def process_singlecmds(singlecmds, G):
"""Checks the validity of command parameters and creates instances of classes of parameters.
Args:
singlecmds (dict): Commands that can only occur once in the model.
G (class): Grid class instance - holds essential parameters describing the model.
"""
# Check validity of command parameters in order needed
# messages
cmd = '#messages'
if singlecmds[cmd] != 'None':
tmp = singlecmds[cmd].split()
if len(tmp) != 1:
raise CmdInputError(cmd + ' requires exactly one parameter')
if singlecmds[cmd].lower() == 'y':
G.messages = True
elif singlecmds[cmd].lower() == 'n':
G.messages = False
else:
raise CmdInputError(cmd + ' requires input values of either y or n')
# Title
cmd = '#title'
if singlecmds[cmd] != 'None':
G.title = singlecmds[cmd]
if G.messages:
print('Model title: {}'.format(G.title))
# Number of threads (OpenMP) to use
cmd = '#num_threads'
if sys.platform == 'darwin':
os.environ['OMP_WAIT_POLICY'] = 'ACTIVE' # What to do with threads when they are waiting; can drastically effect performance
os.environ['OMP_DYNAMIC'] = 'FALSE'
os.environ['OMP_PROC_BIND'] = 'TRUE' # Bind threads to physical cores
if singlecmds[cmd] != 'None':
tmp = tuple(int(x) for x in singlecmds[cmd].split())
if len(tmp) != 1:
raise CmdInputError(cmd + ' requires exactly one parameter to specify the number of threads to use')
if tmp[0] < 1:
raise CmdInputError(cmd + ' requires the value to be an integer not less than one')
G.nthreads = tmp[0]
os.environ['OMP_NUM_THREADS'] = str(G.nthreads)
elif os.environ.get('OMP_NUM_THREADS'):
G.nthreads = int(os.environ.get('OMP_NUM_THREADS'))
else:
# Set number of threads to number of physical CPU cores, i.e. avoid hyperthreading with OpenMP
G.nthreads = psutil.cpu_count(logical=False)
os.environ['OMP_NUM_THREADS'] = str(G.nthreads)
if G.messages:
machineID, cpuID, osversion = get_machine_cpu_os()
print('Number of threads: {} ({})'.format(G.nthreads, cpuID))
if G.nthreads > psutil.cpu_count(logical=False):
print(Fore.RED + 'WARNING: You have specified more threads ({}) than available physical CPU cores ({}). This may lead to degraded performance.'.format(G.nthreads, psutil.cpu_count(logical=False)) + Style.RESET_ALL)
# Spatial discretisation
cmd = '#dx_dy_dz'
tmp = [float(x) for x in singlecmds[cmd].split()]
if len(tmp) != 3:
raise CmdInputError(cmd + ' requires exactly three parameters')
if tmp[0] <= 0:
raise CmdInputError(cmd + ' requires the x-direction spatial step to be greater than zero')
if tmp[1] <= 0:
raise CmdInputError(cmd + ' requires the y-direction spatial step to be greater than zero')
if tmp[2] <= 0:
raise CmdInputError(cmd + ' requires the z-direction spatial step to be greater than zero')
G.dx = tmp[0]
G.dy = tmp[1]
G.dz = tmp[2]
if G.messages:
print('Spatial discretisation: {:g} x {:g} x {:g}m'.format(G.dx, G.dy, G.dz))
# Domain
cmd = '#domain'
tmp = [float(x) for x in singlecmds[cmd].split()]
if len(tmp) != 3:
raise CmdInputError(cmd + ' requires exactly three parameters')
G.nx = round_value(tmp[0] / G.dx)
G.ny = round_value(tmp[1] / G.dy)
G.nz = round_value(tmp[2] / G.dz)
if G.nx == 0 or G.ny == 0 or G.nz == 0:
raise CmdInputError(cmd + ' requires at least one cell in every dimension')
if G.messages:
print('Domain size: {:g} x {:g} x {:g}m ({:d} x {:d} x {:d} = {:g} cells)'.format(tmp[0], tmp[1], tmp[2], G.nx, G.ny, G.nz, (G.nx * G.ny * G.nz)))
# Estimate memory (RAM) usage
stdoverhead = 70e6
floatarrays = (6 + 6 + 1) * (G.nx + 1) * (G.ny + 1) * (G.nz + 1) * np.dtype(floattype).itemsize # 6 x field arrays + 6 x ID arrays + 1 x solid array
