def _create_temp_vars(self, temp_vars):
n = self.n
temp_vars['pids'] = Array(np.uint32, n=n, backend='opencl')
for var, dtype in zip(self.index_function_args,
self.index_function_arg_dtypes):
temp_vars[var] = Array(dtype, n=n, backend='opencl')
temp_vars['cids'] = Array(np.uint32, n=n, backend='opencl')
def get_leaf_size_partitions(self, group_min, group_max):
"""Partition leaves based on leaf size
Parameters
----------
group_min
Minimum leaf size
group_max
Maximum leaf size
Returns
-------
groups : Array
An array which contains the cell ids of leaves
with leaf size > group_min and leaf size <= group_max
group_count : int
The number of leaves which satisfy the given condition
on the leaf size
"""
groups = Array(np.uint32,
n=int(self.unique_cid_count),
backend='opencl')
group_count = Array(np.uint32, n=1, backend='opencl')
get_cid_groups = _get_cid_groups_kernel(self.ctx)
get_cid_groups(self.unique_cids.dev[:self.unique_cid_count],
self.pbounds.dev, groups.dev, group_count.dev,
np.int32(group_min), np.int32(group_max))
result = groups, int(group_count.dev[0].get())
return result
def _get_unique_cids_and_count(self):
n = self.n
self.unique_cids = Array(np.uint32, n=n, backend='opencl')
self.unique_cids_map = Array(np.uint32, n=n, backend='opencl')
uniq_count = Array(np.uint32, n=1, backend='opencl')
unique_cids_kernel = _get_unique_cids_kernel(self.ctx)
unique_cids_kernel(self.cids.dev, self.unique_cids_map.dev,
self.unique_cids.dev, uniq_count.dev)
self.unique_cid_count = uniq_count.dev[0].get()
def _add_prop_or_const(self, name, carray):
"""Add a new property or constant given the name and carray, note
that this assumes that this property is already added to the
particle array.
"""
np_array = self._get_array(carray)
g_ary = Array(np_array.dtype, n=carray.length, backend=self.backend)
g_ary.set(np_array)
self._data[name] = g_ary
setattr(self, name, g_ary)
def get_leaves(self):
leaves = Array(np.uint32,
n=self.offsets.dev.shape[0],
backend='opencl')
num_leaves = Array(np.uint32, n=1, backend='opencl')
leaves_kernel = _get_leaves_kernel(self.ctx, self.leaf_size)
leaves_kernel(self.offsets.dev, self.pbounds.dev, leaves.dev,
num_leaves.dev)
num_leaves = num_leaves.dev[0].get()
return leaves.dev[:num_leaves], num_leaves
def append_parray(self, parray, align=True, update_constants=False):
""" Add particles from a particle array
properties that are not there in self will be added
"""
if parray.gpu is None:
parray.set_device_helper(DeviceHelper(parray))
if parray.gpu.get_number_of_particles() == 0:
return
num_extra_particles = parray.gpu.get_number_of_particles()
old_num_particles = self.get_number_of_particles()
new_num_particles = num_extra_particles + old_num_particles
# extend current arrays by the required number of particles
self.extend(num_extra_particles)
my_stride = self._particle_array.stride
for prop_name in parray.gpu.properties:
stride = parray.stride.get(prop_name, 1)
if stride > 1 and prop_name not in my_stride:
my_stride[prop_name] = stride
if prop_name in self.properties:
arr = self._data[prop_name]
source = parray.gpu.get_device_array(prop_name)
arr.dev[old_num_particles * stride:] = source.dev
else:
# meaning this property is not there in self.
dtype = parray.gpu.get_device_array(prop_name).dtype
arr = Array(dtype,
n=new_num_particles * stride,
backend=self.backend)
arr.fill(parray.default_values[prop_name])
self.update_prop(prop_name, arr)
# now add the values to the end of the created array
dest = self._data[prop_name]
source = parray.gpu.get_device_array(prop_name)
dest.dev[old_num_particles * stride:] = source.dev
if update_constants:
for const in parray.gpu.constants:
if const not in self.constants:
arr = parray.gpu.get_device_array(const)
self.update_const(const, arr.copy())
if num_extra_particles > 0 and align:
self.align_particles()
def _initialize_data(self):
self.sorted = False
num_particles = self.n
self.pids = Array(np.uint32, n=num_particles, backend='opencl')
self.cids = Array(np.uint32, n=num_particles, backend='opencl')
self.cids.fill(0)
for var, dtype in zip(self.index_function_args,
self.index_function_arg_dtypes):
setattr(self, var, Array(dtype, n=num_particles, backend='opencl'))
# Filled after tree built
self.pbounds = None
self.offsets = None
self.initialized = True
def _merge_layers(self, offsets_temp, pbounds_temp):
curr_offset = 0
total_nodes = 0
for i in range(self.depth + 1):
total_nodes += self.num_nodes[i]
self.offsets = Array(np.int32, n=total_nodes, backend='opencl')
self.pbounds = Array(cl.cltypes.uint2, n=total_nodes, backend='opencl')
append_layer = self.main_helper.get_kernel('append_layer')
self.total_nodes = total_nodes
for i in range(self.depth + 1):
append_layer(offsets_temp[i].dev, pbounds_temp[i].dev,
self.offsets.dev, self.pbounds.dev,
np.int32(curr_offset), np.uint8(i == self.depth))
curr_offset += self.num_nodes[i]
def _remove_particles_bool(self, if_remove, align=True):
""" Remove particle i if if_remove[i] is True
"""
num_indices = int(array.sum(if_remove, backend=self.backend))
if num_indices == 0:
return
num_particles = self.get_number_of_particles()
new_indices = Array(np.uint32,
n=(num_particles - num_indices),
backend=self.backend)
num_removed_particles = array.empty(1,
dtype=np.int32,
backend=self.backend)
remove_knl, stride_knl = self._get_remove_particles_bool_kernels()
remove_knl(if_remove=if_remove,
new_indices=new_indices,
num_removed_particles=num_removed_particles,
num_particles=num_particles)
new_num_particles = num_particles - int(num_removed_particles.get())
strides = set(self._particle_array.stride.values())
s_indices = {1: new_indices}
for stride in strides:
if stride == 1:
continue
size = new_num_particles * stride
s_index = Array(np.uint32, n=size, backend=self.backend)
stride_knl(new_indices, s_index, size, stride)
s_indices[stride] = s_index
for prop in self.properties:
stride = self._particle_array.stride.get(prop, 1)
s_index = s_indices[stride]
self._data[prop].align(s_index)
setattr(self, prop, self._data[prop])
if align:
self.align_particles()
def find_neighbor_cids(self, tree_src):
neighbor_cid_count = Array(np.uint32, n=self.unique_cid_count + 1,
backend='opencl')
find_neighbor_cid_counts = self._leaf_neighbor_operation(
tree_src,
args="uint2 *pbounds, int *cnt",
setup="int count=0",
operation="""
if (pbounds[cid_src].s0 < pbounds[cid_src].s1)
count++;
""",
output_expr="cnt[i] = count;"
)
find_neighbor_cid_counts = profile_kernel(
find_neighbor_cid_counts, 'find_neighbor_cid_count',
backend='opencl'
)
find_neighbor_cid_counts(tree_src.pbounds.dev,
neighbor_cid_count.dev)
neighbor_psum = _get_neighbor_count_prefix_sum_kernel(self.ctx)
neighbor_psum(neighbor_cid_count.dev)
total_neighbors = int(neighbor_cid_count.dev[-1].get())
neighbor_cids = Array(np.uint32, n=total_neighbors,
backend='opencl')
find_neighbor_cids = self._leaf_neighbor_operation(
tree_src,
args="uint2 *pbounds, int *cnt, int *neighbor_cids",
setup="int offset=cnt[i];",
operation="""
if (pbounds[cid_src].s0 < pbounds[cid_src].s1)
neighbor_cids[offset++] = cid_src;
""",
output_expr=""
)
find_neighbor_cids = profile_kernel(
find_neighbor_cids, 'find_neighbor_cids', backend='opencl')
find_neighbor_cids(tree_src.pbounds.dev,
neighbor_cid_count.dev, neighbor_cids.dev)
return neighbor_cid_count, neighbor_cids
def remove_particles(self, indices):
""" Remove particles whose indices are given in index_list.
We repeatedly interchange the values of the last element and
values from the index_list and reduce the size of the array
by one. This is done for every property that is being maintained.
Parameters
----------
indices : array
an array of indices, this array can be a list, numpy array
or a LongArray.
