def conv_temporal():
mpi_num_procs, omp_num_threads = utils.parse_args()
num_steps = 8
############################################################################
# TEST 1: TEMPORAL CONVERGENCE
############################################################################
# prog = 'advdiff-ss'
# prog = 'advdiff-ss-tv'
prog = 'advdiff-ss-tv-extrap'
vtk_save_rate = 0
mrg_type = 3
np = mpi_num_procs
num_pnts = 8**(math.floor(math.log(np,8)+1))
nt = omp_num_threads
# TREE TOLERANCE
tl_factor = 1#0.1
tl_init = 1e-5
tl_list = [tl_init*math.pow(tl_factor,float(cnt)) for cnt in range(0,num_steps)]
# TIME RESOLUTION
dt_factor = 0.5
# dt_init = 0.1/16#0.5**5
dt_init = 0.5**0
dt_list = [dt_init*math.pow(dt_factor,float(cnt)) for cnt in range(0,num_steps)]
# NUM TIME STEPS
T_END = 1.0
tn_factor = 1.0/dt_factor
tn_init = T_END/dt_init
tn_list = [tn_init*math.pow(tn_factor,float(cnt)) for cnt in range(0,num_steps)]
dp_list = [15 for cnt in range(0,num_steps)]
cq_list = [14 for cnt in range(0,num_steps)]
ci_list = [True for cnt in range(0,num_steps)]
uf_list = [2 for cnt in range(0,num_steps)]
pn_list = [num_pnts for cnt in range(0,num_steps)]
np_list = [np for cnt in range(0,num_steps)]
nt_list = [nt for cnt in range(0,num_steps)]
mg_list = [mrg_type for cnt in range(0,num_steps)]
vs_list = [vtk_save_rate for cnt in range(0,num_steps)]
tt_list = [11 for cnt in range(0,num_steps)]
cmd_args = OrderedDict()
cmd_args = utils.generate_commands(
prog,
pn_list,
tl_list,
dp_list,
cq_list,
ci_list,
uf_list,
np_list,
nt_list,
dt_list,
tn_list,
vs_list,
mg_list,
tt_list)
utils.execute_commands(cmd_args, 'temporal')
def conv_temporal_spatial_long_time():
############################################################################
# TEST 2: CONVERGENCE TEST FOR ADVECTION-DIFFUSION SOLVER
############################################################################
mpi_num_procs, omp_num_threads = utils.parse_args()
# prog = 'advdiff-ss'
# prog = 'advdiff-ss-tv'
prog = 'advdiff-ss-tv-extrap'
dt = 0.0628
vtk_save_rate = 0
mrg_type = 3
np = mpi_num_procs
num_pnts = 8**(math.floor(math.log(np, 8) + 1))
nt = omp_num_threads
# UNIFORM
# dp_list = [5 , 6 , 7 ]
# cq_list = [3 , 3 , 3 ]
# ci_list = [True , True , True ]
# uf_list = [2 , 2 , 2 ]
# dt_list = [dt , dt/2 , dt/4 ]
# tn_list = [100 , 200 , 400 ]
# num_steps = len(dp_list)
# tl_list = [1e-30 for cnt in range(0,num_steps)]
# ADAPTIVE
# LOW ORDER
tl_list = [1e-02, 1e-03, 1e-04]
dp_list = [15, 15, 15]
cq_list = [3, 3, 3]
ci_list = [True, True, True]
uf_list = [2, 2, 2]
dt_list = [dt, dt / 2, dt / 3]
tn_list = [100, 200, 300]
# HIGH ORDER
# tl_list = [1e-03, 1e-04, 1e-05, 1e-6 ]
# dp_list = [15 , 15 , 15 , 15 ]
# cq_list = [14 , 14 , 14 , 14 ]
# ci_list = [True , True , True , True ]
# uf_list = [2 , 2 , 2 , 2 ]
# dt_list = [dt/4 , dt/8 , dt/16, dt/32 ]
# tn_list = [400 , 800 , 1600 , 3200 ]
num_steps = len(dp_list)
pn_list = [num_pnts for cnt in range(0, num_steps)]
