def concatenate_layers(layers):
bvh_module = get_cu_module('bvh.cu', options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
# Put 0 at beginning of list
layer_bounds = np.insert(np.cumsum(map(len, layers)), 0, 0)
nodes = ga.empty(shape=int(layer_bounds[-1]), dtype=ga.vec.uint4)
nthreads_per_block = 256
for layer_start, layer_end, layer in zip(layer_bounds[:-1],
layer_bounds[1:],
layers):
if layer_end == layer_bounds[-1]:
# leaf nodes need no offset
child_offset = 0
else:
child_offset = layer_end
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(layer_end-layer_start, nthreads_per_block,
max_blocks=10000):
bvh_funcs.copy_and_offset(np.uint32(first_index),
np.uint32(elements_this_iter),
np.uint32(child_offset),
cuda.In(layer),
nodes[layer_start:],
block=(nthreads_per_block,1,1),
grid=(nblocks_this_iter,1))
return nodes.get(), layer_bounds
def __init__(self, photons, ncopies=1, max_time=4.):
"""Load ``photons`` onto the GPU, replicating as requested.
Args:
- photons: chroma.Event.Photons
Photon state information to load onto GPU
- ncopies: int, *optional*
Number of times to replicate the photons
on the GPU. This is used if you want
to propagate the same event many times,
for example in a likelihood calculation.
The amount of GPU storage will be proportionally
larger if ncopies > 1, so be careful.
"""
module = get_cu_module('propagate_hit.cu', options=cuda_options)
propagate_hit_kernel = module.get_function('propagate_hit')
propagate_hit_kernel.prepare('iiPPPPPPPPPPPiiiPPP')
self.propagate_hit_kernel = propagate_hit_kernel
self.gpu_funcs = GPUFuncs(module)
self.max_time = max_time
self.ncopies = ncopies
self.true_nphotons = len(photons)
self.marshall_photons(photons, ncopies)
def concatenate_layers(layers):
bvh_module = get_cu_module('bvh.cu',
options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
# Put 0 at beginning of list
layer_bounds = np.insert(np.cumsum(map(len, layers)), 0, 0)
nodes = ga.empty(shape=int(layer_bounds[-1]), dtype=ga.vec.uint4)
nthreads_per_block = 256
for layer_start, layer_end, layer in zip(layer_bounds[:-1],
layer_bounds[1:], layers):
if layer_end == layer_bounds[-1]:
# leaf nodes need no offset
child_offset = 0
else:
child_offset = layer_end
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(layer_end-layer_start, nthreads_per_block,
max_blocks=10000):
bvh_funcs.copy_and_offset(np.uint32(first_index),
np.uint32(elements_this_iter),
np.uint32(child_offset),
cuda.In(layer),
nodes[layer_start:],
block=(nthreads_per_block, 1, 1),
grid=(nblocks_this_iter, 1))
return nodes.get(), layer_bounds
def merge_nodes_detailed(nodes, first_child, nchild):
'''Merges nodes into len(first_child) parent nodes, using
the provided arrays to determine the index of the first
child of each parent, and how many children there are.'''
bvh_module = get_cu_module('bvh.cu', options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
gpu_nodes = ga.to_gpu(nodes)
gpu_first_child = ga.to_gpu(first_child.astype(np.int32))
gpu_nchild = ga.to_gpu(nchild.astype(np.int32))
nparent = len(first_child)
gpu_parent_nodes = ga.empty(shape=nparent, 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_detailed(np.uint32(first_index),
np.uint32(elements_this_iter),
gpu_nodes,
gpu_parent_nodes,
gpu_first_child,
gpu_nchild,
block=(nthreads_per_block,1,1),
grid=(nblocks_this_iter,1))
return gpu_parent_nodes.get()
def merge_nodes_detailed(nodes, first_child, nchild):
'''Merges nodes into len(first_child) parent nodes, using
the provided arrays to determine the index of the first
child of each parent, and how many children there are.'''
