def build_simple_bvh(degree):
#mesh = chroma.models.lionsolid()
mesh = chroma.models.companioncube()
bvh = make_simple_bvh(mesh, degree)
nodes = bvh.nodes
layer_bounds = np.append(bvh.layer_offsets, len(nodes))
world_coords = bvh.world_coords
for i, (layer_start,
layer_end) in enumerate(zip(layer_bounds[:-1], layer_bounds[1:])):
print i, node_areas(
nodes[layer_start:layer_end]).sum() * world_coords.world_scale**2
def build_simple_bvh(degree):
mesh = chroma.models.lionsolid()
bvh = make_simple_bvh(mesh, degree)
nodes = bvh.nodes
layer_bounds = np.append(bvh.layer_offsets, len(nodes))
world_coords = bvh.world_coords
for i, (layer_start, layer_end) in enumerate(zip(layer_bounds[:-1],
layer_bounds[1:])):
print i, node_areas(nodes[layer_start:layer_end]).sum() * world_coords.world_scale**2
assert isinstance(bvh, BVH)
def build_simple_bvh(degree):
#mesh = chroma.models.lionsolid()
mesh = chroma.models.companioncube()
bvh = make_recursive_grid_bvh(mesh,
degree,
save_morton_codes="mortonout.cu.nudot.txt")
#bvh = make_simple_bvh(mesh, degree)
nodes = bvh.nodes
layer_bounds = np.append(bvh.layer_offsets, len(nodes))
world_coords = bvh.world_coords
for i, (layer_start,
layer_end) in enumerate(zip(layer_bounds[:-1], layer_bounds[1:])):
print i, node_areas(
nodes[layer_start:layer_end]).sum() * world_coords.world_scale**2
return bvh
def merge_nodes(nodes, degree, max_ratio=None):
nthreads_per_block = 256
context = None
queue = None
if gpuapi.is_gpu_api_opencl():
context = cltools.get_last_context()
queue = cl.CommandQueue(context)
# Load GPU functions
if gpuapi.is_gpu_api_cuda():
bvh_module = get_module('bvh.cu',
options=api_options,
include_source_directory=True)
elif gpuapi.is_gpu_api_opencl():
# don't like the last context method. trouble. trouble.
bvh_module = get_module('bvh.cl',
context,
options=api_options,
include_source_directory=True)
else:
raise RuntimeError('API is neither CUDA nor OpenCL?!')
bvh_funcs = GPUFuncs(bvh_module)
# determine number of parents
nparent = len(nodes) / degree
if len(nodes) % degree != 0:
nparent += 1
if nparent == 1:
nparent_pad = nparent
else:
nparent_pad = round_up_to_multiple(nparent, 1) #degree
# allocate memory
if gpuapi.is_gpu_api_cuda():
gpu_parent_nodes = ga.zeros(shape=nparent_pad, dtype=ga.vec.uint4)
elif gpuapi.is_gpu_api_opencl():
parent_nodes_np = np.zeros(shape=nparent, dtype=ga.vec.uint4)
gpu_parent_nodes = ga.to_device(queue, parent_nodes_np)
gpu_nodes = ga.to_device(queue, nodes)
else:
raise RuntimeError('API is neither CUDA nor OpenCL?!')
# run kernel
if gpuapi.is_gpu_api_cuda():
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(nparent, nthreads_per_block, max_blocks=10000):
bvh_funcs.make_parents(np.uint32(first_index),
np.uint32(elements_this_iter),
np.uint32(degree),
gpu_parent_nodes,
cuda.In(nodes),
np.uint32(0),
np.uint32(len(nodes)),
block=(nthreads_per_block, 1, 1),
grid=(nblocks_this_iter, 1))
elif gpuapi.is_gpu_api_opencl():
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(nparent, nthreads_per_block, max_blocks=1):
bvh_funcs.make_parents(queue, (elements_this_iter, 1, 1), None,
np.uint32(first_index),
np.uint32(elements_this_iter),
np.uint32(degree), gpu_parent_nodes.data,
gpu_nodes.data, np.uint32(0),
np.uint32(len(nodes))).wait()
else:
raise RuntimeError('API is neither CUDA nor OpenCL?!')
