def load_seismic_volume(filename, key, dist, use_hdf5):
''' Load the seismic volume, from HDF5 or .dnpy files. '''
# Create context.
context = Context()
if use_hdf5:
print('Loading from .hdf5 file...')
da = load_hdf5_distarray(context, filename, key, dist)
else:
print('Loading from .dnpy files...')
da = load_dnpy_distarray(context, filename)
# Print some stuff about the array.
if False:
dump_distarray_info(da)
return da
def _transport_pre_plugin_list_run(self):
self.n_processes = \
self.exp.meta_data.plugin_list._get_n_processing_plugins()
self.context = Context(targets=self.targets)
closing(self.context).__enter__()
def _transport_initialise(self, options):
# self.exp is not available here
MPI_setup(options) # change this?
with closing(Context()) as context:
self.targets = context.targets # set mpi logging here?
class DistArrayTransport(TransportControl):
def __init__(self):
self.targets = None
self.context = None
self.n_processes = None
self.count = None
self.history = []
def _transport_initialise(self, options):
# self.exp is not available here
MPI_setup(options) # change this?
with closing(Context()) as context:
self.targets = context.targets # set mpi logging here?
def _transport_pre_plugin_list_run(self):
self.n_processes = \
self.exp.meta_data.plugin_list._get_n_processing_plugins()
self.context = Context(targets=self.targets)
closing(self.context).__enter__()
def _transport_pre_plugin(self):
# store all datasets and associated patterns
self.__update_history(self.exp.index)
self.__distribute_arrays(self.exp.index)
def _transport_post_plugin(self):
# if you wish to output datasets that have been removed from the index
# then do that here (data.remove is True)
pass
def _transport_post_plugin_list_run(self):
# convert distarrays to hdf5
for data in self.exp.index['in_data'].values():
name = data.get_name()
fname = self.exp.meta_data.get('filename')[name]
gname = self.exp.meta_data.get('group_name')[name]
data.data.context.save_hdf5(fname, data.data, gname, mode='w')
self.exp._get_experiment_collection()['saver_plugin']\
._open_read_only(data, fname, gname)
closing(self.context).__exit__()
def __update_history(self, data_index):
for dtype, data_dict in data_index.iteritems():
for name, dobj in data_dict.iteritems():
pattern = dobj._get_plugin_data().get_pattern()
self.history.append({name: pattern})
def __distribute_arrays(self, data_index):
if not self.history:
self.__load_data_from_hdf5(data_index['in_data']) # expand this later for other types (or first set should always be treated as hdf5 dataset?)
# - i.e. get data as before directly from file and output to distributed array
else:
self.__redistribute_data(data_index['in_data'])
self.__create_out_data(data_index['out_data'])
def __redistribute_data(self, data_list):
""" Calculate the pattern distributions and if they are not the same\
redistribute.
"""
for data in data_list.values():
patterns = self.__get_distribution_history(data.get_name())
if patterns[0] != patterns[1]:
temp = data.data.toarray()
# *** temporarily creating ndarray
# distarray (create empty dist array and populate?)
distribution = \
Distribution(self.context, data.get_shape(), patterns[-1]) # currently redundant
data.data = self.context.fromarray(temp, patterns[-1])
def __load_data_from_hdf5(self, data_list):
''' Create a distarray from the specified section of the HDF5 file. '''
for data in data_list:
input_file = data.backing_file.filename
dist = self.__calculate_distribution(
data._get_plugin_data().get_pattern())
distribution = \
Distribution(self.context, data.get_shape(), dist=dist)
data.data = self.context.load_hdf5(
input_file, distribution=distribution, key=data.name)
def __create_out_data(self, out_data):
for data in out_data.values():
dist = self.__calculate_distribution(
data._get_plugin_data().get_pattern())
dist = Distribution(self.context, data.get_shape(), dist)
data.data = self.context.zeros(dist, dtype=np.int32)
def __get_distribution_history(self, name):
hist = [i for i in range(len(self.history)) if
self.history[i].keys()[0] == name][-2:]
return [self.__calculate_distribution(
self.history[p].values()[0]) for p in hist]
def __calculate_distribution(self, pattern):
core_dirs = pattern.values()[0]['core_dir']
slice_dirs = pattern.values()[0]['slice_dir']
nDims = len(core_dirs + slice_dirs)
dist = ['n']*nDims
for sl in slice_dirs:
dist[sl] = 'b'
return ''.join(dist)
def _transport_process(self, plugin):
#self.distributed_process(self.process, plugin)
print self.testing
pickler.dump(self)
self.distributed_process()
def distributed_process(self, kernel):
self.context.register(kernel)
iters_key = \
self.context.apply(self.local_process, (), {'kernel': kernel})
return iters_key
def local_process(frames, output, params, kernel):
from distarray.localapi import LocalArray
recon = kernel(frames, output, params)
res = LocalArray(output.distribution, buf=recon)
return proxyize(res) # noqa
def testing(self):
print "running the testing function"
def cli(cmd):
"""
Process command line arguments, set default params, and do_julia_runs.
