def main():
print_header()
print("[main]: initializing...")
print("")
app_data = {}
app_data['app'] = app
app_data['app_name'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune+':
for g_size in [3, 5, 7]:
for t1 in [8, 16, 32, 64, 128, 256]:
for t2 in [8, 16, 32, 64, 128, 256]:
create_lib(build_unsharp, app, app_data, g_size,
[1, t1, t2])
for t in range(0, 0):
print("Running for iteration #", t)
elif app_data['mode'] == 'tune':
print("Tuning")
auto_tune(app_data)
else:
create_lib(build_unsharp, app, app_data)
min_avg = 100000
for i in range(0, 10000000):
min_avg = min(min_avg, unsharp_mask(app_data))
print("minimum average ", min_avg)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune+':
for g_size in [3, 5, 7]:
for t1 in [8, 16, 32, 64, 128, 256]:
for t2 in [8, 16, 32, 64, 128, 256]:
create_lib(build_pyramid, app, app_data, g_size, [t1, t2])
for t in range (0, 0):
print ("Running for iteration #", t)
pyramid_blending(app_data)
elif app_data['mode'] == 'tune' or app_data['mode'] == 'tune_execute':
auto_tune(app_data)
pass
else:
create_lib(build_pyramid, app, app_data)
min_avg = 100000
for r in range (0,5):
min_avg = min (min_avg, pyramid_blending(app_data))
print ("Minimum Average Time ", min_avg)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
#print ('app_data', app_data)
if app_data['mode'] == 'tune+':
for g_size in [3, 5, 7, 10, 15]:
create_lib(build_nlmeans, app, app_data, g_size)
for t in range(0, 5):
print("Running for iteration #", t)
nlmeans(app_data)
elif app_data['mode'] == 'tune':
auto_tune(pipe_data, app_data)
pass
else:
create_lib(build_nlmeans, app, app_data)
for i in range(0, 5):
nlmeans(app_data)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune+':
for g_size in [3, 5, 7]:
for t1 in [8, 16, 32, 64, 128, 256]:
for t2 in [8, 16, 32, 64, 128, 256]:
create_lib(build_harris, app, app_data, g_size, [t1, t2])
for t in range (0, 0):
print ("Running for iteration #", t)
harrispipe(app_data)
elif app_data['mode'] == 'tune':
pass
else:
create_lib(build_harris, app, app_data)
_m = 10000000
for i in range (0,5):
_m = min (_m, harrispipe(app_data))
print ("min time is ", _m)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune+':
for g_size in [3, 5, 7]:
for t1 in [8, 16, 32, 64, 128, 256]:
for t2 in [8, 16, 32, 64, 128, 256]:
create_lib(build_harris, app, app_data, g_size, [t1, t2])
for t in range(0, 0):
print("Running for iteration #", t)
harrispipe(app_data)
elif app_data['mode'] == 'tune':
print("Tuning")
auto_tune(app_data)
else:
create_lib(build_harris, app, app_data)
_m = 10000000
nsamples = 5
for i in range(0, nsamples):
_m = min(_m, harrispipe(app_data))
print("[main] Minimum of averaged times across ", nsamples,
"samples: ", _m, " ms")
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune+':
for g_size in [3, 5, 7]:
for t1 in [8, 16, 32, 64, 128, 256]:
for t2 in [8, 16, 32, 64, 128, 256]:
create_lib(build_campipe, app, app_data, g_size, [t1, t2])
for t in range(0, 0):
print("Running for iteration #", t)
campipe(app_data)
elif app_data['mode'] == 'tune':
auto_tune(app_data)
else:
# create shared lib
create_lib(build_campipe, app, app_data)
min_avg = 100000
nsamples = 5
print("[main] Benchmarking (%d samples)" % nsamples)
for t in range(0, nsamples):
# execute the compiled pipeline
min_avg = min(min_avg, campipe(app_data))
print("[main] Minimum of averaged times across ", nsamples,
"samples: ", min_avg, " ms")
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune+':
for g_size in [3, 5, 7, 9, 11, 200]:
for t1 in [8, 16, 32, 64, 128, 256]:
for t2 in [8, 16, 32, 64, 128, 256]:
create_lib(build_interpolate, app, app_data, g_size,
[1, t1, t2])
for t in range(0, 0):
print("Running for iteration #", t)