parent_nodes = gpu_parent_nodes.get()
if max_ratio is not None:
areas = node_areas(parent_nodes)
child_areas = node_areas(nodes)
excessive_area = np.zeros(shape=len(areas), dtype=bool)
for i, parent_area in enumerate(areas):
nchild = parent_nodes['w'][i] >> CHILD_BITS
child_index = parent_nodes['w'][i] & ~NCHILD_MASK
child_area = child_areas[child_index:child_index + nchild].sum()
#if parent_area > 1e9:
# print i, 'Children: %e, Parent: %e' % (child_area, parent_area)
if child_area / parent_area < 0.3:
excessive_area[i] = True
#print i, 'Children: %e, Parent: %e' % (child_area, parent_area)
extra_slots = round_up_to_multiple(
(degree - 1) * np.count_nonzero(excessive_area), 1)
print 'Extra slots:', extra_slots
new_parent_nodes = np.zeros(shape=len(parent_nodes) + extra_slots,
dtype=parent_nodes.dtype)
new_parent_nodes[:len(parent_nodes)] = parent_nodes
offset = 0
for count, index in enumerate(np.argwhere(excessive_area)):
index = index[0] + offset
nchild = new_parent_nodes['w'][index] >> CHILD_BITS
child_index = new_parent_nodes['w'][index] & ~NCHILD_MASK
new_parent_nodes[index] = nodes[child_index]
#new_parent_nodes['w'][index] = 1 << CHILD_BITS | child_index
tmp_nchild = new_parent_nodes['w'][index] >> CHILD_BITS
tmp_child_index = new_parent_nodes['w'][index] & ~NCHILD_MASK
new_parent_nodes['w'][index] = tmp_nchild << CHILD_BITS | (
tmp_child_index + len(nodes))
if nchild == 1:
continue
# slide everyone over
#print index, nchild, len(new_parent_nodes)
new_parent_nodes[index + nchild:] = new_parent_nodes[index +
1:-nchild + 1]
offset += nchild - 1
for sibling in xrange(nchild - 1):
new_parent_index = index + 1 + sibling
new_parent_nodes[new_parent_index] = nodes[child_index +
sibling + 1]
if new_parent_nodes['x'][new_parent_index] != 0:
tmp_nchild = new_parent_nodes['w'][
new_parent_index] >> CHILD_BITS
tmp_child_index = new_parent_nodes['w'][
new_parent_index] & ~NCHILD_MASK
new_parent_nodes['w'][
new_parent_index] = tmp_nchild << CHILD_BITS | (
tmp_child_index + len(nodes))
#new_parent_nodes['w'][new_parent_index] = 1 << CHILD_BITS | (child_index + sibling + 1)
#print 'intermediate: %e' % node_areas(new_parent_nodes).max()
print 'old: %e' % node_areas(parent_nodes).max()
print 'new: %e' % node_areas(new_parent_nodes).max()
if len(new_parent_nodes) < len(nodes):
# Only adopt new set of parent nodes if it actually reduces the
# total number of nodes at this level by 1.
parent_nodes = new_parent_nodes
return parent_nodes
def merge_nodes(nodes, degree, max_ratio=None):
bvh_module = get_cu_module('bvh.cu', options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
nparent = len(nodes) / degree
if len(nodes) % degree != 0:
nparent += 1
if nparent == 1:
nparent_pad = nparent
else:
nparent_pad = round_up_to_multiple(nparent, 1)#degree)
gpu_parent_nodes = ga.zeros(shape=nparent_pad, dtype=ga.vec.uint4)
nthreads_per_block = 256
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(nparent, nthreads_per_block, max_blocks=10000):
bvh_funcs.make_parents(np.uint32(first_index),
np.uint32(elements_this_iter),
np.uint32(degree),
gpu_parent_nodes,
cuda.In(nodes),
np.uint32(0),
np.uint32(len(nodes)),
block=(nthreads_per_block,1,1),
grid=(nblocks_this_iter,1))
parent_nodes = gpu_parent_nodes.get()
if max_ratio is not None:
areas = node_areas(parent_nodes)
child_areas = node_areas(nodes)
excessive_area = np.zeros(shape=len(areas), dtype=bool)
for i, parent_area in enumerate(areas):
nchild = parent_nodes['w'][i] >> CHILD_BITS
child_index = parent_nodes['w'][i] & ~NCHILD_MASK
child_area = child_areas[child_index:child_index+nchild].sum()
#if parent_area > 1e9:
# print i, 'Children: %e, Parent: %e' % (child_area, parent_area)
if child_area/parent_area < 0.3:
excessive_area[i] = True
#print i, 'Children: %e, Parent: %e' % (child_area, parent_area)
extra_slots = round_up_to_multiple((degree - 1) * np.count_nonzero(excessive_area), 1)
print 'Extra slots:', extra_slots
new_parent_nodes = np.zeros(shape=len(parent_nodes) + extra_slots,
dtype=parent_nodes.dtype)
new_parent_nodes[:len(parent_nodes)] = parent_nodes
offset = 0
for count, index in enumerate(np.argwhere(excessive_area)):
index = index[0] + offset
nchild = new_parent_nodes['w'][index] >> CHILD_BITS
child_index = new_parent_nodes['w'][index] & ~NCHILD_MASK
new_parent_nodes[index] = nodes[child_index]
#new_parent_nodes['w'][index] = 1 << CHILD_BITS | child_index
tmp_nchild = new_parent_nodes['w'][index] >> CHILD_BITS
tmp_child_index = new_parent_nodes['w'][index] & ~NCHILD_MASK
new_parent_nodes['w'][index] = tmp_nchild << CHILD_BITS | (tmp_child_index + len(nodes))
if nchild == 1:
continue
# slide everyone over
#print index, nchild, len(new_parent_nodes)
new_parent_nodes[index+nchild:] = new_parent_nodes[index+1:-nchild+1]
offset += nchild - 1
for sibling in xrange(nchild - 1):
new_parent_index = index + 1 + sibling
new_parent_nodes[new_parent_index] = nodes[child_index + sibling + 1]
if new_parent_nodes['x'][new_parent_index] != 0:
tmp_nchild = new_parent_nodes['w'][new_parent_index] >> CHILD_BITS
tmp_child_index = new_parent_nodes['w'][new_parent_index] & ~NCHILD_MASK
new_parent_nodes['w'][new_parent_index] = tmp_nchild << CHILD_BITS | (tmp_child_index + len(nodes))
#new_parent_nodes['w'][new_parent_index] = 1 << CHILD_BITS | (child_index + sibling + 1)
#print 'intermediate: %e' % node_areas(new_parent_nodes).max()
print 'old: %e' % node_areas(parent_nodes).max()
print 'new: %e' % node_areas(new_parent_nodes).max()
if len(new_parent_nodes) < len(nodes):