Parameters
----------
cmd : list of str
sys.argv
"""
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('resolution_list',
metavar='N',
type=int,
nargs='+',
help="resolutions of the Julia set to benchmark (NxN)")
parser.add_argument("-r",
"--repeat",
type=int,
dest='repeat_count',
default=3,
help=("number of repetitions of each unique parameter "
"set, default: 3"))
parser.add_argument("-o",
"--output-filename",
type=str,
dest='output_filename',
default='out.json',
help=("filename to write the json data to."))
parser.add_argument("-k",
"--kernel",
type=str,
default='fancy',
choices=("fancy", "numpy", "cython"),
help=("kernel to use for computation. "
"Options are 'fancy', 'numpy', or 'cython'."))
parser.add_argument(
"-s",
"--scaling",
type=str,
default="strong",
choices=("strong", "weak"),
help=("Kind of scaling test. Options are 'strong' or 'weak'"))
args = parser.parse_args()
## Default parameters
with closing(Context()) as context:
# use all available targets
engine_count_list = list(range(1, len(context.targets) + 1))
dist_list = ['bn', 'cn', 'bb', 'cc']
c_list = [complex(-0.045, 0.45)] # This Julia set has many points inside
# needing all iterations.
re_ax = (-1.5, 1.5)
im_ax = (-1.5, 1.5)
z_max = 2.0
n_max = 100
fn_from_kernel = {
'fancy': fancy_numpy_julia_calc,
'numpy': numpy_julia_calc
}
if args.kernel == 'cython':
from kernel import cython_julia_calc
fn_from_kernel['cython'] = cython_julia_calc
results = do_julia_runs(args.repeat_count,
engine_count_list,
dist_list,
args.resolution_list,
c_list,
re_ax,
im_ax,
z_max,
n_max,
output_filename=args.output_filename,
kernel=fn_from_kernel[args.kernel],
scaling=args.scaling)
def do_julia_runs(repeat_count,
engine_count_list,
dist_list,
resolution_list,
c_list,
re_ax,
im_ax,
z_max,
n_max,
output_filename,
kernel=fancy_numpy_julia_calc,
scaling="strong"):
"""Perform a series of Julia set calculations, and print the results.
Loop over all parameter lists.
Parameters
----------
repeat_count : int
Number of times to repeat each unique parameter set. Later we can take
the average or minimum of these values to reduce noise in the output.
engine_count_list : list of int
List of numbers of engines to test. Example: list(range(1, 5))
dist_list : list of 2-element sequences
List of distribution types to test. Example: ['bn', 'cn', 'bb', 'cc']
resolution_list = list of int
List of resolutions of Julia set to test.
c_list : list of complex
Constants to use to compute Julia set.
Example: [complex(-0.045, 0.45)]
re_ax : 2-tuple of float
Min and max for real axis.
im_ax : 2-tuple of float
Min and max for imaginary axis.
z_max : float
Size of number that we consider as going off to infinity. I think that
2.0 is sufficient to be sure that the point will escape.
n_max : int
Maximum iteration counts. Points in the set will hit this limit, so
increasing this has a large effect on the run-time.
output_filename : str
kernel : function
Kernel to use for computation of the Julia set. Options are 'fancy',
'numpy', or 'cython'.