bvh_module = get_cu_module('bvh.cu',
options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
gpu_nodes = ga.to_gpu(nodes)
gpu_first_child = ga.to_gpu(first_child.astype(np.int32))
gpu_nchild = ga.to_gpu(nchild.astype(np.int32))
nparent = len(first_child)
gpu_parent_nodes = ga.empty(shape=nparent, 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_detailed(np.uint32(first_index),
np.uint32(elements_this_iter),
gpu_nodes,
gpu_parent_nodes,
gpu_first_child,
gpu_nchild,
block=(nthreads_per_block, 1, 1),
grid=(nblocks_this_iter, 1))
return gpu_parent_nodes.get()
def __init__(self, pos, dir, max_alpha_depth=10, nblocks=64):
self.pos = ga.to_gpu(to_float3(pos))
self.dir = ga.to_gpu(to_float3(dir))
self.max_alpha_depth = max_alpha_depth
self.nblocks = nblocks
transform_module = get_cu_module('transform.cu', options=cuda_options)
self.transform_funcs = GPUFuncs(transform_module)
render_module = get_cu_module('render.cu', options=cuda_options)
self.render_funcs = GPUFuncs(render_module)
self.dx = ga.empty(max_alpha_depth*self.pos.size, dtype=np.float32)
self.color = ga.empty(self.dx.size, dtype=ga.vec.float4)
self.dxlen = ga.zeros(self.pos.size, dtype=np.uint32)
def __init__(self, pos, dir, max_alpha_depth=10, nblocks=64):
self.pos = ga.to_gpu(to_float3(pos))
self.dir = ga.to_gpu(to_float3(dir))
self.max_alpha_depth = max_alpha_depth
self.nblocks = nblocks
transform_module = get_cu_module('transform.cu', options=cuda_options)
self.transform_funcs = GPUFuncs(transform_module)
render_module = get_cu_module('render.cu', options=cuda_options)
self.render_funcs = GPUFuncs(render_module)
self.dx = ga.empty(max_alpha_depth * self.pos.size, dtype=np.float32)
self.color = ga.empty(self.dx.size, dtype=ga.vec.float4)
self.dxlen = ga.zeros(self.pos.size, dtype=np.uint32)
def __init__(self, photons, ncopies=1):
"""Load ``photons`` onto the GPU, replicating as requested.
Args:
- photons: chroma.Event.Photons
Photon state information to load onto GPU
- ncopies: int, *optional*
Number of times to replicate the photons
on the GPU. This is used if you want
to propagate the same event many times,
for example in a likelihood calculation.
The amount of GPU storage will be proportionally
larger if ncopies > 1, so be careful.
"""
nphotons = len(photons)
self.pos = ga.empty(shape=nphotons*ncopies, dtype=ga.vec.float3)
self.dir = ga.empty(shape=nphotons*ncopies, dtype=ga.vec.float3)
self.pol = ga.empty(shape=nphotons*ncopies, dtype=ga.vec.float3)
self.wavelengths = ga.empty(shape=nphotons*ncopies, dtype=np.float32)
self.t = ga.empty(shape=nphotons*ncopies, dtype=np.float32)
self.last_hit_triangles = ga.empty(shape=nphotons*ncopies, dtype=np.int32)
self.flags = ga.empty(shape=nphotons*ncopies, dtype=np.uint32)
self.weights = ga.empty(shape=nphotons*ncopies, dtype=np.float32)
# Assign the provided photons to the beginning (possibly
# the entire array if ncopies is 1
self.pos[:nphotons].set(to_float3(photons.pos))
self.dir[:nphotons].set(to_float3(photons.dir))
self.pol[:nphotons].set(to_float3(photons.pol))
self.wavelengths[:nphotons].set(photons.wavelengths.astype(np.float32))
self.t[:nphotons].set(photons.t.astype(np.float32))
self.last_hit_triangles[:nphotons].set(photons.last_hit_triangles.astype(np.int32))
self.flags[:nphotons].set(photons.flags.astype(np.uint32))
self.weights[:nphotons].set(photons.weights.astype(np.float32))
module = get_cu_module('propagate.cu', options=cuda_options)
self.gpu_funcs = GPUFuncs(module)
# Replicate the photons to the rest of the slots if needed
if ncopies > 1:
max_blocks = 1024
nthreads_per_block = 64
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.photon_duplicate(np.int32(first_photon), np.int32(photons_this_round),
self.pos, self.dir, self.wavelengths, self.pol, self.t,
self.flags, self.last_hit_triangles, self.weights,
np.int32(ncopies-1),
np.int32(nphotons),
block=(nthreads_per_block,1,1), grid=(blocks, 1))
# Save the duplication information for the iterate_copies() method
self.true_nphotons = nphotons
