def init_from_file(filename, parser=int):
"""Instantiate a distributed tree from a file
Parameters
----------
filename : str
The name of the file that contains the PTree to instantiate
parser : callable, optional
A function that transforms a string into a specific type.
By default, string to int
"""
filename = filename + "." + str(PID)
def __parser_couple(s):
s = s.replace("(", "")
s = s.replace(")", "")
ss = s.split(",")
return int(ss[0]), int(ss[1])
p = PTree()
content = SList([])
with open(filename, "r") as f:
count_line = 0
for line in f:
if line.strip()[0] == '#':
continue
# __distribution: PID -> nb of segments
# __global_index: num seg -> (start, offset)
if count_line == 0: # Get the distribution
p.distribution = SList.from_str(line)
p.start_index = p.distribution.scanl(
lambda x, y: x + y, 0)[PID]
p.nb_segs = p.distribution[PID]
elif count_line == 1: # Get the global_index
p.global_index = SList.from_str(line,
parser=__parser_couple)
else: # Get the content
content.extend(Segment.from_str(line, parser=parser))
count_line = count_line + 1
p.content = content
return p
class PTree:
# pylint: disable=too-many-instance-attributes
"""A class used to represent a distributed tree
Attributes
----------
__distribution: PID -> nb of segments
__global_index: num seg -> (start, offset)
__start_index: index of first index for the current PID in global_index
__nb_segs: nb of indexes for the current PID in global_index
__content: concatenation of the segments contained in the current instance
"""
def __init__(self, lt=None):
self.__distribution = SList([])
self.__global_index = SList([])
self.__start_index = 0
self.__nb_segs = 0
self.__content = SList([])
if lt is not None:
(distribution, global_index) = distribute_tree(lt, NPROCS)
self.__distribution = distribution
self.__global_index = global_index
self.__start_index = distribution.scanl(lambda x, y: x + y, 0)[PID]
self.__nb_segs = distribution[PID]
for i_seg in range(self.__start_index,
self.__start_index + self.__nb_segs):
self.__content.extend(lt[i_seg])
@property
def distribution(self):
"""Distribution getter"""
return self.__distribution
@property
def global_index(self):
"""Global index getter"""
return self.__global_index
@property
def start_index(self):
"""Start index getter"""
return self.__start_index
@property
def nb_segs(self):
"""Nb segs getter"""
return self.__nb_segs
@property
def content(self):
"""Content getter"""
return self.__content
@distribution.setter
def distribution(self, value):
"""Distribution setter"""
self.__distribution = value
@global_index.setter
def global_index(self, value):
"""Global index setter"""
self.__global_index = value
@start_index.setter
def start_index(self, value):
"""Start index setter"""
self.__start_index = value
@nb_segs.setter
def nb_segs(self, value):
"""Nb segs setter"""
self.__nb_segs = value
@content.setter
def content(self, value):
"""Content setter"""
self.__content = value
def __eq__(self, other):
if isinstance(other, PTree):
return self.__distribution == other.distribution \
and self.__global_index == other.distribution \
and self.__start_index == other.start_index \
and self.__nb_segs == other.nb_segs \
and self.__content == other.content
return False
@staticmethod
def init(pt, content):
"""Factory for distributed tree.
Creates a PTree from a already existing one, but changing its content
Parameters
----------
pt : :obj:`PTree`
A parallel tree that we want to copy the distribution
content : :obj:`SList`
The content of the resulting PTree
"""
p = PTree()
p.distribution = pt.distribution
p.global_index = pt.global_index
p.start_index = pt.start_index
p.nb_segs = pt.nb_segs
p.content = content
return p
@staticmethod
def init_from_file(filename, parser=int):
"""Instantiate a distributed tree from a file
Parameters
----------
filename : str
The name of the file that contains the PTree to instantiate
parser : callable, optional
A function that transforms a string into a specific type.