np_list = [np for cnt in range(0, num_steps)]
nt_list = [nt for cnt in range(0, num_steps)]
mg_list = [mrg_type for cnt in range(0, num_steps)]
vs_list = [vtk_save_rate for cnt in range(0, num_steps)]
tt_list = [11 for cnt in range(0, num_steps)]
cmd_args = OrderedDict()
cmd_args = utils.generate_commands(prog, pn_list, tl_list, dp_list,
cq_list, ci_list, uf_list, np_list,
nt_list, dt_list, tn_list, vs_list,
mg_list, tt_list)
utils.execute_commands(cmd_args, 'temporal-spatial-long-time')
def conv_temporal():
mpi_num_procs, omp_num_threads = utils.parse_args()
num_steps = 8
############################################################################
# TEST 1: TEMPORAL CONVERGENCE
############################################################################
# prog = 'advdiff-ss'
# prog = 'advdiff-ss-tv'
prog = 'advdiff-ss-tv-extrap'
vtk_save_rate = 0
mrg_type = 3
np = mpi_num_procs
num_pnts = 8**(math.floor(math.log(np, 8) + 1))
nt = omp_num_threads
# TREE TOLERANCE
tl_factor = 1 #0.1
tl_init = 1e-5
tl_list = [
tl_init * math.pow(tl_factor, float(cnt))
for cnt in range(0, num_steps)
]
# TIME RESOLUTION
dt_factor = 0.5
# dt_init = 0.1/16#0.5**5
dt_init = 0.5**0
dt_list = [
dt_init * math.pow(dt_factor, float(cnt))
for cnt in range(0, num_steps)
]
# NUM TIME STEPS
T_END = 1.0
tn_factor = 1.0 / dt_factor
tn_init = T_END / dt_init
tn_list = [
tn_init * math.pow(tn_factor, float(cnt))
for cnt in range(0, num_steps)
]
dp_list = [15 for cnt in range(0, num_steps)]
cq_list = [14 for cnt in range(0, num_steps)]
ci_list = [True for cnt in range(0, num_steps)]
uf_list = [2 for cnt in range(0, num_steps)]
pn_list = [num_pnts for cnt in range(0, num_steps)]
np_list = [np for cnt in range(0, num_steps)]
nt_list = [nt for cnt in range(0, num_steps)]
mg_list = [mrg_type for cnt in range(0, num_steps)]
vs_list = [vtk_save_rate for cnt in range(0, num_steps)]
tt_list = [11 for cnt in range(0, num_steps)]
cmd_args = OrderedDict()
cmd_args = utils.generate_commands(prog, pn_list, tl_list, dp_list,
cq_list, ci_list, uf_list, np_list,
nt_list, dt_list, tn_list, vs_list,
mg_list, tt_list)
utils.execute_commands(cmd_args, 'temporal')
def conv_temporal_spatial_long_time():
############################################################################
# TEST 2: CONVERGENCE TEST FOR ADVECTION
############################################################################
mpi_num_procs, omp_num_threads = utils.parse_args()
prog = 'advection'
dt = 0.0628
vsr = 0
mrg_type = 3
np = mpi_num_procs
num_pnts = 8**(math.floor(math.log(np,8)+1))
nt = omp_num_threads
# UNIFORM
dp_list = [5 , 6 , 7 ]#, 5 , 6 , 7 ]
cq_list = [3 , 3 , 3 ]#, 3 , 3 , 3 ]
ci_list = [False , False , False ]#, False , False , False]
uf_list = [2 , 2 , 2 ]#, 2 , 2 , 2 ]
dt_list = [dt , dt/2 , dt/4 ]#, dt , dt/2 , dt/4 ]
tn_list = [100 , 200 , 400 ]#, 100 , 200 , 400 ]
num_steps = len(dp_list)
tl_list = [1e-30 for cnt in range(0,num_steps)]