rigidarray = (12 + 6) * (G.nx + 1) * (G.ny + 1) * (G.nz + 1) * np.dtype(np.int8).itemsize
memestimate = stdoverhead + floatarrays + rigidarray
if memestimate > psutil.virtual_memory().total:
print(Fore.RED + 'WARNING: Estimated memory (RAM) required ~{} exceeds {} detected!\n'.format(human_size(memestimate), human_size(psutil.virtual_memory().total, a_kilobyte_is_1024_bytes=True)) + Style.RESET_ALL)
if G.messages:
print('Estimated memory (RAM) required: ~{} ({} detected)'.format(human_size(memestimate), human_size(psutil.virtual_memory().total, a_kilobyte_is_1024_bytes=True)))
# Time step CFL limit (use either 2D or 3D) and default PML thickness
if G.nx == 1:
G.dt = 1 / (c * np.sqrt((1 / G.dy) * (1 / G.dy) + (1 / G.dz) * (1 / G.dz)))
G.dimension = '2D'
G.pmlthickness['xminus'] = 0
G.pmlthickness['xplus'] = 0
elif G.ny == 1:
G.dt = 1 / (c * np.sqrt((1 / G.dx) * (1 / G.dx) + (1 / G.dz) * (1 / G.dz)))
G.dimension = '2D'
G.pmlthickness['yminus'] = 0
G.pmlthickness['yplus'] = 0
elif G.nz == 1:
G.dt = 1 / (c * np.sqrt((1 / G.dx) * (1 / G.dx) + (1 / G.dy) * (1 / G.dy)))
G.dimension = '2D'
G.pmlthickness['zminus'] = 0
G.pmlthickness['zplus'] = 0
else:
G.dt = 1 / (c * np.sqrt((1 / G.dx) * (1 / G.dx) + (1 / G.dy) * (1 / G.dy) + (1 / G.dz) * (1 / G.dz)))
G.dimension = '3D'
# Round down time step to nearest float with precision one less than hardware maximum. Avoids inadvertently exceeding the CFL due to binary representation of floating point number.
G.dt = round_value(G.dt, decimalplaces=d.getcontext().prec - 1)
if G.messages:
print('Time step (at {} CFL limit): {:g} secs'.format(G.dimension, G.dt))
# Time step stability factor
cmd = '#time_step_stability_factor'
if singlecmds[cmd] != 'None':
tmp = tuple(float(x) for x in singlecmds[cmd].split())
if len(tmp) != 1:
raise CmdInputError(cmd + ' requires exactly one parameter')
if tmp[0] <= 0 or tmp[0] > 1:
raise CmdInputError(cmd + ' requires the value of the time step stability factor to be between zero and one')
G.dt = G.dt * tmp[0]
if G.messages:
print('Time step (modified): {:g} secs'.format(G.dt))
# Time window
cmd = '#time_window'
tmp = singlecmds[cmd].split()
if len(tmp) != 1:
raise CmdInputError(cmd + ' requires exactly one parameter to specify the time window. Either in seconds or number of iterations.')
tmp = tmp[0].lower()
# If number of iterations given
try:
tmp = int(tmp)
G.timewindow = (tmp - 1) * G.dt
G.iterations = tmp
# If real floating point value given
except:
tmp = float(tmp)
if tmp > 0:
G.timewindow = tmp
G.iterations = round_value((tmp / G.dt)) + 1
else:
raise CmdInputError(cmd + ' must have a value greater than zero')
if G.messages:
print('Time window: {:g} secs ({} iterations)'.format(G.timewindow, G.iterations))
# PML
cmd = '#pml_cells'
if singlecmds[cmd] != 'None':
tmp = singlecmds[cmd].split()
if len(tmp) != 1 and len(tmp) != 6:
raise CmdInputError(cmd + ' requires either one or six parameters')
if len(tmp) == 1:
for key in G.pmlthickness.keys():
G.pmlthickness[key] = int(tmp[0])
else:
G.pmlthickness['xminus'] = int(tmp[0])
G.pmlthickness['yminus'] = int(tmp[1])
G.pmlthickness['zminus'] = int(tmp[2])
G.pmlthickness['xplus'] = int(tmp[3])
G.pmlthickness['yplus'] = int(tmp[4])
G.pmlthickness['zplus'] = int(tmp[5])
if 2 * G.pmlthickness['xminus'] >= G.nx or 2 * G.pmlthickness['yminus'] >= G.ny or 2 * G.pmlthickness['zminus'] >= G.nz or 2 * G.pmlthickness['xplus'] >= G.nx or 2 * G.pmlthickness['yplus'] >= G.ny or 2 * G.pmlthickness['zplus'] >= G.nz:
raise CmdInputError(cmd + ' has too many cells for the domain size')
