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 >>> >> >> >