# ADAPTIVE
# tl_list = [1e-02, 1e-03, 1e-04, 1e-02 , 1e-03 , 1e-04]
# dp_list = [15 , 15 , 15 , 15 , 15 , 15 ]
# cq_list = [3 , 3 , 3 , 3 , 3 , 3 ]
# ci_list = [True , True , True , False , False , False]
# uf_list = [2 , 2 , 2 , 2 , 2 , 2 ]
# dt_list = [dt , dt/2 , dt/4 , dt , dt/2 , dt/4 ]
# tn_list = [100 , 200 , 400 , 100 , 200 , 400 ]
num_steps = len(dp_list)
pn_list = [num_pnts for cnt in range(0,num_steps)]
np_list = [np for cnt in range(0,num_steps)]
nt_list = [nt for cnt in range(0,num_steps)]
mg_list = [mrg_type for cnt in range(0,num_steps)]
vs_list = [vsr for cnt in range(0,num_steps)]
cmd_args = OrderedDict()
cmd_args = utils.generate_commands(
prog,
pn_list,
tl_list,
dp_list,
cq_list,
ci_list,
uf_list,
np_list,
nt_list,
dt_list,
tn_list,
vs_list,
mg_list)
utils.execute_commands(cmd_args, 'temporal-spatial-long-time')
def scals():
mpi_num_procs, omp_num_threads = utils.parse_args()
############################################################################
# TEST 1: STRONG SCALING
############################################################################
# prog = 'advection'
prog = 'advdiff-ss'
np_list = [
1,
2,
4,
8,
# 16,
# 32,
# 64,
]
mrg_type = 3;
max_np = max(np_list)
num_pnts = 8**3#(math.floor(math.log(max_np,8)+1))
num_steps = len(np_list)
pn_list = [num_pnts for cnt in range(0,num_steps)]
tl_list = [1e-0 for cnt in range(0,num_steps)]
dp_list = [15 for cnt in range(0,num_steps)]
cq_list = [14 for cnt in range(0,num_steps)]
ci_list = [True for cnt in range(0,num_steps)]
uf_list = [4 for cnt in range(0,num_steps)]
nt_list = [omp_num_threads for cnt in range(0,num_steps)]
dt_list = [0.125 for cnt in range(0,num_steps)]
tn_list = [1 for cnt in range(0,num_steps)]
vs_list = [0 for cnt in range(0,num_steps)]
mg_list = [mrg_type for cnt in range(0,num_steps)]
cmd_args = OrderedDict()
cmd_args = utils.generate_commands(
prog,
pn_list,
tl_list,
dp_list,
cq_list,
ci_list,
uf_list,
np_list,
nt_list,
dt_list,
tn_list,
vs_list,
mg_list)
utils.execute_commands(cmd_args, 'sscal'+prog)
def conv_temporal_spatial_long_time():
############################################################################
# TEST 2: CONVERGENCE TEST FOR ADVECTION
############################################################################
mpi_num_procs, omp_num_threads = utils.parse_args()
prog = 'advection'
dt = 0.0628
vsr = 0
mrg_type = 3
np = mpi_num_procs
num_pnts = 8**(math.floor(math.log(np, 8) + 1))
nt = omp_num_threads
# UNIFORM
dp_list = [5, 6, 7] #, 5 , 6 , 7 ]
cq_list = [3, 3, 3] #, 3 , 3 , 3 ]
ci_list = [False, False, False] #, False , False , False]
uf_list = [2, 2, 2] #, 2 , 2 , 2 ]
dt_list = [dt, dt / 2, dt / 4] #, dt , dt/2 , dt/4 ]
tn_list = [100, 200, 400] #, 100 , 200 , 400 ]
num_steps = len(dp_list)
tl_list = [1e-30 for cnt in range(0, num_steps)]