self.ncopies = ncopies
def __init__(self, gpu_detector, ndaq=1):
self.earliest_time_gpu = ga.empty(gpu_detector.nchannels*ndaq, dtype=np.float32)
self.earliest_time_int_gpu = ga.empty(gpu_detector.nchannels*ndaq, dtype=np.uint32)
self.channel_history_gpu = ga.zeros_like(self.earliest_time_int_gpu)
self.channel_q_int_gpu = ga.zeros_like(self.earliest_time_int_gpu)
self.channel_q_gpu = ga.zeros(len(self.earliest_time_int_gpu), dtype=np.float32)
self.detector_gpu = gpu_detector.detector_gpu
self.solid_id_map_gpu = gpu_detector.solid_id_map
self.solid_id_to_channel_index_gpu = gpu_detector.solid_id_to_channel_index_gpu
self.module = get_cu_module('daq.cu', options=cuda_options,
include_source_directory=True)
self.gpu_funcs = GPUFuncs(self.module)
self.ndaq = ndaq
self.stride = gpu_detector.nchannels
def area_sort_nodes(gpu_geometry, layer_bounds):
bvh_module = get_cu_module('bvh.cu', options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
bounds = zip(layer_bounds[:-1], layer_bounds[1:])[:-1]
bounds.reverse()
nthreads_per_block = 256
for start, end in bounds:
bvh_funcs.area_sort_child(np.uint32(start),
np.uint32(end),
gpu_geometry,
block=(nthreads_per_block,1,1),
grid=(120,1))
return gpu_geometry.nodes.get()
def color_solids(self, solid_hit, colors, nblocks_per_thread=64,
max_blocks=1024):
solid_hit_gpu = ga.to_gpu(np.array(solid_hit, dtype=np.bool))
solid_colors_gpu = ga.to_gpu(np.array(colors, dtype=np.uint32))
module = get_cu_module('mesh.h', options=cuda_options)
color_solids = module.get_function('color_solids')
for first_triangle, triangles_this_round, blocks in \
chunk_iterator(self.triangles.size, nblocks_per_thread,
max_blocks):
color_solids(np.int32(first_triangle),
np.int32(triangles_this_round), self.solid_id_map,
solid_hit_gpu, solid_colors_gpu, self.gpudata,
block=(nblocks_per_thread,1,1),
grid=(blocks,1))
def color_solids(self, solid_hit, colors, nblocks_per_thread=64,
max_blocks=1024):
solid_hit_gpu = ga.to_gpu(np.array(solid_hit, dtype=np.bool))
solid_colors_gpu = ga.to_gpu(np.array(colors, dtype=np.uint32))
module = get_cu_module('mesh.h', options=cuda_options)
color_solids = module.get_function('color_solids')
for first_triangle, triangles_this_round, blocks in \
chunk_iterator(self.triangles.size, nblocks_per_thread,
max_blocks):
color_solids(np.int32(first_triangle),
np.int32(triangles_this_round), self.solid_id_map,
solid_hit_gpu, solid_colors_gpu, self.gpudata,
block=(nblocks_per_thread,1,1),
grid=(blocks,1))
def area_sort_nodes(gpu_geometry, layer_bounds):
bvh_module = get_cu_module('bvh.cu',
options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
bounds = zip(layer_bounds[:-1], layer_bounds[1:])[:-1]
bounds.reverse()
nthreads_per_block = 256
for start, end in bounds:
bvh_funcs.area_sort_child(np.uint32(start),
np.uint32(end),
gpu_geometry,
block=(nthreads_per_block, 1, 1),
grid=(120, 1))
return gpu_geometry.nodes.get()
def collapse_chains(nodes, layer_bounds):
bvh_module = get_cu_module('bvh.cu', options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
gpu_nodes = ga.to_gpu(nodes)
bounds = zip(layer_bounds[:-1], layer_bounds[1:])[:-1]
bounds.reverse()
nthreads_per_block = 256
for start, end in bounds:
bvh_funcs.collapse_child(np.uint32(start),
np.uint32(end),
gpu_nodes,
block=(nthreads_per_block,1,1),
grid=(120,1))
return gpu_nodes.get()
def collapse_chains(nodes, layer_bounds):
bvh_module = get_cu_module('bvh.cu',
options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
gpu_nodes = ga.to_gpu(nodes)
bounds = zip(layer_bounds[:-1], layer_bounds[1:])[:-1]
bounds.reverse()
nthreads_per_block = 256
for start, end in bounds:
bvh_funcs.collapse_child(np.uint32(start),
np.uint32(end),
gpu_nodes,
block=(nthreads_per_block, 1, 1),
grid=(120, 1))
return gpu_nodes.get()
def __init__(self, pos, dir, pol, wavelengths, t, last_hit_triangles,
flags, weights):
'''Create new object using slices of GPUArrays from an instance
of GPUPhotons. NOTE THESE ARE NOT CPU ARRAYS!'''