Notes
-----
Pseudo-code for the implementation::
if index_list.length > number of particles
raise ValueError
sorted_indices <- index_list sorted in ascending order.
for every every array in property_array
array.remove(sorted_indices)
"""
if len(indices) > self.get_number_of_particles():
msg = 'Number of particles to be removed is greater than'
msg += 'number of particles in array'
raise ValueError(msg)
num_particles = self.get_number_of_particles()
if_remove = Array(np.int32, n=num_particles, backend=self.backend)
if_remove.fill(0)
fill_if_remove_knl = self._get_remove_particles_kernel()
fill_if_remove_knl(indices, if_remove, num_particles)
self._remove_particles_bool(if_remove)
def remove_particles(self, indices, align=True):
""" Remove particles whose indices are given in index_list.
Parameters
----------
indices : array
an array of indices, this array can be a list, numpy array
or a LongArray.
"""
if len(indices) > self.get_number_of_particles():
msg = 'Number of particles to be removed is greater than'
msg += 'number of particles in array'
raise ValueError(msg)
num_particles = self.get_number_of_particles()
if_remove = Array(np.int32, n=num_particles, backend=self.backend)
if_remove.fill(0)
fill_if_remove_knl = self._get_remove_particles_kernel()
fill_if_remove_knl(indices, if_remove, num_particles)
self._remove_particles_bool(if_remove, align=align)
def _update_node_data(self, offsets_prev, pbounds_prev, offsets, pbounds,
seg_flag, child_count_prefix_sum, csum_nodes,
csum_nodes_next, n):
"""Update node data and return number of children which are leaves."""
# Update particle-related data of children
set_node_data = self.main_helper.get_kernel("set_node_data", k=self.k)
set_node_data(offsets_prev.dev, pbounds_prev.dev, offsets.dev,
pbounds.dev, seg_flag.dev, child_count_prefix_sum.dev,
np.uint32(csum_nodes), np.uint32(n))
# Set children offsets
leaf_count = Array(np.uint32, n=1, backend='opencl')
set_offsets = _get_set_offset_kernel(self.ctx, self.k, self.leaf_size)
set_offsets(pbounds.dev, offsets.dev, leaf_count.dev,
np.uint32(csum_nodes_next))
return leaf_count.dev[0].get()
def _create_ghosts_periodic(self):
"""Identify boundary particles and create images.
We need to find all particles that are within a specified
distance from the boundaries and place image copies on the
other side of the boundary. Corner reflections need to be
accounted for when using domains with multiple periodicity.
The periodic domain is specified using the DomainManager object
"""
copy_props = self.copy_props
pa_wrappers = self.pa_wrappers
narrays = self.narrays
# cell size used to check for periodic ghosts. For summation density
# like operations, we need to create two layers of ghost images, this
# is configurable via the n_layers argument to the constructor.
cell_size = self.n_layers * self.cell_size
# periodic domain values
xmin, xmax = self.xmin, self.xmax
ymin, ymax = self.ymin, self.ymax
zmin, zmax = self.zmin, self.zmax
xtranslate = self.xtranslate
ytranslate = self.ytranslate
ztranslate = self.ztranslate
# periodicity flags
periodic_in_x = self.periodic_in_x
periodic_in_y = self.periodic_in_y
periodic_in_z = self.periodic_in_z
reduce_knl = self._get_ghosts_reduction_kernel()
scan_knl = self._get_ghosts_scan_kernel()
translate_knl = self._get_translate_kernel()
if not self.ghosts:
self.ghosts = [
paw.pa.empty_clone(props=copy_props[i])
for i, paw in enumerate(pa_wrappers)
]
else:
