def sample_cutoff(test):
print "** Sampling for Real Space"
samples_dir = Config.get_test_samples_dir(Config.test)
Config.create_dir(samples_dir)
lmp_config = PPPMUtilities.LammpsPPPMConfig()
lmp_config.npart = test.npart
lmp_config.timesteps = test.timesteps
lmp_config.ewald = real_space_sampling_params["ewald"]
# cutoff set in the loop
lmp_config.order = real_space_sampling_params["order"]
lmp_config.grid = real_space_sampling_params["grid"]
lmp_config.mixing = test.mixing
lmp_config.diff = test.diff_mode
#
cutoffs = get_sampled_cutoffs(test.npart, test.domain, test.box)
timings = [None for _ in cutoffs]
ewald = rsp["ewald"]
P = rsp["order"]
grid = rsp["grid"]
for i, c in enumerate(
get_sampled_cutoffs(test.npart, test.domain, test.box)):
try:
t = extract_timings_from_log( os.path.join(samples_dir, "Output-%s-%s-%.2f-%.2f-%d-%dx%dx%d" % \
(test.diff_mode, test.mixing, ewald, c, P, grid[0], grid[1], grid[2])) )[1]
timings[i] = t
except Exception, e:
pass
def fit_neigh(test):
samples_dir = Config.get_test_samples_dir(Config.test)
cutoffs, timings = [], []
ewald = rsp["ewald"]
P = rsp["order"]
grid = rsp["grid"]
for c in get_sampled_cutoffs(test.npart, test.domain, test.box):
try:
t = extract_timings_from_log( os.path.join(samples_dir, "Output-%s-%s-%.2f-%.2f-%d-%dx%dx%d" % \
(test.diff_mode, test.mixing, ewald, c, P, grid[0], grid[1], grid[2])) )[3]
cutoffs.append(c)
timings.append(t)
except Exception, e:
pass
def fit_samples(npart, domain, box, mixing, np, nn, ts, diff):
cwd = os.getcwd()
os.chdir(Config.get_test_samples_dir(Config.test))
fits = []
fit_cutoff(Config.test)
fits.append(get_real_space_prediction)
fit_neigh(Config.test)
fits.append(get_neigh_prediction)
# kspace samples
ewald = ksp["ewald"]
cutoffs = get_sampled_cutoffs(npart, domain, box)
_c.extend((cutoffs[0], cutoffs[-1]))
Ps = get_sampled_Ps(npart, domain, box)
grids = get_sampled_grid_sizes(npart, domain, box)
for i, cutoff in enumerate([cutoffs[0], cutoffs[-1]]):
_kspace_piecewise[i].extend(
[None for _ in range(Ps[0] - Config.order_values[0])])
for P in Ps:
timings = []
gs = []
for grid in grids:
try:
t = extract_timings_from_log(
"Output-%s-%s-%.2f-%.2f-%d-%dx%dx%d" %
(diff, mixing, ewald, cutoff, P, grid[0], grid[1],
grid[2]))[2]
gs.append(grid)
timings.append(t)
except Exception, e:
pass
try:
x, y = filter_data([mult(g_) for g_ in gs], timings)
bounds, fit_vars = topdown.top_down(numpy.array(x),
numpy.array(y),
kspace_fitting_f)
_kspace_piecewise[i].append((bounds, fit_vars))
except ValueError, e:
print e
print >> sys.stderr, "Cannot fit. Infs or Nans found."
(p, g) = (float('NaN'), float('NaN'))
raise Exception
def sample(lmp_config, target, value):
if target in ("cutoff", "neigh"):
lmp_config.cutoff = value
if target == "cutoff":
log_pos = 1
else:
log_pos = 3
print "Sampling cutoff:", value
elif target == "kspace": # gonna need to deal with grid and mult(grid)
lmp_config.grid = value
log_pos = 2
print "Sampling grid:", value
#return
# Set paths and write lammps config file
lmp_config.set_file_paths(Config.get_test_samples_dir(Config.test))
lmp_config.write_config_file(lmp_config.infile)
#
done = False
attempt = 1
while not done and attempt <= 10:
try:
os.remove(lmp_config.outfile)
except OSError:
pass
try:
#subprocess32.check_call("mpirun -np %d $HOME/MD-libs/lammps-22Jan14/src/lmp_openmpi -l /dev/null < %s > %s" % (Config.test.nprocesses, lmp_config.infile, lmp_config.outfile), shell=True, timeout=1500)
subprocess32.check_call(
"$MPIEXEC -np %d $HOME/MD-libs/lammps-22Jan14/src/lmp_openmpi -l /dev/null < %s > %s"
% (Config.test.nprocesses, lmp_config.infile,
lmp_config.outfile),
shell=True,
timeout=1500)
t = extract_timings_from_log(lmp_config.outfile)[log_pos]
if not math.isnan(t):
done = True
except subprocess32.TimeoutExpired:
pass
attempt += 1
if done:
return t
else:
raise Exception
def sample_kspace(test):
print "** Sampling for KSpace"
samples_dir = Config.get_test_samples_dir(Config.test)
Config.create_dir(samples_dir)
lmp_config = PPPMUtilities.LammpsPPPMConfig()
lmp_config.npart = test.npart
lmp_config.timesteps = test.timesteps
lmp_config.ewald = kspace_sampling_params["ewald"]
lmp_config.mixing = test.mixing
lmp_config.diff = test.diff_mode
#
grids = get_sampled_grid_sizes(test.npart, test.domain, test.box)
grids_cont = [mult(g) for g in grids]
Ps = get_sampled_Ps(test.npart, test.domain, test.box)
#
cutoffs = get_sampled_cutoffs(test.npart, test.domain, test.box)
c_min = cutoffs[0]
c_max = cutoffs[-1]
ewald = kspace_sampling_params["ewald"]
for c in [c_min, c_max]:
lmp_config.cutoff = c
for P in Ps:
lmp_config.order = P
timings = [None for _ in grids]
for i, g in enumerate(grids):
try:
t = extract_timings_from_log( os.path.join(samples_dir, "Output-%s-%s-%.2f-%.2f-%d-%dx%dx%d" % \
(test.diff_mode, test.mixing, ewald, c, P, g[0], g[1], g[2])) )[2]
if not math.isnan(t):
timings[i] = t
except Exception, e:
print c, P, g, e
pass
dynamic_sampling(lmp_config, "kspace", grids_cont, grids, timings,
kspace_fitting_f)