scaling: str, either "strong" or "weak"
"""
max_engine_count = max(engine_count_list)
with closing(Context()) as context:
# Check that we have enough engines available.
num_engines = len(context.targets)
if max_engine_count > num_engines:
msg = 'Require %d engines, but only %d are available.' % (
max_engine_count, num_engines)
raise ValueError(msg)
# Loop over everything and time the calculations.
results = []
hdr = (('Start', 'End', 'Dist', 'Resolution', 'c', 'Engines', 'Iters'))
print("(n/n_runs: time)", hdr)
# progress stats
n_regular_runs = repeat_count * (len(resolution_list) * len(c_list) *
len(engine_count_list) * len(dist_list))
n_numpy_runs = repeat_count * (len(resolution_list) * len(c_list))
n_runs = n_regular_runs + n_numpy_runs
prog_fmt = "({:d}/{:d}: {:0.3f}s)"
n = 0
for i in range(repeat_count):
for resolution in resolution_list:
dimensions = (resolution, resolution)
for c in c_list:
with closing(Context(targets=[0])) as context:
# numpy julia run
complex_plane = create_complex_plane(
context, dimensions, 'bn', re_ax, im_ax)
result = do_julia_run(context,
'numpy',
dimensions,
c,
complex_plane,
z_max,
n_max,
benchmark_numpy=True,
kernel=kernel)
results.append({h: r for h, r in zip(hdr, result)})
n += 1
print(prog_fmt.format(n, n_runs, result[1] - result[0]),
result)
for engine_count in engine_count_list:
if scaling == "weak":
factor = sqrt(engine_count)
dimensions = (int(floor(resolution * factor)), ) * 2
for dist in dist_list:
targets = list(range(engine_count))
with closing(Context(targets=targets)) as context:
context.register(kernel)
complex_plane = create_complex_plane(
context, dimensions, dist, re_ax, im_ax)
result = do_julia_run(context,
dist,
dimensions,
c,
complex_plane,
z_max,
n_max,
benchmark_numpy=False,
kernel=kernel)
results.append({h: r for h, r in zip(hdr, result)})
n += 1
print(
prog_fmt.format(n, n_runs,
result[1] - result[0]), result)
with open(output_filename, 'wt') as fp:
json.dump(results,
fp,
sort_keys=True,
indent=4,
separators=(',', ': '))
return results
"""
Script to test launching an MPI-only client.
$ mpiexec -np <np> python launch_mpi.py
If exits cleanly, then everything is fine. If exits with an error code, then
there's a problem.
"""
from __future__ import print_function
from distarray.globalapi import Context, Distribution
import numpy as np
c = Context(kind='MPI')
fmt = lambda s: "{:.<25s}:".format(s)
print(fmt("Context"), c)
print(fmt("targets"), c.targets)
if __name__ == '__main__':
size = len(c.targets) * 100
print(fmt("size"), size)
dist = Distribution(c, (size,))
print(fmt("Distribution"), dist)
da = c.ones(dist, dtype=np.int64)
print(fmt("DistArray"), da)
factor = 2
db = da * factor
print(fmt("DistArray"), db)
# ---------------------------------------------------------------------------
# Copyright (C) 2008-2014, IPython Development Team and Enthought, Inc.
# Distributed under the terms of the BSD License. See COPYING.rst.
# ---------------------------------------------------------------------------
"""
Estimate pi using a Monte Carlo method with distarray.
"""
from __future__ import division, print_function
from util import timer
from distarray.globalapi import Context, Distribution, hypot
from distarray.globalapi.random import Random
context = Context()
random = Random(context)
@timer
def calc_pi(n):
"""Estimate pi using distributed NumPy arrays."""
distribution = Distribution(context=context, shape=(n, ))
x = random.rand(distribution)
y = random.rand(distribution)
r = hypot(x, y)
mask = (r < 1)
return 4 * mask.sum().toarray() / n
def main(N):
'''
Needs IPython da cluster running:
dacluster start -n4
'''
def timeit(method):
def timed(*args, **kw):
ts = time.time()
result = method(*args, **kw)
te = time.time()
print 'Time: %2.6f sec' % (te-ts)
return result
return timed
context = Context()
@timeit
def task_np(arr):
return (np.sin(arr) + np.cos(arr)).sum(axis=1) / arr.sum(axis=2)
@timeit
def task_da(arr):
return (da.sin(arr) + da.cos(arr)).sum(axis=1) / arr.sum(axis=2)
N = 400
np_arr = np.random.random_sample(size=(N,N,N))
