def random_field_space(task_name, settings):
name = next_name('field_space')
num_fields = randint(1, settings.max_fields)
field_ids = []
for i in xrange(num_fields):
field_ids.append(name + '_' + str(i))
return FieldSpace(name, task_name, field_ids)
def random_index_partition(task_name, depth, start, end, settings):
num_colors = randint(1, settings.max_colors_per_partition)
subspaces = {}
for i in xrange(0, num_colors):
subspaces[i] = random_index_subspace(task_name, depth+1, start, end, settings)
part_name = next_name('index_partition')
return IndexPartition(part_name, task_name, subspaces)
def random_task(regions, settings, depth):
name = next_name('task')
rrs = random_region_requirements_no_alias(regions, settings)
t = Task(name, rrs)
t.logical_regions_created = random_logical_region_trees(t.name, settings)
child_tasks = random_tasks(t.logical_regions_created, settings, depth+1)
t.child_tasks = child_tasks
return t
def random_index_tree(task_name, settings):
start = randint(settings.ind_min, settings.ind_max)
end = randint(start, settings.ind_max)
name = next_name('index_space')
if should_stop(0, settings):
partitions = []
else:
partitions = random_index_partitions(task_name, 0, start, end, settings)
return IndexSpace(name, task_name, start, end, partitions)
def random_index_subspace(task_name, depth, start, end, settings):
sub_start = randint(start, end)
sub_end = randint(sub_start, end)
name = next_name('index_subspace')
if should_stop(depth, settings):
partitions = []
else:
partitions = random_index_partitions(task_name, depth+1, sub_start, sub_end, settings)
return IndexSubspace(name, task_name, sub_start, sub_end, partitions)
def random_task(regions, settings, depth, parent_rrs=[], parent_name=None):
name = next_name( 'task' )
region_requirements = random_region_requirements_no_alias(regions, settings)
region_requirements += random_region_requirements_from_rrs_no_alias(parent_rrs, settings)
# if parent_rrs:
# region_requirements += parent_rrs
# num_region_requirements_to_pass_on = int(len(region_requirements) * 0.5)
# region_requirements = sample(region_requirements, num_region_requirements_to_pass_on)
# Create task
t = Task(name, region_requirements, parent_rrs)
created_regions = random_logical_region_trees(t.name, settings)
t.logical_regions_created = created_regions
child_tasks = random_tasks(created_regions,
settings, depth+1,
parent_rrs=region_requirements,
parent_name=name)
t.child_tasks = child_tasks
return t
def make_logical_subspace(task_name, field_space, index_subspace):
name = next_name('logical_subregion')
partitions = []
for p in index_subspace.partitions:
partitions.append(make_logical_partition(task_name, field_space, p))
return LogicalSubspace(name, task_name, field_space, index_subspace, partitions)
def make_logical_partition(task_name, field_space, partition):
name = next_name('logical_partition')
logical_subspaces = {}
for i in partition.subspaces:
logical_subspaces[i] = make_logical_subspace(task_name, field_space, partition.subspaces[i])
return LogicalPartition(name, task_name, partition, logical_subspaces)
def make_logical_region_tree(task_name, field_space, index_tree):
name = next_name('logical_region')
partitions = []
for p in index_tree.partitions:
partitions.append(make_logical_partition(task_name, field_space, p))
return LogicalRegion(name, task_name, field_space, index_tree, partitions)