for ghost_pa in self.ghosts:
ghost_pa.resize(0)
for i in range(narrays):
self.ghosts[i].ensure_properties(pa_wrappers[i].pa,
props=copy_props[i])
for i, pa_wrapper in enumerate(self.pa_wrappers):
ghost_pa = self.ghosts[i]
x = pa_wrapper.pa.gpu.x
y = pa_wrapper.pa.gpu.y
z = pa_wrapper.pa.gpu.z
num_extra_particles = reduce_knl(x, y, z, xmin, ymin, zmin, xmax,
ymax, zmax, cell_size,
periodic_in_x, periodic_in_y,
periodic_in_z)
num_extra_particles = int(num_extra_particles)
indices = Array(np.int32, n=num_extra_particles)
masks = Array(np.int32, n=num_extra_particles)
scan_knl(periodic_in_x=periodic_in_x,
periodic_in_y=periodic_in_y,
periodic_in_z=periodic_in_z,
x=x,
y=y,
z=z,
xmin=xmin,
ymin=ymin,
zmin=zmin,
xmax=xmax,
ymax=ymax,
zmax=zmax,
cell_size=cell_size,
masks=masks,
indices=indices)
pa_wrapper.pa.extract_particles(indices,
ghost_pa,
align=False,
props=copy_props[i])
translate_knl(ghost_pa.gpu.x, ghost_pa.gpu.y, ghost_pa.gpu.z,
ghost_pa.gpu.tag, xtranslate, ytranslate, ztranslate,
masks)
pa_wrapper.pa.append_parray(ghost_pa, align=False)
def _allocate_memory(self, pa_gpu):
shape = getattr(pa_gpu, self.varnames[0]).dev.shape[0]
for v in self.varnames:
setattr(
self, v,
Array(ctype_to_dtype(self.c_type), n=shape, backend='opencl'))
class Tree(object):
"""k-ary Tree
"""
def __init__(self, n, k=8, leaf_size=32):
self.ctx = get_context()
self.queue = get_queue()
self.sorted = False
self.main_helper = get_helper(os.path.join('tree', 'tree.mako'))
self.initialized = False
self.preamble = ""
self.leaf_size = leaf_size
self.k = k
self.n = n
self.sorted = False
self.depth = 0
self.index_function_args = []
self.index_function_arg_ctypes = []
self.index_function_arg_dtypes = []
self.index_function_consts = []
self.index_function_const_ctypes = []
self.index_code = ""
self.set_index_function_info()
def set_index_function_info(self):
raise NotImplementedError
def get_data_args(self):
return [getattr(self, v) for v in self.index_function_args]
def get_index_constants(self, depth):
raise NotImplementedError
def _initialize_data(self):
self.sorted = False
num_particles = self.n
self.pids = Array(np.uint32, n=num_particles, backend='opencl')
self.cids = Array(np.uint32, n=num_particles, backend='opencl')
self.cids.fill(0)
for var, dtype in zip(self.index_function_args,
self.index_function_arg_dtypes):
setattr(self, var, Array(dtype, n=num_particles, backend='opencl'))
# Filled after tree built
self.pbounds = None
self.offsets = None
self.initialized = True
def _reinitialize_data(self):
self.sorted = False
num_particles = self.n
self.pids.resize(num_particles)
self.cids.resize(num_particles)
self.cids.fill(0)
for var in self.index_function_args:
getattr(self, var).resize(num_particles)
# Filled after tree built
self.pbounds = None
self.offsets = None
def _setup_build(self):
if not self.initialized:
self._initialize_data()
else:
self._reinitialize_data()
def _build(self, fixed_depth=None):
self._build_tree(fixed_depth)
###########################################################################
# Core construction algorithm and helper functions
###########################################################################
# A little bit of manual book-keeping for temporary variables.
# More specifically, these temporary variables would otherwise be thrown
# away after building each layer of the tree.
# We could instead just allocate new arrays after building each layer and
# and let the GC take care of stuff but I'm guessing this is a
# a better approach to save on memory
def _create_temp_vars(self, temp_vars):