self.pos = pos
self.dir = dir
self.pol = pol
self.wavelengths = wavelengths
self.t = t
self.last_hit_triangles = last_hit_triangles
self.flags = flags
self.weights = weights
module = get_cu_module('propagate.cu', options=cuda_options)
self.gpu_funcs = GPUFuncs(module)
self.true_nphotons = len(pos)
self.ncopies = 1
def __init__(self, pos, dir, pol, wavelengths, t, last_hit_triangles,
flags, weights):
'''Create new object using slices of GPUArrays from an instance
of GPUPhotons. NOTE THESE ARE NOT CPU ARRAYS!'''
self.pos = pos
self.dir = dir
self.pol = pol
self.wavelengths = wavelengths
self.t = t
self.last_hit_triangles = last_hit_triangles
self.flags = flags
self.weights = weights
module = get_cu_module('propagate.cu', options=cuda_options)
self.gpu_funcs = GPUFuncs(module)
self.true_nphotons = len(pos)
self.ncopies = 1
def __init__(self,
photons,
ncopies=1,
copy_flags=True,
copy_triangles=True,
copy_weights=True):
"""Load ``photons`` onto the GPU, replicating as requested.
Args:
- photons: chroma.Event.Photons
Photon state information to load onto GPU
- ncopies: int, *optional*
Number of times to replicate the photons
on the GPU. This is used if you want
to propagate the same event many times,
for example in a likelihood calculation.
The amount of GPU storage will be proportionally
larger if ncopies > 1, so be careful.
"""
nphotons = len(photons)
self.pos = ga.empty(shape=nphotons * ncopies, dtype=ga.vec.float3)
self.dir = ga.empty(shape=nphotons * ncopies, dtype=ga.vec.float3)
self.pol = ga.empty(shape=nphotons * ncopies, dtype=ga.vec.float3)
self.wavelengths = ga.empty(shape=nphotons * ncopies, dtype=np.float32)
self.t = ga.empty(shape=nphotons * ncopies, dtype=np.float32)
self.last_hit_triangles = ga.empty(shape=nphotons * ncopies,
dtype=np.int32)
if not copy_triangles:
self.last_hit_triangles.fill(-1)
if not copy_flags:
self.flags = ga.zeros(shape=nphotons * ncopies, dtype=np.uint32)
else:
self.flags = ga.empty(shape=nphotons * ncopies, dtype=np.uint32)
if not copy_weights:
self.weights = ga.ones_like(self.last_hit_triangles,
dtype=np.float32)
else:
self.weights = ga.empty(shape=nphotons * ncopies, dtype=np.float32)
self.evidx = ga.empty(shape=nphotons, dtype=np.uint32)
# Assign the provided photons to the beginning (possibly
# the entire array if ncopies is 1
self.pos[:nphotons].set(to_float3(photons.pos))
self.dir[:nphotons].set(to_float3(photons.dir))
self.pol[:nphotons].set(to_float3(photons.pol))
self.wavelengths[:nphotons].set(photons.wavelengths.astype(np.float32))
self.t[:nphotons].set(photons.t.astype(np.float32))
if copy_triangles:
self.last_hit_triangles[:nphotons].set(
photons.last_hit_triangles.astype(np.int32))
if copy_flags:
self.flags[:nphotons].set(photons.flags.astype(np.uint32))
if copy_weights:
self.weights[:nphotons].set(photons.weights.astype(np.float32))
self.evidx[:nphotons].set(photons.evidx.astype(np.uint32))
module = get_cu_module('propagate.cu', options=cuda_options)
self.gpu_funcs = GPUFuncs(module)
# Replicate the photons to the rest of the slots if needed
if ncopies > 1:
max_blocks = 1024
nthreads_per_block = 64
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.photon_duplicate(np.int32(first_photon),
np.int32(photons_this_round),
self.pos,
self.dir,
self.wavelengths,
self.pol,
self.t,
self.flags,
self.last_hit_triangles,
self.weights,
self.evidx,
np.int32(ncopies - 1),
np.int32(nphotons),
block=(nthreads_per_block, 1,
1),
grid=(blocks, 1))
# Save the duplication information for the iterate_copies() method
self.true_nphotons = nphotons
self.ncopies = ncopies
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 create_leaf_nodes(mesh, morton_bits=16, round_to_multiple=1):
'''Compute the leaf nodes surrounding a triangle mesh.
