP, then switch default to 'scalable'. User can overwrite default.
For the case of np=8000, ne=599 (see job.ne599.n500.np8000.sh), I get MatPtAP 3.6224e+01 (nonscalable for small mats, scalable for larger ones) scalable MatPtAP 4.6129e+01 hypre 1.9389e+02
This work in on petsc-master. Give it a try. If you encounter any problem, let me know.
Hong
On Wed, May 3, 2017 at 10:01 AM, Mark Adams <[email protected]> wrote:
(Hong), what is the current state of optimizing RAP for scaling?
Nate, is driving 3D elasticity problems at scaling with GAMG and we are working out performance problems. They are hitting problems at ~1.5B dof problems on a basic Cray (XC30 I think).
Thanks, Mark