n = self.n
temp_vars['pids'] = Array(np.uint32, n=n, backend='opencl')
for var, dtype in zip(self.index_function_args,
self.index_function_arg_dtypes):
temp_vars[var] = Array(dtype, n=n, backend='opencl')
temp_vars['cids'] = Array(np.uint32, n=n, backend='opencl')
def _exchange_temp_vars(self, temp_vars):
for k in temp_vars.keys():
t = temp_vars[k]
temp_vars[k] = getattr(self, k)
setattr(self, k, t)
def _clean_temp_vars(self, temp_vars):
for k in list(temp_vars.keys()):
del temp_vars[k]
def _get_temp_data_args(self, temp_vars):
result = [temp_vars[v] for v in self.index_function_args]
return result
def _reorder_particles(self, depth, child_count_prefix_sum, offsets_parent,
pbounds_parent, seg_flag, csum_nodes_prev,
temp_vars):
# Scan
args = [('__global ' + ctype + ' *' + v) for v, ctype in zip(
self.index_function_args, self.index_function_arg_ctypes)]
args += [(ctype + ' ' + v) for v, ctype in zip(
self.index_function_consts, self.index_function_const_ctypes)]
args = ', '.join(args)
particle_kernel = _get_particle_kernel(self.ctx, self.k, args,
self.index_code)
args = [seg_flag.dev, child_count_prefix_sum.dev]
args += [x.dev for x in self.get_data_args()]
args += self.get_index_constants(depth)
particle_kernel(*args)
# Reorder particles
reorder_particles = self.main_helper.get_kernel(
'reorder_particles',
k=self.k,
data_vars=tuple(self.index_function_args),
data_var_ctypes=tuple(self.index_function_arg_ctypes),
const_vars=tuple(self.index_function_consts),
const_var_ctypes=tuple(self.index_function_const_ctypes),
index_code=self.index_code)
args = [
self.pids.dev, self.cids.dev, seg_flag.dev, pbounds_parent.dev,
offsets_parent.dev, child_count_prefix_sum.dev,
temp_vars['pids'].dev, temp_vars['cids'].dev
]
args += [x.dev for x in self.get_data_args()]
args += [x.dev for x in self._get_temp_data_args(temp_vars)]
args += self.get_index_constants(depth)
args += [np.uint32(csum_nodes_prev)]
reorder_particles(*args)
self._exchange_temp_vars(temp_vars)
def _merge_layers(self, offsets_temp, pbounds_temp):
curr_offset = 0
total_nodes = 0
for i in range(self.depth + 1):
total_nodes += self.num_nodes[i]
self.offsets = Array(np.int32, n=total_nodes, backend='opencl')
self.pbounds = Array(cl.cltypes.uint2, n=total_nodes, backend='opencl')
append_layer = self.main_helper.get_kernel('append_layer')
self.total_nodes = total_nodes
for i in range(self.depth + 1):
append_layer(offsets_temp[i].dev, pbounds_temp[i].dev,
self.offsets.dev, self.pbounds.dev,
np.int32(curr_offset), np.uint8(i == self.depth))
curr_offset += self.num_nodes[i]
def _update_node_data(self, offsets_prev, pbounds_prev, offsets, pbounds,
seg_flag, child_count_prefix_sum, csum_nodes,
csum_nodes_next, n):
"""Update node data and return number of children which are leaves."""
# Update particle-related data of children
set_node_data = self.main_helper.get_kernel("set_node_data", k=self.k)
set_node_data(offsets_prev.dev, pbounds_prev.dev, offsets.dev,
pbounds.dev, seg_flag.dev, child_count_prefix_sum.dev,
np.uint32(csum_nodes), np.uint32(n))
# Set children offsets
leaf_count = Array(np.uint32, n=1, backend='opencl')
set_offsets = _get_set_offset_kernel(self.ctx, self.k, self.leaf_size)
set_offsets(pbounds.dev, offsets.dev, leaf_count.dev,
np.uint32(csum_nodes_next))
return leaf_count.dev[0].get()
def _build_tree(self, fixed_depth=None):