By default, string to int
"""
filename = filename + "." + str(PID)
def __parser_couple(s):
s = s.replace("(", "")
s = s.replace(")", "")
ss = s.split(",")
return int(ss[0]), int(ss[1])
p = PTree()
content = SList([])
with open(filename, "r") as f:
count_line = 0
for line in f:
if line.strip()[0] == '#':
continue
# __distribution: PID -> nb of segments
# __global_index: num seg -> (start, offset)
if count_line == 0: # Get the distribution
p.distribution = SList.from_str(line)
p.start_index = p.distribution.scanl(
lambda x, y: x + y, 0)[PID]
p.nb_segs = p.distribution[PID]
elif count_line == 1: # Get the global_index
p.global_index = SList.from_str(line,
parser=__parser_couple)
else: # Get the content
content.extend(Segment.from_str(line, parser=parser))
count_line = count_line + 1
p.content = content
return p
def __str__(self):
return "PID[" + str(PID) + "]:\n" + \
" global_index: " + str(self.__global_index) + "\n" + \
" distribution: " + str(self.__distribution) + "\n" + \
" nb_segs: " + str(self.__nb_segs) + "\n" + \
" start_index: " + str(self.__start_index) + "\n" + \
" content: " + str(self.__content) + "\n"
def browse(self):
"""Browse the linearized distributed tree contained in the current processor
"""
res = "PID[" + str(PID) + "] "
for (start, offset) in \
self.__global_index[self.__start_index: self.__start_index + self.__nb_segs]:
seg = Segment(self.__content[start:start + offset])
res = res + "\n " + str(seg)
return res
def map(self, kl, kn):
"""Map skeleton for distributed tree
Parameters
----------
kl : callable
Function to apply to every leaf value of the current instance
kn : callable
Function to apply to every node value of the current instance
"""
content = SList([None] * self.__content.length())
logger.debug('[START] PID[%s] map skeleton', PID)
for (start, offset) in \
self.__global_index[self.__start_index: self.__start_index + self.__nb_segs]:
logger.debug('[START] PID[%s] map_local from %s to %s', PID, start,
start + offset)
content[start:start + offset] = Segment(
self.__content[start:start + offset]).map_local(kl, kn)
logger.debug('[END] PID[%s] map_local from %s to %s', PID, start,
start + offset)
res = PTree.init(self, content)
logger.debug('[END] PID[%s] map skeleton', PID)
return res
# pylint: disable=too-many-arguments
def reduce(self, k, phi, psi_n, psi_l, psi_r):
"""Reduce skeleton for distributed tree
The parameters must respect these equalities (closure property):
* k(l, b, r) = psi_n(l, phi(b), r)
* psi_n(psi_n(value, l, y), b, r) = psi_n(value, psi_l(l,b,r), y)
* psi_n(l, b, psi_n(value, r, y)) = psi_n(value, psi_r(l,b,r), y)
Parameters
----------
k : callable
The function used to reduce a BTree into a single value
phi : callable
A function used to respect the closure property to allow partial computation
psi_n : callable
A function used to respect the closure property to make partial computation
psi_l : callable
A function used to respect the closure property to make partial computation on the left
psi_r : callable
A function used to respect the closure property to make partial computation on the right
"""
logger.debug('[START] PID[%s] reduce skeleton', PID)
# Step 1 : Local Reduction
gt = Segment([None] * self.__nb_segs)
i = 0
for (start, offset) in \
self.__global_index[self.__start_index: self.__start_index + self.__nb_segs]:
logger.debug('[START] PID[%s] reduce_local from %s to %s', PID,
start, start + offset)
gt[i] = Segment(self.__content[start:start + offset]).reduce_local(
k, phi, psi_l, psi_r)
logger.debug('[END] PID[%s] reduce_local from %s to %s', PID,
start, start + offset)
i = i + 1
# Step 2 : Gather local Results
self.__gather_local_result(gt, i, TAG_COMM_UACC_1)
# Step 3 : Global Reduction
par.at_root(lambda: logger.debug('[START] PID[%s] reduce_global', PID))
res = gt.reduce_global(psi_n) if PID == 0 else None
par.at_root(lambda: logger.debug('[END] PID[%s] reduce_global', PID))
logger.debug('[END] PID[%s] reduce skeleton', PID)
return res
def __local_upwards_accumulation(self, k, phi, psi_l, psi_r):
gt = Segment([None] * self.__nb_segs)
lt2 = SList([None] * self.__nb_segs)
i = 0
for (start, offset) in \
self.__global_index[self.__start_index: self.__start_index + self.__nb_segs]:
logger.debug('[START] PID[%s] uacc_local from %s to %s', PID,
start, start + offset)
(top,
res) = Segment(self.__content[start:start + offset]).uacc_local(
k, phi, psi_l, psi_r)