# src_steps
cmd = '#src_steps'
if singlecmds[cmd] != 'None':
tmp = singlecmds[cmd].split()
if len(tmp) != 3:
raise CmdInputError(cmd + ' requires exactly three parameters')
G.srcsteps[0] = round_value(float(tmp[0]) / G.dx)
G.srcsteps[1] = round_value(float(tmp[1]) / G.dy)
G.srcsteps[2] = round_value(float(tmp[2]) / G.dz)
if G.messages:
print('Simple sources will step {:g}m, {:g}m, {:g}m for each model run.'.format(G.srcsteps[0] * G.dx, G.srcsteps[1] * G.dy, G.srcsteps[2] * G.dz))
# rx_steps
cmd = '#rx_steps'
if singlecmds[cmd] != 'None':
tmp = singlecmds[cmd].split()
if len(tmp) != 3:
raise CmdInputError(cmd + ' requires exactly three parameters')
G.rxsteps[0] = round_value(float(tmp[0]) / G.dx)
G.rxsteps[1] = round_value(float(tmp[1]) / G.dy)
G.rxsteps[2] = round_value(float(tmp[2]) / G.dz)
if G.messages:
print('All receivers will step {:g}m, {:g}m, {:g}m for each model run.'.format(G.rxsteps[0] * G.dx, G.rxsteps[1] * G.dy, G.rxsteps[2] * G.dz))
# Excitation file for user-defined source waveforms
cmd = '#excitation_file'
if singlecmds[cmd] != 'None':
tmp = singlecmds[cmd].split()
if len(tmp) != 1:
raise CmdInputError(cmd + ' requires exactly one parameter')
excitationfile = tmp[0]
# See if file exists at specified path and if not try input file directory
if not os.path.isfile(excitationfile):
excitationfile = os.path.abspath(os.path.join(G.inputdirectory, excitationfile))
# Get waveform names
with open(excitationfile, 'r') as f:
waveformIDs = f.readline().split()
# Read all waveform values into an array
waveformvalues = np.loadtxt(excitationfile, skiprows=1, dtype=floattype)
for waveform in range(len(waveformIDs)):
if any(x.ID == waveformIDs[waveform] for x in G.waveforms):
raise CmdInputError('Waveform with ID {} already exists'.format(waveformIDs[waveform]))
w = Waveform()
w.ID = waveformIDs[waveform]
w.type = 'user'
if len(waveformvalues.shape) == 1:
w.uservalues = waveformvalues[:]
else:
w.uservalues = waveformvalues[:, waveform]
if G.messages:
print('User waveform {} created.'.format(w.ID))
G.waveforms.append(w)
# Parse command line arguments
parser = argparse.ArgumentParser(description='Plots execution times and speedup factors from benchmarking models run with different numbers of threads. Results are read from a NumPy archive.', usage='cd gprMax; python -m tests.benchmarking.plot_benchmark numpyfile')
parser.add_argument('baseresult', help='name of NumPy archive file including path')
parser.add_argument('--otherresults', default=None, help='list of NumPy archives file including path', nargs='+')
args = parser.parse_args()
# Load base result
baseresult = np.load(args.baseresult)
# Get machine/CPU/OS details
try:
machineIDlong = str(baseresult['machineID'])
#machineIDlong = 'Dell PowerEdge R630; Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz; Linux (3.10.0-327.18.2.el7.x86_64)' # Use to manually describe machine
machineID = machineIDlong.split(';')[0]
except KeyError:
machineID, cpuID, osversion = get_machine_cpu_os()
machineIDlong = machineID + '; ' + cpuID + '; ' + osversion
print('MachineID: {}'.format(machineIDlong))
# Base result info
print('Model: {}'.format(args.baseresult))
for thread in range(len(baseresult['threads'])):
print('{} thread(s): {:g} s'.format(baseresult['threads'][thread], baseresult['benchtimes'][thread]))
baseplotlabel = os.path.splitext(os.path.split(args.baseresult)[1])[0] + '.in'
# Load other results and info
otherresults = []
otherplotlabels = []
if args.otherresults is not None:
for i, result in enumerate(args.otherresults):
otherresults.append(np.load(result))