# We build the tree one layer at a time. We stop building new
# layers after either all the
# nodes are leaves or after reaching the target depth (fixed_depth).
# At this point, the information for each layer is segmented / not
# contiguous in memory, and so we run a merge_layers procedure to
# move the data for all layers into a single array.
#
# The procedure for building each layer can be split up as follows
# 1) Determine which child each particle is going to belong to in the
# next layer
# 2) Perform a kind of segmented scan over this. This gives us the
# new order of the particles so that consecutive particles lie in
# the same child
# 3) Reorder the particles based on this order
# 4) Create a new layer and set the node data for the new layer. We
# get to know which particles belong to each node directly from the
# results of step 2
# 5) Set the predicted offsets of the children of the nodes in the
# new layer. If a node has fewer than leaf_size particles, it's a
# leaf. A kind of prefix sum over this directly let's us know the
# predicted offsets.
# Rinse and repeat for building more layers.
#
# Note that after building the last layer, the predicted offsets for
# the children might not be correctly since we're not going to build
# more layers. The _merge_layers procedure sets the offsets in the
# last layer to -1 to correct this.
num_leaves_here = 0
n = self.n
temp_vars = {}
self.depth = 0
self.num_nodes = [1]
# Cumulative sum of nodes in the previous layers
csum_nodes_prev = 0
csum_nodes = 1
# Initialize temporary data (but persistent across layers)
self._create_temp_vars(temp_vars)
child_count_prefix_sum = Array(get_vector_dtype('uint', self.k),
n=n,
backend='opencl')
seg_flag = Array(cl.cltypes.char, n=n, backend='opencl')
seg_flag.fill(0)
seg_flag.dev[0] = 1
offsets_temp = [Array(np.int32, n=1, backend='opencl')]
offsets_temp[-1].fill(1)
pbounds_temp = [Array(cl.cltypes.uint2, n=1, backend='opencl')]
pbounds_temp[-1].dev[0].set(cl.cltypes.make_uint2(0, n))
# FIXME: Depths above 20 possible and feasible for binary / quad trees
loop_lim = min(fixed_depth, 20)
for depth in range(1, loop_lim):
num_nodes = self.k * (self.num_nodes[-1] - num_leaves_here)
if num_nodes == 0:
break
else:
self.depth += 1
self.num_nodes.append(num_nodes)
# Allocate new layer
offsets_temp.append(
Array(np.int32, n=self.num_nodes[-1], backend='opencl'))
pbounds_temp.append(
Array(cl.cltypes.uint2, n=self.num_nodes[-1],
backend='opencl'))
# Generate particle index and reorder the particles
self._reorder_particles(depth, child_count_prefix_sum,
offsets_temp[-2], pbounds_temp[-2],
seg_flag, csum_nodes_prev, temp_vars)
num_leaves_here = self._update_node_data(
offsets_temp[-2], pbounds_temp[-2], offsets_temp[-1],
pbounds_temp[-1], seg_flag, child_count_prefix_sum, csum_nodes,
csum_nodes + self.num_nodes[-1], n)
csum_nodes_prev = csum_nodes
csum_nodes += self.num_nodes[-1]
self._merge_layers(offsets_temp, pbounds_temp)
self._clean_temp_vars(temp_vars)
###########################################################################
# Misc
###########################################################################
def _get_unique_cids_and_count(self):
n = self.n
self.unique_cids = Array(np.uint32, n=n, backend='opencl')
self.unique_cids_map = Array(np.uint32, n=n, backend='opencl')
uniq_count = Array(np.uint32, n=1, backend='opencl')
unique_cids_kernel = _get_unique_cids_kernel(self.ctx)
unique_cids_kernel(self.cids.dev, self.unique_cids_map.dev,
self.unique_cids.dev, uniq_count.dev)
self.unique_cid_count = uniq_count.dev[0].get()
def get_leaves(self):
leaves = Array(np.uint32,
n=self.offsets.dev.shape[0],
backend='opencl')
num_leaves = Array(np.uint32, n=1, backend='opencl')
leaves_kernel = _get_leaves_kernel(self.ctx, self.leaf_size)
leaves_kernel(self.offsets.dev, self.pbounds.dev, leaves.dev,
num_leaves.dev)
num_leaves = num_leaves.dev[0].get()
return leaves.dev[:num_leaves], num_leaves
def _sort(self):
"""Set tree as being sorted
The particle array needs to be aligned by the caller!
"""
if not self.sorted:
self.sorted = 1
###########################################################################
# Tree API
###########################################################################
def allocate_node_prop(self, dtype):
return Array(dtype, n=self.total_nodes, backend='opencl')
def allocate_leaf_prop(self, dtype):
return Array(dtype, n=int(self.unique_cid_count), backend='opencl')
def get_preamble(self):
if self.sorted:
return "#define PID(idx) (idx)"
else:
return "#define PID(idx) (pids[idx])"
def get_leaf_size_partitions(self, group_min, group_max):
"""Partition leaves based on leaf size
Parameters
----------
group_min
Minimum leaf size
group_max
Maximum leaf size
Returns
-------
groups : Array
An array which contains the cell ids of leaves
with leaf size > group_min and leaf size <= group_max
group_count : int
The number of leaves which satisfy the given condition
on the leaf size
"""
groups = Array(np.uint32,
n=int(self.unique_cid_count),
backend='opencl')
group_count = Array(np.uint32, n=1, backend='opencl')
get_cid_groups = _get_cid_groups_kernel(self.ctx)
get_cid_groups(self.unique_cids.dev[:self.unique_cid_count],
self.pbounds.dev, groups.dev, group_count.dev,
np.int32(group_min), np.int32(group_max))
result = groups, int(group_count.dev[0].get())
return result
def tree_bottom_up(self,
args,
setup,
leaf_operation,
node_operation,
output_expr,
preamble=""):
return tree_bottom_up(self.ctx, args, setup, leaf_operation,
node_operation, output_expr, preamble)
def leaf_tree_traverse(self,
args,
setup,
node_operation,
leaf_operation,
output_expr,
common_operation="",
preamble=""):
"""
Traverse this (source) tree. One thread for each leaf of
destination tree.