``mesh``: chroma.geometry.Mesh
Triangles to box
``morton_bits``: int
Number of bits to use per dimension when computing Morton code.
``round_to_multiple``: int
Round the number of nodes created up to multiple of this number
Extra nodes will be all zero.
Returns (world_coords, nodes, morton_codes), where
``world_coords``: chroma.bvh.WorldCoords
Defines the fixed point coordinate system
``nodes``: ndarray(shape=len(mesh.triangles), dtype=uint4)
List of leaf nodes. Child IDs will be set to triangle offsets.
``morton_codes``: ndarray(shape=len(mesh.triangles), dtype=np.uint64)
Morton codes for each triangle, using ``morton_bits`` per axis.
Must be <= 16 bits.
'''
# Load GPU functions
bvh_module = get_cu_module('bvh.cu',
options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
# compute world coordinates
world_origin = mesh.vertices.min(axis=0)
world_scale = np.max((mesh.vertices.max(axis=0) - world_origin)) \
/ (2**16 - 2)
world_coords = WorldCoords(world_origin=world_origin,
world_scale=world_scale)
# Put triangles and vertices in mapped host memory
triangles = mapped_empty(shape=len(mesh.triangles),
dtype=ga.vec.uint3,
write_combined=True)
triangles[:] = to_uint3(mesh.triangles)
vertices = mapped_empty(shape=len(mesh.vertices),
dtype=ga.vec.float3,
write_combined=True)
vertices[:] = to_float3(mesh.vertices)
# Call GPU to compute nodes
nodes = ga.zeros(shape=round_up_to_multiple(len(triangles),
round_to_multiple),
dtype=ga.vec.uint4)
morton_codes = ga.empty(shape=len(triangles), dtype=np.uint64)
# Convert world coords to GPU-friendly types
world_origin = ga.vec.make_float3(*world_origin)
world_scale = np.float32(world_scale)
nthreads_per_block = 256
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(len(triangles), nthreads_per_block,
max_blocks=30000):
bvh_funcs.make_leaves(np.uint32(first_index),
np.uint32(elements_this_iter),
Mapped(triangles),
Mapped(vertices),
world_origin,
world_scale,
nodes,
morton_codes,
block=(nthreads_per_block, 1, 1),
grid=(nblocks_this_iter, 1))
morton_codes_host = morton_codes.get() >> (16 - morton_bits)
return world_coords, nodes.get(), morton_codes_host
def optimize_layer(orig_nodes):
bvh_module = get_cu_module('bvh.cu',
options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
nodes = ga.to_gpu(orig_nodes)
n = len(nodes)
areas = ga.empty(shape=n / 2, dtype=np.uint64)
nthreads_per_block = 128
min_areas = ga.empty(shape=int(np.ceil(n / float(nthreads_per_block))),
dtype=np.uint64)
min_index = ga.empty(shape=min_areas.shape, dtype=np.uint32)
update = 10000
skip_size = 1
flag = mapped_empty(shape=skip_size, dtype=np.uint32)
i = 0
skips = 0
swaps = 0
while i < n / 2 - 1:
