class GPUPhotons(object):
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 get(self):
pos = self.pos.get().view(np.float32).reshape((len(self.pos),3))
dir = self.dir.get().view(np.float32).reshape((len(self.dir),3))
pol = self.pol.get().view(np.float32).reshape((len(self.pol),3))
wavelengths = self.wavelengths.get()
t = self.t.get()
last_hit_triangles = self.last_hit_triangles.get()
flags = self.flags.get()
weights = self.weights.get()
return event.Photons(pos, dir, pol, wavelengths, t, last_hit_triangles, flags, weights)
def get_hits(self, gpu_detector, target_flag=(0x1<<2), nthreads_per_block=64, max_blocks=1024,
start_photon=None, nphotons=None):
'''Return a map of GPUPhoton objects containing only photons that
have a particular bit set in their history word and were detected by
a channel.'''
cuda.Context.get_current().synchronize()
index_counter_gpu = ga.zeros(shape=1, dtype=np.uint32)
cuda.Context.get_current().synchronize()
if start_photon is None:
start_photon = 0
if nphotons is None:
nphotons = self.pos.size - start_photon
# First count how much space we need
for first_photon, photons_this_round, blocks in chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.count_photon_hits(np.int32(start_photon+first_photon),
np.int32(photons_this_round),
np.uint32(target_flag),
self.flags,
gpu_detector.solid_id_map,
self.last_hit_triangles,
gpu_detector.detector_gpu,
index_counter_gpu,
block=(nthreads_per_block,1,1),
grid=(blocks, 1))
cuda.Context.get_current().synchronize()
reduced_nphotons = int(index_counter_gpu.get()[0])
# Then allocate new storage space
pos = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
dir = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
pol = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
wavelengths = ga.empty(shape=reduced_nphotons, dtype=np.float32)
t = ga.empty(shape=reduced_nphotons, dtype=np.float32)
last_hit_triangles = ga.empty(shape=reduced_nphotons, dtype=np.int32)
flags = ga.empty(shape=reduced_nphotons, dtype=np.uint32)
weights = ga.empty(shape=reduced_nphotons, dtype=np.float32)
channels = ga.empty(shape=reduced_nphotons, dtype=np.int32)
# And finaly copy hits, if there are any
if reduced_nphotons > 0:
index_counter_gpu.fill(0)
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.copy_photon_hits(np.int32(start_photon+first_photon),
np.int32(photons_this_round),
np.uint32(target_flag),
gpu_detector.solid_id_map,
gpu_detector.detector_gpu,
index_counter_gpu,
self.pos, self.dir, self.wavelengths, self.pol, self.t, self.flags, self.last_hit_triangles, self.weights,
pos, dir, wavelengths, pol, t, flags, last_hit_triangles, weights, channels,
block=(nthreads_per_block,1,1),
grid=(blocks, 1))
assert index_counter_gpu.get()[0] == reduced_nphotons
pos = pos.get().view(np.float32).reshape((len(pos),3))
dir = dir.get().view(np.float32).reshape((len(dir),3))
pol = pol.get().view(np.float32).reshape((len(pol),3))
wavelengths = wavelengths.get()
t = t.get()
last_hit_triangles = last_hit_triangles.get()
flags = flags.get()
weights = weights.get()
channels = channels.get()
hitmap = {}
for chan in np.unique(channels):
mask = (channels == chan).astype(bool)
hitmap[chan] = event.Photons(pos[mask], dir[mask], pol[mask], wavelengths[mask], t[mask], last_hit_triangles[mask], flags[mask], weights[mask])
return hitmap
def iterate_copies(self):
'''Returns an iterator that yields GPUPhotonsSlice objects
corresponding to the event copies stored in ``self``.'''
for i in xrange(self.ncopies):
window = slice(self.true_nphotons*i, self.true_nphotons*(i+1))
yield GPUPhotonsSlice(pos=self.pos[window],
dir=self.dir[window],
pol=self.pol[window],
wavelengths=self.wavelengths[window],
t=self.t[window],
last_hit_triangles=self.last_hit_triangles[window],
flags=self.flags[window],
weights=self.weights[window])
@profile_if_possible
def propagate(self, gpu_geometry, rng_states, nthreads_per_block=64,
max_blocks=1024, max_steps=10, use_weights=False,
scatter_first=0):
"""Propagate photons on GPU to termination or max_steps, whichever
comes first.