logger.debug('[END] PID[%s] uacc_local from %s to %s', PID, start,
start + offset)
gt[i] = top
lt2[i] = res
i = i + 1
return i, gt, lt2
@staticmethod
def __gather_local_result(gt, i, tag):
if PID == 0:
for iproc in range(1, NPROCS):
logger.debug('[START] PID[%s] reception local from %s', PID, i)
gt.extend(COMM.recv(source=iproc, tag=tag)['c'])
logger.debug('[END] PID[%s] reception local from %s', PID, i)
else:
logger.debug('[START] PID[%s] emission local to %s', PID, 0)
COMM.send({'c': gt}, dest=0, tag=tag)
logger.debug('[END] PID[%s] emission local to %s', PID, 0)
@staticmethod
def __global_upwards_accumulation(psi_n, gt):
gt2 = None
if PID == 0:
par.at_root(
lambda: logger.debug('[START] PID[%s] uacc_global', PID))
gt2 = gt.uacc_global(psi_n)
for i, _ in enumerate(gt2):
if gt2[i].is_node():
gt2[i] = TaggedValue((gt2.get_left(i).get_value(),
gt2.get_right(i).get_value()),
gt2[i].get_tag())
par.at_root(lambda: logger.debug('[END] PID[%s] uacc_global', PID))
return gt2
def __distribute_global_result(self, gt2, tag):
start = 0
if PID == 0:
for iproc in range(NPROCS):
iproc_off = self.__distribution[iproc]
if iproc != 0:
logger.debug('[START] PID[%s] emission global to %s', PID,
iproc)
COMM.send({'g': gt2[start:start + iproc_off]},
dest=iproc,
tag=tag)
logger.debug('[END] PID[%s] emission global to %s', PID,
iproc)
start = start + iproc_off
else:
logger.debug('[START] PID[%s] reception global from %s', PID, 0)
gt2 = COMM.recv(source=0, tag=tag)['g']
logger.debug('[END] PID[%s] reception global from %s', PID, 0)
return gt2
def __local_updates(self, gt, gt2, lt2, k):
content = SList([None] * self.__content.length())
for i in range(
len(self.__global_index[self.__start_index:self.__start_index +
self.__nb_segs])):
(start, offset
) = self.__global_index[self.__start_index:self.__start_index +
self.__nb_segs][i]
logger.debug('[START] PID[%s] uacc_update from %s to %s', PID,
start, start + offset)
if gt[i].is_node():
(lc, rc) = gt2[i].get_value()
val = Segment(self.__content[start:start +
offset]).uacc_update(
lt2[i], k, lc, rc)
else:
val = lt2[i]
logger.debug('[END] PID[%s] uacc_update from %s to %s', PID, start,
start + offset)
content[start:start + offset] = val
return content
def uacc(self, k, phi, psi_n, psi_l, psi_r):
"""Upward accumulation skeleton for distributed tree
The parameters must respect these equalities (closure property):
* k(l, b, r) = psi_n(l, phi(b), r)
* psi_n(psi_n(value, l, y), b, r) = psi_n(value, psi_l(l,b,r), y)
* psi_n(l, b, psi_n(value, r, y)) = psi_n(value, psi_r(l,b,r), y)
Parameters
----------
k : callable
The function used to reduce a BTree into a single value
phi : callable
A function used to respect the closure property to allow partial computation
psi_n : callable
A function used to respect the closure property to make partial computation
psi_l : callable
A function used to respect the closure property to make partial computation on the left
psi_r : callable
A function used to respect the closure property to make partial computation on the right
"""
logger.debug('[START] PID[%s] uAcc skeleton', PID)
assert self.__distribution != []
# Step 1 : Local Upwards Accumulation
i, gt, lt2 = self.__local_upwards_accumulation(k, phi, psi_l, psi_r)
# Step 2 : Gather local Results
self.__gather_local_result(gt, i, TAG_COMM_UACC_1)
# Step 3 : Global Upward Accumulation
gt2 = self.__global_upwards_accumulation(psi_n, gt)
# Step 4 : Distributing Global Result
gt2 = self.__distribute_global_result(gt2, TAG_COMM_UACC_2)
# Step 5 : Local Updates
content = self.__local_updates(gt, gt2, lt2, k)
res = PTree.init(self, content)
logger.debug('[END] PID[%s] uAcc skeleton', PID)
return res
def dacc(self, gl, gr, c, phi_l, phi_r, psi_u, psi_d):
"""Downward accumulation skeleton for distributed tree
The parameters must respect these equalities (closure property):
* gl(c, b) = psi_d(c, phi_l(b))
* gr(c, b) = psi_d(c, phi_r(b))
* psi_d(psi_d(c, b), a) = psi_d(c, psi_u(b,a))
Parameters
---------
gl : callable
The function used to make an accumulation to the left children in a binary tree
gr : callable
The function used to make an accumulation to the right children in a binary tree
c
Initial value of accumulation
phi_l : callable
A function used to respect the closure property to allow partial computation
on the left
phi_r : callable
A function used to respect the closure property to allow partial computation
on the right