# ADAPTIVE
# tl_list = [1e-02, 1e-03, 1e-04, 1e-02 , 1e-03 , 1e-04]
# dp_list = [15 , 15 , 15 , 15 , 15 , 15 ]
# cq_list = [3 , 3 , 3 , 3 , 3 , 3 ]
# ci_list = [True , True , True , False , False , False]
# uf_list = [2 , 2 , 2 , 2 , 2 , 2 ]
# dt_list = [dt , dt/2 , dt/4 , dt , dt/2 , dt/4 ]
# tn_list = [100 , 200 , 400 , 100 , 200 , 400 ]
num_steps = len(dp_list)
pn_list = [num_pnts for cnt in range(0, num_steps)]
np_list = [np for cnt in range(0, num_steps)]
nt_list = [nt for cnt in range(0, num_steps)]
mg_list = [mrg_type for cnt in range(0, num_steps)]
vs_list = [vsr for cnt in range(0, num_steps)]
cmd_args = OrderedDict()
cmd_args = utils.generate_commands(prog, pn_list, tl_list, dp_list,
cq_list, ci_list, uf_list, np_list,
nt_list, dt_list, tn_list, vs_list,
mg_list)
utils.execute_commands(cmd_args, 'temporal-spatial-long-time')
def omp():
############################################################################
# TEST: OMP SCALING
############################################################################
mpi_num_procs, omp_num_threads = utils.parse_args()
# prog = 'advection'
prog = 'advdiff-ss'
dt = 0.0628
vsr = 0
mrg_type = 3
num_pnts = 8**(math.floor(math.log(mpi_num_procs,8)+1))
num_steps = omp_num_threads
nt_list = [cnt+1 for cnt in range(0,num_steps)]
np_list = [mpi_num_procs for cnt in range(0,num_steps)]
tn_list = [10 for cnt in range(0,num_steps)]
pn_list = [num_pnts for cnt in range(0,num_steps)]
tl_list = [1e-30 for cnt in range(0,num_steps)]
dp_list = [4 for cnt in range(0,num_steps)]
mg_list = [mrg_type for cnt in range(0,num_steps)]
vs_list = [vsr for cnt in range(0,num_steps)]
cq_list = [14 for cnt in range(0,num_steps)]
ci_list = [True for cnt in range(0,num_steps)]
uf_list = [4 for cnt in range(0,num_steps)]
dt_list = [dt for cnt in range(0,num_steps)]
cmd_args = OrderedDict()
cmd_args = utils.generate_commands(
prog,
pn_list,
tl_list,
dp_list,
cq_list,
ci_list,
uf_list,
np_list,
nt_list,
dt_list,
tn_list,
vs_list,
mg_list)
utils.execute_commands(cmd_args, 'omp'+prog)
def conv_spatial():
############################################################################
# TEMPORAL CONVERGENCE TEST FOR ADVECTION
############################################################################
mpi_num_procs, omp_num_threads = utils.parse_args()
prog = 'advection'
num_steps = 8
##############################
# TREE TOLERANCE
##############################
tl_fact = 0.1
tl_init = 1e-1
tl_list = [
tl_init * math.pow(tl_fact, float(cnt)) for cnt in range(0, num_steps)
]
##############################
# TIME RESOLUTION
##############################
dt_fact = 1
dt_init = 1e-3
dt_list = [
dt_init * math.pow(dt_fact, float(cnt)) for cnt in range(0, num_steps)
]
T_END = 1.0
tn_fact = 1.0 / dt_fact
tn_init = 1 #T_END/dt_init
tn_list = [
tn_init * math.pow(tn_fact, float(cnt)) for cnt in range(0, num_steps)
]
##############################
# TREE DEPTH/POINTS
##############################
dp_list = [15 for cnt in range(0, num_steps)]
num_pnts = 8**(math.floor(math.log(mpi_num_procs, 8) + 1))