interpolate(app_data)
elif app_data['mode'] == 'tune':
pass
else:
create_lib(build_interpolate, app, app_data)
_min_time = 10000
for t in range(0, 5):
_min_time = min(_min_time, interpolate(app_data))
print("Minimum Time = ", _min_time)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune+':
for g_size in [3, 5, 7]:
for t1 in [8, 16, 32, 64, 128, 256]:
for t2 in [8, 16, 32, 64, 128, 256]:
create_lib(build_campipe, app, app_data, g_size, [t1, t2])
for t in range (0, 0):
print ("Running for iteration #", t)
campipe(app_data)
elif app_data['mode'] == 'tune':
auto_tune(app_data)
else:
# create shared lib
create_lib(build_campipe, app, app_data)
min_avg = 100000
for t in range (0, 5):
# execute the compiled pipeline
min_avg = min (min_avg, campipe(app_data))
print ("Minimum Average ", min_avg)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune+':
for g_size in [3, 5, 7, 9, 11, 200]:
for t1 in [8, 16, 32, 64, 128, 256]:
for t2 in [8, 16, 32, 64, 128, 256]:
create_lib(build_interpolate, app, app_data, g_size,
[1, t1, t2])
for t in range(0, 0):
print("Running for iteration #", t)
interpolate(app_data)
elif app_data['mode'] == 'tune':
print("Tuning")
auto_tune(app_data)
else:
create_lib(build_interpolate, app, app_data)
min_avg = 10000
nsamples = 5
print("[main] Benchmarking (%d samples)" % nsamples)
for t in range(0, nsamples):
min_avg = min(min_avg, interpolate(app_data))
print("[main] Minimum of averaged times across ", nsamples, "runs: ",
min_avg, " ms")
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune+':
for g_size in [3, 5, 7]:
for t1 in [8, 16, 32, 64, 128, 256]:
for t2 in [8, 16, 32, 64, 128, 256]:
create_lib(build_bilateral, app, app_data, g_size,
[1, t1, t2])
for t in range(0, 0):
print("Running for iteration #", t)
bilateralgrid(app_data)
elif app_data['mode'] == 'tune':
print("Tuning")
auto_tune(app_data)
else:
create_lib(build_bilateral, app, app_data)
min_avg = 10000
# input ("wait to run amplxe " + str(os.getpid()))
# input ("wwww")
nsamples = 5
print("[main] Benchmarking (%d samples)" % nsamples)
for t in range(0, nsamples):
min_avg = min(min_avg, bilateralgrid(app_data))
print("[main] Minimum of averaged times across ", nsamples,
"samples: ", min_avg, " ms")
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune+':
for g_size in [3, 5, 7]:
for t1 in [8, 16, 32, 64, 128, 256]:
for t2 in [8, 16, 32, 64, 128, 256]:
create_lib(build_bilateral, app, app_data, g_size, [1, t1, t2])
for t in range (0, 0):
print ("Running for iteration #", t)
bilateralgrid(app_data)
elif app_data['mode'] == 'tune':
pass
else:
create_lib(build_bilateral, app, app_data)
_min_time = 10000
input ("wait to run amplxe " + str(os.getpid()))
input ("wwww")
for t in range(0, 5):
_min_time = min (_min_time, bilateralgrid(app_data))
print ("Minimum Time = ", _min_time)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune' or app_data['mode'] == 'tune_execute':
auto_tune(app_data)
pass
else:
create_lib(build_pyramid, app, app_data)
pyramid_blending(app_data)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune':
auto_tune(app_data)
else:
create_lib(build_harris, app, app_data)
harrispipe(app_data)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune':
auto_tune(app_data)
else:
create_lib(build_harris, app, app_data)
harrispipe(app_data)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune':
#auto_tune(pipe_data,app_data)
pass
else:
create_lib(build_bilateral, app, app_data)
bilateralgrid(app_data)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune':
#auto_tune(pipe_data,app_data)
pass
else:
create_lib(build_maxfilter, app, app_data)
maxfilter(app_data)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune':
print("Tuning...")