# Only adopt new set of parent nodes if it actually reduces the
# total number of nodes at this level by 1.
parent_nodes = new_parent_nodes
return parent_nodes
def merge_nodes(nodes, degree, max_ratio=None):
bvh_module = get_cu_module('bvh.cu',
options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
nparent = len(nodes) / degree
if len(nodes) % degree != 0:
nparent += 1
if nparent == 1:
nparent_pad = nparent
else:
nparent_pad = round_up_to_multiple(nparent, 1) #degree)
gpu_parent_nodes = ga.zeros(shape=nparent_pad, dtype=ga.vec.uint4)
nthreads_per_block = 256
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(nparent, nthreads_per_block, max_blocks=10000):
bvh_funcs.make_parents(np.uint32(first_index),
np.uint32(elements_this_iter),
np.uint32(degree),
gpu_parent_nodes,
cuda.In(nodes),
np.uint32(0),
np.uint32(len(nodes)),
block=(nthreads_per_block, 1, 1),
grid=(nblocks_this_iter, 1))
parent_nodes = gpu_parent_nodes.get()
if max_ratio is not None:
areas = node_areas(parent_nodes)
child_areas = node_areas(nodes)
excessive_area = np.zeros(shape=len(areas), dtype=bool)
for i, parent_area in enumerate(areas):
nchild = parent_nodes['w'][i] >> CHILD_BITS
child_index = parent_nodes['w'][i] & ~NCHILD_MASK
child_area = child_areas[child_index:child_index + nchild].sum()
#if parent_area > 1e9:
# print i, 'Children: %e, Parent: %e' % (child_area, parent_area)
if child_area / parent_area < 0.3:
excessive_area[i] = True
#print i, 'Children: %e, Parent: %e' % (child_area, parent_area)
extra_slots = round_up_to_multiple(
(degree - 1) * np.count_nonzero(excessive_area), 1)
print 'Extra slots:', extra_slots
new_parent_nodes = np.zeros(shape=len(parent_nodes) + extra_slots,
dtype=parent_nodes.dtype)
new_parent_nodes[:len(parent_nodes)] = parent_nodes
offset = 0
for count, index in enumerate(np.argwhere(excessive_area)):
index = index[0] + offset
nchild = new_parent_nodes['w'][index] >> CHILD_BITS
child_index = new_parent_nodes['w'][index] & ~NCHILD_MASK
new_parent_nodes[index] = nodes[child_index]
#new_parent_nodes['w'][index] = 1 << CHILD_BITS | child_index
tmp_nchild = new_parent_nodes['w'][index] >> CHILD_BITS
tmp_child_index = new_parent_nodes['w'][index] & ~NCHILD_MASK
new_parent_nodes['w'][index] = tmp_nchild << CHILD_BITS | (
tmp_child_index + len(nodes))
if nchild == 1:
continue
# slide everyone over
#print index, nchild, len(new_parent_nodes)
new_parent_nodes[index + nchild:] = new_parent_nodes[index +
1:-nchild + 1]
offset += nchild - 1
for sibling in xrange(nchild - 1):
new_parent_index = index + 1 + sibling
new_parent_nodes[new_parent_index] = nodes[child_index +
sibling + 1]
if new_parent_nodes['x'][new_parent_index] != 0:
tmp_nchild = new_parent_nodes['w'][
new_parent_index] >> CHILD_BITS
tmp_child_index = new_parent_nodes['w'][
new_parent_index] & ~NCHILD_MASK
new_parent_nodes['w'][
new_parent_index] = tmp_nchild << CHILD_BITS | (
tmp_child_index + len(nodes))
#new_parent_nodes['w'][new_parent_index] = 1 << CHILD_BITS | (child_index + sibling + 1)
#print 'intermediate: %e' % node_areas(new_parent_nodes).max()
print 'old: %e' % node_areas(parent_nodes).max()
print 'new: %e' % node_areas(new_parent_nodes).max()
if len(new_parent_nodes) < len(nodes):
# Only adopt new set of parent nodes if it actually reduces the
# total number of nodes at this level by 1.
parent_nodes = new_parent_nodes
return parent_nodes
