'mean_dE', 'std_dE', 'mean_dt', 'std_dt')
test_string += '{:+10.10e}\t{:+10.10e}\t{:+10.10e}\t{:+10.10e}\n'.format(
np.mean(my_beam.dE), np.std(my_beam.dE), np.mean(my_beam.dt),
np.std(my_beam.dt))
# ACCELERATION MAP-------------------------------------------------------------
# import argparse
# parser = argparse.ArgumentParser()
# parser.add_argument('-g', default = False, action='store_true')
# parser.add_argument('-d', default = False, action='store_true')
# args = parser.parse_args()
# print(args)
if (args['gpu'] == 1):
import blond.utils.bmath as bm
bm.use_gpu()
total_induced_voltage.use_gpu()
ring_RF_section.use_gpu()
slice_beam.use_gpu()
#map_ = [total_induced_voltage] + [ring_RF_section] + [slice_beam] #+ [bunchmonitor] + [plots]
map_ = [ring_RF_section] + [slice_beam]
# TRACKING + PLOTS-------------------------------------------------------------
for i in range(1, n_turns + 1):
# print(i)
for m in map_:
m.track()
# # Plots
filename = args['monitorfile']
else:
filename = 'monitorfiles/ps-t{}-p{}-b{}-sl{}-approx{}-prec{}-r{}-m{}-se{}-w{}'.format(
n_iterations, n_particles, n_bunches, n_slices, approx,
args['precision'], n_turns_reduce, args['monitor'], seed,
worker.workers)
slicesMonitor = SlicesMonitor(filename=filename,
n_turns=np.ceil(n_iterations /
args['monitor']),
profile=profile,
rf=rf_params,
Nbunches=n_bunches)
if worker.hasGPU:
bm.use_gpu(gpu_id=worker.gpu_id)
PS_longitudinal_intensity.use_gpu()
tracker.use_gpu()
bm.enable_gpucache()
print(
f'Glob rank: [{worker.rank}], Node rank: [{worker.noderank}], Intra rank: [{worker.intrarank}], GPU rank: [{worker.gpucommrank}], hasGPU: {worker.hasGPU}'
)
worker.initDLB(args['loadbalance'], n_iterations)
worker.sync()
timing.reset()
start_t = time.time()
# for i in range(n_iterations):
for turn in range(n_iterations):
filename = 'monitorfiles/lhc-t{}-p{}-b{}-sl{}-approx{}-prec{}-r{}-m{}-se{}-w{}'.format(
n_iterations, n_particles, n_bunches, n_slices, approx, args['precision'],
n_turns_reduce, args['monitor'], seed, worker.workers)
slicesMonitor = SlicesMonitor(filename=filename,
n_turns=np.ceil(
n_iterations / args['monitor']),
profile=profile,
rf=rf,
Nbunches=n_bunches)
# bm.GPU(args['gpu'])
if worker.hasGPU:
# Here we pass the gpu_id, if this is < 0, means don't use the gpu
bm.use_gpu(gpu_id=worker.gpu_id)
tracker.use_gpu()
totVoltage.use_gpu()
beam.use_gpu()
PL.use_gpu()
bm.enable_gpucache()
print(f'Glob rank: [{worker.rank}], Node rank: [{worker.noderank}], Intra rank: [{worker.intrarank}], GPU rank: [{worker.gpucommrank}], hasGPU: {worker.hasGPU}')
worker.initDLB(args['loadbalance'], n_iterations)
worker.sync()
timing.reset()
start_t = time.time()
for turn in range(n_iterations):
np.std(my_beam.dt))
# ACCELERATION MAP-------------------------------------------------------------
map_ = [tot_vol] + [ring_RF_section] + [slice_beam
] #+ [bunchmonitor] + [plots]
map_freq = [tot_vol_freq] + [ring_RF_section_freq] + [slice_beam_freq] \
#+ [bunchmonitor_freq] + [plots_freq]
map_res = [tot_vol_res] + [ring_RF_section_res] + [slice_beam_res] \
#+ [bunchmonitor_res] + [plots_res]
if (args['gpu'] == 1):
import blond.utils.bmath as bm
bm.use_gpu()
tot_vol.use_gpu()
tot_vol_freq.use_gpu()
tot_vol_res.use_gpu()
ring_RF_section.use_gpu()
ring_RF_section_freq.use_gpu()
ring_RF_section_res.use_gpu()
slice_beam.use_gpu()
slice_beam_freq.use_gpu()
slice_beam_res.use_gpu()
# long_tracker_1.use_gpu()
# long_tracker_2.use_gpu()
# slice_beam.use_gpu()
# long_tracker_tot.use_gpu()
# TRACKING + PLOTS-------------------------------------------------------------