"""
return leaf_tree_traverse(self.ctx, self.k, args, setup,
node_operation, leaf_operation, output_expr,
common_operation, preamble)
def point_tree_traverse(self,
args,
setup,
node_operation,
leaf_operation,
output_expr,
common_operation="",
preamble=""):
"""
Traverse this (source) tree. One thread for each particle of
destination tree.
"""
return point_tree_traverse(self.ctx, self.k, args, setup,
node_operation, leaf_operation, output_expr,
common_operation, preamble)
def _build_tree(self, fixed_depth=None):
# We build the tree one layer at a time. We stop building new
# layers after either all the
# nodes are leaves or after reaching the target depth (fixed_depth).
# At this point, the information for each layer is segmented / not
# contiguous in memory, and so we run a merge_layers procedure to
# move the data for all layers into a single array.
#
# The procedure for building each layer can be split up as follows
# 1) Determine which child each particle is going to belong to in the
# next layer
# 2) Perform a kind of segmented scan over this. This gives us the
# new order of the particles so that consecutive particles lie in
# the same child
# 3) Reorder the particles based on this order
# 4) Create a new layer and set the node data for the new layer. We
# get to know which particles belong to each node directly from the
# results of step 2
# 5) Set the predicted offsets of the children of the nodes in the
# new layer. If a node has fewer than leaf_size particles, it's a
# leaf. A kind of prefix sum over this directly let's us know the
# predicted offsets.
# Rinse and repeat for building more layers.
#
# Note that after building the last layer, the predicted offsets for
# the children might not be correctly since we're not going to build
# more layers. The _merge_layers procedure sets the offsets in the
# last layer to -1 to correct this.
num_leaves_here = 0
n = self.n
temp_vars = {}
self.depth = 0
self.num_nodes = [1]
# Cumulative sum of nodes in the previous layers
csum_nodes_prev = 0
csum_nodes = 1
# Initialize temporary data (but persistent across layers)
self._create_temp_vars(temp_vars)
child_count_prefix_sum = Array(get_vector_dtype('uint', self.k),
n=n,
backend='opencl')
seg_flag = Array(cl.cltypes.char, n=n, backend='opencl')
seg_flag.fill(0)
seg_flag.dev[0] = 1
offsets_temp = [Array(np.int32, n=1, backend='opencl')]
offsets_temp[-1].fill(1)
pbounds_temp = [Array(cl.cltypes.uint2, n=1, backend='opencl')]
pbounds_temp[-1].dev[0].set(cl.cltypes.make_uint2(0, n))
# FIXME: Depths above 20 possible and feasible for binary / quad trees
loop_lim = min(fixed_depth, 20)
for depth in range(1, loop_lim):
num_nodes = self.k * (self.num_nodes[-1] - num_leaves_here)
if num_nodes == 0:
break
else:
self.depth += 1
self.num_nodes.append(num_nodes)
# Allocate new layer
offsets_temp.append(
Array(np.int32, n=self.num_nodes[-1], backend='opencl'))
pbounds_temp.append(
Array(cl.cltypes.uint2, n=self.num_nodes[-1],
backend='opencl'))
# Generate particle index and reorder the particles
self._reorder_particles(depth, child_count_prefix_sum,
offsets_temp[-2], pbounds_temp[-2],
seg_flag, csum_nodes_prev, temp_vars)
num_leaves_here = self._update_node_data(
offsets_temp[-2], pbounds_temp[-2], offsets_temp[-1],
pbounds_temp[-1], seg_flag, child_count_prefix_sum, csum_nodes,
csum_nodes + self.num_nodes[-1], n)
csum_nodes_prev = csum_nodes
csum_nodes += self.num_nodes[-1]
self._merge_layers(offsets_temp, pbounds_temp)
self._clean_temp_vars(temp_vars)
def allocate_node_prop(self, dtype):
return Array(dtype, n=self.total_nodes, backend='opencl')
def allocate_leaf_prop(self, dtype):
return Array(dtype, n=int(self.unique_cid_count), backend='opencl')