pn_list = [num_pnts for cnt in range(0, num_steps)]
##############################
# PARALLEL
##############################
mpi_num_procs = mpi_num_procs
np_list = [mpi_num_procs for cnt in range(0, num_steps)]
nt = omp_num_threads
nt_list = [nt for cnt in range(0, num_steps)]
mrg_type = 3
mg_list = [mrg_type for cnt in range(0, num_steps)]
##############################
# CHEBYSHEV/CUBIC INTERPOLATION
##############################
cq_list = [14 for cnt in range(0, num_steps)]
ci_list = [True for cnt in range(0, num_steps)]
uf_list = [4 for cnt in range(0, num_steps)]
##############################
# VISUALIZATION
##############################
vtk_save_rate = 0
vs_list = [vtk_save_rate for cnt in range(0, num_steps)]
cmd_args = OrderedDict()
cmd_args = utils.generate_commands(prog, pn_list, tl_list, dp_list,
cq_list, ci_list, uf_list, np_list,
nt_list, dt_list, tn_list, vs_list,
mg_list)
utils.execute_commands(cmd_args, 'spatial')
def scalws():
############################################################################
# TEST 1: WEAK/STRONG SCALING FOR ADVECTION SOLVER
############################################################################
prog = 'advection'
tl_list = [
# 1e+0,
1e-2,
1e-4,
1e-7,
]
dp_list = [
# 6,
# 8,
# 10,
15,
]
cq_list = [
4,
6,
14,
]
np_list = [
1,
2,
4,
8,
16,
32,
]
# dt = 7.85398e-03
# tn = 100
dt = 0.0628
tn = 200
use_cubic = True
vsr = 0
merge_type = 3
max_np = max(np_list)
num_pnts = 8**(math.floor(math.log(max_np,8)+1))
uf = 2
table_counter = 0
for cq in cq_list:
for tl in tl_list:
if cq is 4 and tl is 1e-7:
continue
for dp in dp_list:
# USE UF 4 FOR Q 14
if cq is 14:
uf = 4
cmd_args = OrderedDict()
cmd_id = 0
for np in np_list:
cmd_args[cmd_id] = utils.generate_commands(
prog = prog,
pn_list = [num_pnts ],
tl_list = [tl ],
dp_list = [dp ],
cq_list = [cq ],
ci_list = [use_cubic ],
uf_list = [uf ],
np_list = [np ],
nt_list = [omp_num_threads ],
dt_list = [dt ],
tn_list = [tn ],
vs_list = [vsr ],
mg_list = [merge_type ]
)[1]
cmd_id = cmd_id + 1
utils.execute_commands(cmd_args, 'scalws'+prog+'-table-'+str(table_counter))
table_counter = table_counter + 1
def conv_temporal_spatial_long_time():
############################################################################
# TEST 2: CONVERGENCE TEST FOR ADVECTION-DIFFUSION SOLVER
############################################################################
mpi_num_procs, omp_num_threads = utils.parse_args()
# prog = 'advdiff-ss'
# prog = 'advdiff-ss-tv'
prog = 'advdiff-ss-tv-extrap'
dt = 0.0628
vtk_save_rate = 0
mrg_type = 3
np = mpi_num_procs
num_pnts = 8**(math.floor(math.log(np,8)+1))
nt = omp_num_threads
# UNIFORM
# dp_list = [5 , 6 , 7 ]
# cq_list = [3 , 3 , 3 ]
# ci_list = [True , True , True ]
# uf_list = [2 , 2 , 2 ]
# dt_list = [dt , dt/2 , dt/4 ]
# tn_list = [100 , 200 , 400 ]
# num_steps = len(dp_list)
# tl_list = [1e-30 for cnt in range(0,num_steps)]
# ADAPTIVE
# LOW ORDER
tl_list = [1e-02, 1e-03, 1e-04 ]