#auto_tune(app_data)
else:
create_lib(build_laplacian, app, app_data)
for i in range(0, 5):
laplacian(app_data)
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune':
auto_tune(app_data)
else:
# create shared lib
create_lib(build_campipe, app, app_data)
# execute the compiled pipeline
campipe(app_data)
return
def main():
print_header()
app_data = {}
app_data['ROOT'] = ROOT
# init all the required data
init_all(app_data)
print_config(app_data)
app_name = "nas_mg_class_"+app_data['prob_class']
app_data['app'] = app_name
if app_data['mode'] == 'tune':
#app_tune(app_data)
pass
else:
#-------------------------------------------------------------------
# setting up residual norm computation
create_lib(None, "norm", app_data)
# setting up multigrid v-cycle computation
create_lib(build_mg3p, app_name, app_data)
# setting up standalone version of residual computation
create_lib(build_resid, "resid", app_data)
#-------------------------------------------------------------------
init_norm(app_data)
multigrid(app_data)
verify_norm(app_data)
#-------------------------------------------------------------------
return
def main():
print_header()
print("[main]: initializing...")
print("")
app_data = {}
app_data['app'] = app
app_data['app_name'] = app
app_data['ROOT'] = ROOT
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune':
print("Tuning")
auto_tune(app_data)
else:
create_lib(build_unsharp, app, app_data)
unsharp_mask(app_data)
return
def main():
print_header()
print("[main]: initializing...")
print("")
app_data = {}
app_data['app'] = app
app_data['app_name'] = app
app_data['ROOT'] = ROOT
g_sizes = [3, 5, 7]
tile_sizes = [8, 16, 32, 64] #, 128, 256]
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune+':
for g_size in g_sizes:
for t1 in tile_sizes:
for t2 in tile_sizes:
create_lib(build_mfd, app, app_data, g_size, [1, t1, t2])
for t in range(0, 0):
print("Running for iteration #", t)
elif app_data['mode'] == 'tune':
print("Tuning")
auto_tune(app_data)
else:
create_lib(build_mfd, app, app_data)
min_avg = 100000
nsamples = 5
for i in range(0, nsamples):
min_avg = min(min_avg, minifluxdiv(app_data))
print("[main] Minimum of averaged times across ", nsamples,
"samples: ", min_avg, " ms")
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
# init all the required data
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune':
auto_tune(app_data)
else:
#-------------------------------------------------------------------
create_lib(None, "norm", app_data)
create_lib(build_mg_cycle, app_data['cycle_name'], app_data)
#-------------------------------------------------------------------
init_norm(app_data)
multigrid(app_data)
#-------------------------------------------------------------------
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
# init all the required data
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune':
auto_tune(app_data)
else:
#-------------------------------------------------------------------
create_lib(None, "norm", app_data)
create_lib(build_mg_cycle, app_data['cycle_name'], app_data)
#-------------------------------------------------------------------
init_norm(app_data)
multigrid(app_data)
#-------------------------------------------------------------------
return
def main():
print_header()
app_data = {}
app_data['app'] = app
app_data['ROOT'] = ROOT
# init all the required data
init_all(app_data)
print_config(app_data)
if app_data['mode'] == 'tune':
print("Tuning...")
auto_tune(app_data)
else:
#-------------------------------------------------------------------
create_lib(None, "norm", app_data)
create_lib(build_jacobi, app, app_data)
#-------------------------------------------------------------------
init_norm(app_data)
exec_jacobi(app_data)
#-------------------------------------------------------------------
return