# How are we doing?
if i % update == 0:
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(n/2, nthreads_per_block, max_blocks=10000):
bvh_funcs.pair_area(np.uint32(first_index),
np.uint32(elements_this_iter),
nodes,
areas,
block=(nthreads_per_block, 1, 1),
grid=(nblocks_this_iter, 1))
areas_host = areas.get()
#print nodes.get(), areas_host.astype(float)
print 'Area of parent layer so far (%d): %1.12e' % (
i * 2, areas_host.astype(float).sum())
print 'Skips: %d, Swaps: %d' % (skips, swaps)
test_index = i * 2
blocks = 0
look_forward = min(8192 * 50, n - test_index - 2)
skip_this_round = min(skip_size, n - test_index - 1)
flag[:] = 0
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(look_forward, nthreads_per_block, max_blocks=10000):
bvh_funcs.min_distance_to(np.uint32(first_index + test_index + 2),
np.uint32(elements_this_iter),
np.uint32(test_index),
nodes,
np.uint32(blocks),
min_areas,
min_index,
Mapped(flag),
block=(nthreads_per_block, 1, 1),
grid=(nblocks_this_iter,
skip_this_round))
blocks += nblocks_this_iter
#print i, first_index, nblocks_this_iter, look_forward
cuda.Context.get_current().synchronize()
if flag[0] == 0:
flag_nonzero = flag.nonzero()[0]
if len(flag_nonzero) == 0:
no_swap_required = skip_size
else:
no_swap_required = flag_nonzero[0]
i += no_swap_required
skips += no_swap_required
continue
min_areas_host = min_areas[:blocks].get()
min_index_host = min_index[:blocks].get()
best_block = min_areas_host.argmin()
better_i = min_index_host[best_block]
swaps += 1
#print 'swap', test_index+1, better_i
assert 0 < better_i < len(nodes)
assert 0 < test_index + 1 < len(nodes)
bvh_funcs.swap(np.uint32(test_index + 1),
np.uint32(better_i),
nodes,
block=(1, 1, 1),
grid=(1, 1))
cuda.Context.get_current().synchronize()
i += 1
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(n/2, nthreads_per_block, max_blocks=10000):
bvh_funcs.pair_area(np.uint32(first_index),
np.uint32(elements_this_iter),
nodes,
areas,
block=(nthreads_per_block, 1, 1),
grid=(nblocks_this_iter, 1))
areas_host = areas.get()
print 'Final area of parent layer: %1.12e' % areas_host.sum()
print 'Skips: %d, Swaps: %d' % (skips, swaps)
return nodes.get()
def __init__(self):
self.module = get_cu_module('pdf.cu', options=cuda_options,
include_source_directory=True)
self.gpu_funcs = GPUFuncs(self.module)
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 create_leaf_nodes(mesh, morton_bits=16, round_to_multiple=1):
'''Compute the leaf nodes surrounding a triangle mesh.
``mesh``: chroma.geometry.Mesh
Triangles to box
``morton_bits``: int
Number of bits to use per dimension when computing Morton code.
``round_to_multiple``: int
Round the number of nodes created up to multiple of this number
Extra nodes will be all zero.
Returns (world_coords, nodes, morton_codes), where
``world_coords``: chroma.bvh.WorldCoords
Defines the fixed point coordinate system
``nodes``: ndarray(shape=len(mesh.triangles), dtype=uint4)
List of leaf nodes. Child IDs will be set to triangle offsets.
``morton_codes``: ndarray(shape=len(mesh.triangles), dtype=np.uint64)
Morton codes for each triangle, using ``morton_bits`` per axis.
Must be <= 16 bits.