dp_list = [15 , 15 , 15 ]
cq_list = [3 , 3 , 3 ]
ci_list = [True , True , True ]
uf_list = [2 , 2 , 2 ]
dt_list = [dt , dt/2 , dt/3 ]
tn_list = [100 , 200 , 300 ]
# HIGH ORDER
# tl_list = [1e-03, 1e-04, 1e-05, 1e-6 ]
# dp_list = [15 , 15 , 15 , 15 ]
# cq_list = [14 , 14 , 14 , 14 ]
# ci_list = [True , True , True , True ]
# uf_list = [2 , 2 , 2 , 2 ]
# dt_list = [dt/4 , dt/8 , dt/16, dt/32 ]
# tn_list = [400 , 800 , 1600 , 3200 ]
num_steps = len(dp_list)
pn_list = [num_pnts for cnt in range(0,num_steps)]
np_list = [np for cnt in range(0,num_steps)]
nt_list = [nt for cnt in range(0,num_steps)]
mg_list = [mrg_type for cnt in range(0,num_steps)]
vs_list = [vtk_save_rate for cnt in range(0,num_steps)]
tt_list = [11 for cnt in range(0,num_steps)]
cmd_args = OrderedDict()
cmd_args = utils.generate_commands(
prog,
pn_list,
tl_list,
dp_list,
cq_list,
ci_list,
uf_list,
np_list,
nt_list,
dt_list,
tn_list,
vs_list,
mg_list,
tt_list)
utils.execute_commands(cmd_args, 'temporal-spatial-long-time')
def conv_spatial():
############################################################################
# TEMPORAL CONVERGENCE TEST FOR ADVECTION
############################################################################
mpi_num_procs, omp_num_threads = utils.parse_args()
prog = 'advection'
num_steps = 8
##############################
# TREE TOLERANCE
##############################
tl_fact = 0.1
tl_init = 1e-1
tl_list = [tl_init*math.pow(tl_fact,float(cnt)) for cnt in range(0,num_steps)]
##############################
# TIME RESOLUTION
##############################
dt_fact = 1
dt_init = 1e-3
dt_list = [dt_init*math.pow(dt_fact,float(cnt)) for cnt in range(0,num_steps)]
T_END = 1.0
tn_fact = 1.0/dt_fact
tn_init = 1#T_END/dt_init
tn_list = [tn_init*math.pow(tn_fact,float(cnt)) for cnt in range(0,num_steps)]
##############################
# TREE DEPTH/POINTS
##############################
dp_list = [15 for cnt in range(0,num_steps)]
num_pnts = 8**(math.floor(math.log(mpi_num_procs,8)+1))
pn_list = [num_pnts for cnt in range(0,num_steps)]
##############################
# PARALLEL
##############################
mpi_num_procs = mpi_num_procs
np_list = [mpi_num_procs for cnt in range(0,num_steps)]
nt = omp_num_threads
nt_list = [nt for cnt in range(0,num_steps)]
mrg_type = 3
mg_list = [mrg_type for cnt in range(0,num_steps)]
##############################
# CHEBYSHEV/CUBIC INTERPOLATION
##############################
cq_list = [14 for cnt in range(0, num_steps)]
ci_list = [True for cnt in range(0, num_steps)]
uf_list = [4 for cnt in range(0, num_steps)]
##############################
# VISUALIZATION
##############################
vtk_save_rate = 0
vs_list = [vtk_save_rate for cnt in range(0,num_steps)]
cmd_args = OrderedDict()
cmd_args = utils.generate_commands(
prog,
pn_list,
tl_list,
dp_list,
cq_list,
ci_list,
uf_list,
np_list,
nt_list,
dt_list,
tn_list,
vs_list,
mg_list)
utils.execute_commands(cmd_args, 'spatial')