psi_d : callable
A function used to respect the closure property to make partial downward accumulation
psi_u : callable
A function used to respect the closure property to make partial computation
"""
logger.debug('[START] PID[%s] dAcc skeleton', PID)
# Step 1 : Computing Local Intermediate Values
gt = Segment([None] * self.__nb_segs)
i = 0
for (start, offset) in \
self.__global_index[self.__start_index: self.__start_index + self.__nb_segs]:
seg = Segment(self.__content[start:start + offset])
logger.debug('[START] PID[%s] dacc_path from %s to %s', PID, start,
start + offset)
if seg.has_critical():
gt[i] = seg.dacc_path(phi_l, phi_r, psi_u)
else:
gt[i] = TaggedValue(seg[0].get_value(), "L")
logger.debug('[END] PID[%s] dacc_path from %s to %s', PID, start,
start + offset)
i = i + 1
# Step 2 : Gather Local Results
self.__gather_local_result(gt, i, TAG_COMM_DACC_1)
# Step 3 : Global Downward Accumulation
par.at_root(lambda: logger.debug('[START] PID[%s] dacc_global', PID))
gt2 = (gt.dacc_global(psi_d, c) if PID == 0 else None)
par.at_root(lambda: logger.debug('[END] PID[%s] dacc_global', PID))
# Step 4 : Distributing Global Result
gt2 = self.__distribute_global_result(gt2, TAG_COMM_DACC_2)
# Step 5 : Local Downward Accumulation
content = SList([None] * self.__content.length())
for i in range(
len(self.__global_index[self.__start_index:self.__start_index +
self.__nb_segs])):
(start, offset
) = self.__global_index[self.__start_index:self.__start_index +
self.__nb_segs][i]
logger.debug('[START] PID[%s] dacc_local from %s to %s', PID,
start, start + offset)
content[start:start + offset] = \
Segment(self.__content[start:start + offset]).dacc_local(gl, gr, gt2[i].get_value())
logger.debug('[END] PID[%s] dacc_local from %s to %s', PID, start,
start + offset)
logger.debug('[END] PID[%s] dAcc skeleton', PID)
return PTree.init(self, content)
def zip(self, pt: 'PTree'):
"""Zip skeleton for distributed tree
Precondition
-------------
The distributions of self and pt should be the same
Parameters
----------
pt : :obj:`PTree`
The PTree to zip with the current instance
"""
logger.debug('[START] PID[%s] zip skeleton', PID)
assert self.__distribution == pt.distribution
content = SList([None] * self.__content.length())
for i in range(
len(self.__global_index[self.__start_index:self.__start_index +
self.__nb_segs])):
(start, offset
) = self.__global_index[self.__start_index:self.__start_index +
self.__nb_segs][i]
logger.debug('[START] PID[%s] zip_local from %s to %s', PID, start,
start + offset)
content[start:start + offset] = Segment(self.__content[start:start + offset]). \
zip(Segment(pt.content[start:start + offset]))
logger.debug('[END] PID[%s] zip_local from %s to %s', PID, start,
start + offset)
res = PTree.init(self, content)
logger.debug('[END] PID[%s] zip skeleton', PID)
return res
def map2(self, f, pt):
"""Map2 skeleton for distributed tree
Precondition
-------------
The distributions of self and pt should be the same
Parameters
----------
pt : :obj:`LTree`
The LTree to zip with the current instance
f : callable
A function to zip values
"""
logger.debug('[START] PID[%s] map2 skeleton', PID)
assert self.__distribution == pt.distribution
content = SList([None] * self.__content.length())
for i in range(
len(self.__global_index[self.__start_index:self.__start_index +
self.__nb_segs])):
(start, offset
) = self.__global_index[self.__start_index:self.__start_index +
self.__nb_segs][i]
logger.debug('[START] PID[%s] map2_local from %s to %s', PID,
start, start + offset)
content[start:start + offset] = Segment(self.__content[start:start + offset]). \
map2(f, Segment(pt.content[start:start + offset]))
logger.debug('[END] PID[%s] map2_local from %s to %s', PID, start,
start + offset)
res = PTree.init(self, content)
logger.debug('[END] PID[%s] map2 skeleton', PID)
return res
def get_full_index(self):
def f(x, y):
(x1, y1) = x
(_, y2) = y
return x1 + y1, y2
return SList(self.__global_index.scanr(f))
def to_seq(self):
full_content = []
if PID == 0:
full_index = self.get_full_index()
res = LTree([None] * full_index.length())
full_content.extend(self.__content)
for iproc in range(1, NPROCS):
full_content.extend(
COMM.recv(source=iproc, tag=TAG_TO_SEQ)['c'])
for i in range(full_index.length()):
(start, offset) = full_index[i]
res[i] = full_content[start:start + offset]
return res
COMM.send({'c': self.__content}, dest=0, tag=TAG_TO_SEQ)
return None
