def distribute(mat):
"""Distributes the mat from root to individual nodes
The data will be distributed along the first axis, as even as possible.
You should make sure that the matrix is in C-contiguous format.
"""
# quick check
if SIZE == 1:
return mat
if is_root():
shape = mat.shape[1:]
segments = get_segments(mat.shape[0])
dtype = mat.dtype
else:
shape = None
segments = None
dtype = None
shape = COMM.bcast(shape)
dtype = COMM.bcast(dtype)
segments = COMM.bcast(segments)
if is_root():
if mat.flags['C_CONTIGUOUS'] != True:
logging.warning('Warning: mat is not contiguous.')
mat = np.ascontiguousarray(mat)
for i in range(1, SIZE):
safe_send_matrix(mat[segments[i]:segments[i + 1]], dest=i)
data = mat[:segments[1]].copy()
else:
data = np.empty((segments[RANK + 1] - segments[RANK], ) + shape,
dtype=dtype)
safe_recv_matrix(data)
return data
def distribute(mat):
"""Distributes the mat from root to individual nodes
The data will be distributed along the first axis, as even as possible.
You should make sure that the matrix is in C-contiguous format.
"""
# quick check
if SIZE == 1:
return mat
if is_root():
shape = mat.shape[1:]
segments = get_segments(mat.shape[0])
dtype = mat.dtype
else:
shape = None
segments = None
dtype = None
shape = COMM.bcast(shape)
dtype = COMM.bcast(dtype)
segments = COMM.bcast(segments)
if is_root():
if mat.flags['C_CONTIGUOUS'] != True:
logging.warning('Warning: mat is not contiguous.')
mat = np.ascontiguousarray(mat)
for i in range(1,SIZE):
safe_send_matrix(mat[segments[i]:segments[i+1]], dest=i)
data = mat[:segments[1]].copy()
else:
data = np.empty((segments[RANK+1] - segments[RANK],) + shape,
dtype = dtype)
safe_recv_matrix(data)
return data
def load_matrix_multi(filename, N = None):
"""Loads the matrix previously dumped by dump_matrix_multi. The MPI size
might be different. The stored files are in the format
filename-xxxxx-of-xxxxx, which we obtain using glob.
Input:
N: (optional) if given, specify the number of parts the matrix is
separated too. Otherwise, the number is automatically inferred by
listing all the files using regexp matching.
"""
files= glob.glob('%s-?????-of-?????.npy' % (filename))
N = len(files)
logging.debug("Loading the matrix from %d parts" % N)
# we will load the length of the data, and then try to distribute them
# as even as possible.
if RANK == 0:
# the root will first taste each file
sizes = np.array([np.load('%s-%05d-of-%05d.npy' % (filename, i, N),
mmap_mode='r').shape[0]
for i in range(N)])
temp = np.load('%s-%05d-of-%05d.npy' % (filename, 0, N),
mmap_mode='r')
shape = temp.shape[1:]
dtype = temp.dtype
else:
sizes = None
shape = None
dtype = None
barrier()
sizes = COMM.bcast(sizes)
shape = COMM.bcast(shape)
dtype = COMM.bcast(dtype)
total = sizes.sum()
segments = get_segments(total)
# now, each node opens the file that overlaps with its data, and reads
# the contents.
my_start = segments[RANK]
my_end = segments[RANK+1]
my_size = my_end - my_start
mat = np.empty((my_size,) + shape, dtype = dtype)
mat = np.empty((my_size,) + shape)
f_start = 0
f_end = 0
for i, size in enumerate(sizes):
f_end += size
if f_start < my_end and f_end > my_start:
file_mat = np.load('%s-%05d-of-%05d.npy' % (filename, i, N),
mmap_mode='r')
mat[max(f_start - my_start, 0):\
min(f_end - my_start, my_size)] = \
file_mat[max(my_start - f_start,0):\
min(my_end - f_start, size)]
f_start += size
return mat
def load_matrix_multi(filename, N=None):
"""Loads the matrix previously dumped by dump_matrix_multi. The MPI size
might be different. The stored files are in the format
filename-xxxxx-of-xxxxx, which we obtain using glob.