'''
# Load GPU functions
bvh_module = get_cu_module('bvh.cu', options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
# compute world coordinates
world_origin = mesh.vertices.min(axis=0)
world_scale = np.max((mesh.vertices.max(axis=0) - world_origin)) \
/ (2**16 - 2)
world_coords = WorldCoords(world_origin=world_origin,
world_scale=world_scale)
# Put triangles and vertices in mapped host memory
triangles = mapped_empty(shape=len(mesh.triangles), dtype=ga.vec.uint3,
write_combined=True)
triangles[:] = to_uint3(mesh.triangles)
vertices = mapped_empty(shape=len(mesh.vertices), dtype=ga.vec.float3,
write_combined=True)
vertices[:] = to_float3(mesh.vertices)
# Call GPU to compute nodes
nodes = ga.zeros(shape=round_up_to_multiple(len(triangles),
round_to_multiple),
dtype=ga.vec.uint4)
morton_codes = ga.empty(shape=len(triangles), dtype=np.uint64)
# Convert world coords to GPU-friendly types
world_origin = ga.vec.make_float3(*world_origin)
world_scale = np.float32(world_scale)
nthreads_per_block = 256
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(len(triangles), nthreads_per_block,
max_blocks=30000):
bvh_funcs.make_leaves(np.uint32(first_index),
np.uint32(elements_this_iter),
Mapped(triangles), Mapped(vertices),
world_origin, world_scale,
nodes, morton_codes,
block=(nthreads_per_block,1,1),
grid=(nblocks_this_iter,1))
morton_codes_host = morton_codes.get() >> (16 - morton_bits)
return world_coords, nodes.get(), morton_codes_host
def __init__(self):
self.module = get_cu_module('pdf.cu',
options=cuda_options,
include_source_directory=True)
self.gpu_funcs = GPUFuncs(self.module)
def optimize_layer(orig_nodes):
bvh_module = get_cu_module('bvh.cu', options=cuda_options,
include_source_directory=True)
bvh_funcs = GPUFuncs(bvh_module)
nodes = ga.to_gpu(orig_nodes)
n = len(nodes)
areas = ga.empty(shape=n/2, dtype=np.uint64)
nthreads_per_block = 128
min_areas = ga.empty(shape=int(np.ceil(n/float(nthreads_per_block))), dtype=np.uint64)
min_index = ga.empty(shape=min_areas.shape, dtype=np.uint32)
update = 10000
skip_size = 1
flag = mapped_empty(shape=skip_size, dtype=np.uint32)
i = 0
skips = 0
swaps = 0
while i < n/2 - 1:
# How are we doing?
if i % update == 0:
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(n/2, nthreads_per_block, max_blocks=10000):
bvh_funcs.pair_area(np.uint32(first_index),
np.uint32(elements_this_iter),
nodes,
areas,
block=(nthreads_per_block,1,1),
grid=(nblocks_this_iter,1))
areas_host = areas.get()
#print nodes.get(), areas_host.astype(float)
print 'Area of parent layer so far (%d): %1.12e' % (i*2, areas_host.astype(float).sum())
print 'Skips: %d, Swaps: %d' % (skips, swaps)
test_index = i * 2
blocks = 0
look_forward = min(8192*50, n - test_index - 2)
skip_this_round = min(skip_size, n - test_index - 1)
flag[:] = 0
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(look_forward, nthreads_per_block, max_blocks=10000):
bvh_funcs.min_distance_to(np.uint32(first_index + test_index + 2),
np.uint32(elements_this_iter),
np.uint32(test_index),
nodes,
np.uint32(blocks),
min_areas,
min_index,
Mapped(flag),
block=(nthreads_per_block,1,1),
grid=(nblocks_this_iter, skip_this_round))
blocks += nblocks_this_iter
#print i, first_index, nblocks_this_iter, look_forward
cuda.Context.get_current().synchronize()
if flag[0] == 0:
flag_nonzero = flag.nonzero()[0]
if len(flag_nonzero) == 0:
no_swap_required = skip_size
else:
no_swap_required = flag_nonzero[0]
i += no_swap_required
skips += no_swap_required
continue
min_areas_host = min_areas[:blocks].get()
min_index_host = min_index[:blocks].get()
best_block = min_areas_host.argmin()
better_i = min_index_host[best_block]
swaps += 1
#print 'swap', test_index+1, better_i
assert 0 < better_i < len(nodes)
assert 0 < test_index + 1 < len(nodes)
bvh_funcs.swap(np.uint32(test_index+1), np.uint32(better_i),
nodes, block=(1,1,1), grid=(1,1))
cuda.Context.get_current().synchronize()
i += 1
for first_index, elements_this_iter, nblocks_this_iter in \
chunk_iterator(n/2, nthreads_per_block, max_blocks=10000):
bvh_funcs.pair_area(np.uint32(first_index),
np.uint32(elements_this_iter),
nodes,
areas,
block=(nthreads_per_block,1,1),
grid=(nblocks_this_iter,1))
areas_host = areas.get()
print 'Final area of parent layer: %1.12e' % areas_host.sum()
print 'Skips: %d, Swaps: %d' % (skips, swaps)
return nodes.get()