Input:
N: (optional) if given, specify the number of parts the matrix is
separated too. Otherwise, the number is automatically inferred by
listing all the files using regexp matching.
"""
files = glob.glob('%s-?????-of-?????.npy' % (filename))
N = len(files)
logging.debug("Loading the matrix from %d parts" % N)
# we will load the length of the data, and then try to distribute them
# as even as possible.
if RANK == 0:
# the root will first taste each file
sizes = np.array([
np.load('%s-%05d-of-%05d.npy' % (filename, i, N),
mmap_mode='r').shape[0] for i in range(N)
])
temp = np.load('%s-%05d-of-%05d.npy' % (filename, 0, N), mmap_mode='r')
shape = temp.shape[1:]
dtype = temp.dtype
else:
sizes = None
shape = None
dtype = None
barrier()
sizes = COMM.bcast(sizes)
shape = COMM.bcast(shape)
dtype = COMM.bcast(dtype)
total = sizes.sum()
segments = get_segments(total)
# now, each node opens the file that overlaps with its data, and reads
# the contents.
my_start = segments[RANK]
my_end = segments[RANK + 1]
my_size = my_end - my_start
mat = np.empty((my_size, ) + shape, dtype=dtype)
mat = np.empty((my_size, ) + shape)
f_start = 0
f_end = 0
for i, size in enumerate(sizes):
f_end += size
if f_start < my_end and f_end > my_start:
file_mat = np.load('%s-%05d-of-%05d.npy' % (filename, i, N),
mmap_mode='r')
mat[max(f_start - my_start, 0):\
min(f_end - my_start, my_size)] = \
file_mat[max(my_start - f_start,0):\
min(my_end - f_start, size)]
f_start += size
return mat
def distribute_list(source):
"""Distributes the list from root to individual nodes
"""
# quick check
if SIZE == 1:
return source
if is_root():
length = len(source)
if length == 0:
logging.warning("Warning: List has length 0")
else:
length = 0
length = COMM.bcast(length)
if length == 0:
return []
segments = get_segments(length)
if is_root():
for i in range(1, SIZE):
send_list = source[segments[i]:segments[i + 1]]
COMM.send(send_list, dest=i)
data = source[:segments[1]]
del source
else:
data = COMM.recv()
return data
def distribute_list(source):
"""Distributes the list from root to individual nodes
"""
# quick check
if SIZE == 1:
return source
if is_root():
length = len(source)
if length == 0:
logging.warning("Warning: List has length 0")
else:
length = 0
length = COMM.bcast(length)
if length == 0:
return []
segments = get_segments(length)
if is_root():
for i in range(1,SIZE):
send_list = source[segments[i]:segments[i+1]]
COMM.send(send_list, dest=i)
data = source[:segments[1]]
del source
else:
data = COMM.recv()
return data
def elect():
'''elect() randomly chooses a node from all the nodes as the president.
Input:
None
Output:
the rank of the president
'''
president = COMM.bcast(np.random.randint(SIZE))
return president
def elect():
'''elect() randomly chooses a node from all the nodes as the president.
Input:
None
Output:
the rank of the president
'''
president = COMM.bcast(np.random.randint(SIZE))
return president
def agree(decision):
"""agree() makes the decision consistent by propagating the decision of the
root to everyone
"""
return COMM.bcast(decision)
def load_matrix_multi(filename, N=None, name=None):
"""Loads the matrix previously dumped by dump_matrix_multi. The MPI size
might be different. The stored files are in the format
filename-xxxxx-of-xxxxx, which we obtain using glob.
Input:
name: if the input is a hdf5 mat file, specify the name here.
"""
if type(filename) is str:
# we use our default format
files = glob.glob('%s-?????-of-?????.npy' % (filename))
files.sort()
else:
files = list(filename)
N = len(files)
logging.debug("Loading the matrix from %d parts" % N)
# we will load the length of the data, and then try to distribute them
# as even as possible.
if RANK == 0:
# the root will first taste each file
if files[0][-3:] == 'npy':
sizes = np.array(
[np.load(f, mmap_mode='r').shape[0] for f in files])
temp = np.load(files[0], mmap_mode='r')
shape = temp.shape[1:]
dtype = temp.dtype
elif files[0][-3:] == 'mat':
sizes = []
for f in files:
fid = h5py.File(f, 'r')
sizes.append(fid[name].shape[0])
shape = fid[name].shape[1:]
dtype = fid[name].dtype
fid.close()
sizes = np.array(sizes)
else:
sizes = None
shape = None
dtype = None
barrier()
sizes = COMM.bcast(sizes)
shape = COMM.bcast(shape)
dtype = COMM.bcast(dtype)
total = sizes.sum()
segments = get_segments(total)
# now, each node opens the file that overlaps with its data, and reads
# the contents.
my_start = segments[RANK]
my_end = segments[RANK + 1]
my_size = my_end - my_start
mat = np.empty((my_size, ) + shape, dtype=dtype)
mat = np.empty((my_size, ) + shape)
f_start = 0
f_end = 0
for i, size in enumerate(sizes):
f_end += size
if f_start < my_end and f_end > my_start:
if files[i][-3:] == 'npy':
file_mat = np.load(files[i], mmap_mode='r')
mat[max(f_start - my_start, 0):\
min(f_end - my_start, my_size)] = \
file_mat[max(my_start - f_start,0):\
min(my_end - f_start, size)]
elif files[i][-3:] == 'mat':
fid = h5py.File(files[i], 'r')
mat[max(f_start - my_start, 0):\
min(f_end - my_start, my_size)] = \
fid[name][max(my_start - f_start,0):\
min(my_end - f_start, size)]
fid.close()
f_start += size
return mat
def agree(decision):
"""agree() makes the decision consistent by propagating the decision of the
root to everyone
"""
return COMM.bcast(decision)
def load_matrix_multi(filename, N = None, name=None):
"""Loads the matrix previously dumped by dump_matrix_multi. The MPI size
might be different. The stored files are in the format
filename-xxxxx-of-xxxxx, which we obtain using glob.
Input:
name: if the input is a hdf5 mat file, specify the name here.
"""
if type(filename) is str:
# we use our default format
files = glob.glob('%s-?????-of-?????.npy' % (filename))
files.sort()
else:
files = list(filename)
N = len(files)
logging.debug("Loading the matrix from %d parts" % N)
# we will load the length of the data, and then try to distribute them
# as even as possible.
if RANK == 0:
# the root will first taste each file
if files[0][-3:] == 'npy':
sizes = np.array([np.load(f, mmap_mode='r').shape[0]
for f in files])
temp = np.load(files[0], mmap_mode='r')
shape = temp.shape[1:]
dtype = temp.dtype
elif files[0][-3:] == 'mat':
sizes = []
for f in files:
fid = h5py.File(f, 'r')
sizes.append(fid[name].shape[0])
shape = fid[name].shape[1:]
dtype = fid[name].dtype
fid.close()
sizes = np.array(sizes)
else:
sizes = None
shape = None
dtype = None
barrier()
sizes = COMM.bcast(sizes)
shape = COMM.bcast(shape)
dtype = COMM.bcast(dtype)
total = sizes.sum()
segments = get_segments(total)
# now, each node opens the file that overlaps with its data, and reads
# the contents.
my_start = segments[RANK]
my_end = segments[RANK+1]
my_size = my_end - my_start
mat = np.empty((my_size,) + shape, dtype = dtype)
mat = np.empty((my_size,) + shape)
f_start = 0
f_end = 0
for i, size in enumerate(sizes):
f_end += size
if f_start < my_end and f_end > my_start:
if files[i][-3:] == 'npy':
file_mat = np.load(files[i], mmap_mode='r')
mat[max(f_start - my_start, 0):\
min(f_end - my_start, my_size)] = \
file_mat[max(my_start - f_start,0):\
min(my_end - f_start, size)]
elif files[i][-3:] == 'mat':
fid = h5py.File(files[i], 'r')
mat[max(f_start - my_start, 0):\
min(f_end - my_start, my_size)] = \
fid[name][max(my_start - f_start,0):\
min(my_end - f_start, size)]
fid.close()
f_start += size
return mat