May be called repeatedly without reloading photon information if
single-stepping through photon history.
..warning::
`rng_states` must have at least `nthreads_per_block`*`max_blocks`
number of curandStates.
"""
nphotons = self.pos.size
step = 0
input_queue = np.empty(shape=nphotons+1, dtype=np.uint32)
input_queue[0] = 0
# Order photons initially in the queue to put the clones next to each other
for copy in xrange(self.ncopies):
input_queue[1+copy::self.ncopies] = np.arange(self.true_nphotons, dtype=np.uint32) + copy * self.true_nphotons
input_queue_gpu = ga.to_gpu(input_queue)
output_queue = np.zeros(shape=nphotons+1, dtype=np.uint32)
output_queue[0] = 1
output_queue_gpu = ga.to_gpu(output_queue)
while step < max_steps:
# Just finish the rest of the steps if the # of photons is low
if nphotons < nthreads_per_block * 16 * 8 or use_weights:
nsteps = max_steps - step
else:
nsteps = 1
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.propagate(np.int32(first_photon), np.int32(photons_this_round), input_queue_gpu[1:], output_queue_gpu, rng_states, self.pos, self.dir, self.wavelengths, self.pol, self.t, self.flags, self.last_hit_triangles, self.weights, np.int32(nsteps), np.int32(use_weights), np.int32(scatter_first), gpu_geometry.gpudata, block=(nthreads_per_block,1,1), grid=(blocks, 1))
step += nsteps
scatter_first = 0 # Only allow non-zero in first pass
if step < max_steps:
temp = input_queue_gpu
input_queue_gpu = output_queue_gpu
output_queue_gpu = temp
# Assign with a numpy array of length 1 to silence
# warning from PyCUDA about setting array with different strides/storage orders.
output_queue_gpu[:1].set(np.ones(shape=1, dtype=np.uint32))
nphotons = input_queue_gpu[:1].get()[0] - 1
if ga.max(self.flags).get() & (1 << 31):
print >>sys.stderr, "WARNING: ABORTED PHOTONS"
cuda.Context.get_current().synchronize()
@profile_if_possible
def select(self, target_flag, nthreads_per_block=64, max_blocks=1024,
start_photon=None, nphotons=None):
'''Return a new GPUPhoton object containing only photons that
have a particular bit set in their history word.'''
cuda.Context.get_current().synchronize()
index_counter_gpu = ga.zeros(shape=1, dtype=np.uint32)
cuda.Context.get_current().synchronize()
if start_photon is None:
start_photon = 0
if nphotons is None:
nphotons = self.pos.size - start_photon
# First count how much space we need
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.count_photons(np.int32(start_photon+first_photon),
np.int32(photons_this_round),
np.uint32(target_flag),
index_counter_gpu, self.flags,
block=(nthreads_per_block,1,1),
grid=(blocks, 1))
cuda.Context.get_current().synchronize()
reduced_nphotons = int(index_counter_gpu.get()[0])
# Then allocate new storage space
pos = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
dir = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
pol = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
wavelengths = ga.empty(shape=reduced_nphotons, dtype=np.float32)
t = ga.empty(shape=reduced_nphotons, dtype=np.float32)
last_hit_triangles = ga.empty(shape=reduced_nphotons, dtype=np.int32)
flags = ga.empty(shape=reduced_nphotons, dtype=np.uint32)
weights = ga.empty(shape=reduced_nphotons, dtype=np.float32)
# And finaly copy photons, if there are any
if reduced_nphotons > 0:
index_counter_gpu.fill(0)
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.copy_photons(np.int32(start_photon+first_photon),
np.int32(photons_this_round),
np.uint32(target_flag),
index_counter_gpu,
self.pos, self.dir, self.wavelengths, self.pol, self.t, self.flags, self.last_hit_triangles, self.weights,
pos, dir, wavelengths, pol, t, flags, last_hit_triangles, weights,
block=(nthreads_per_block,1,1),
grid=(blocks, 1))
assert index_counter_gpu.get()[0] == reduced_nphotons
return GPUPhotonsSlice(pos, dir, pol, wavelengths, t, last_hit_triangles, flags, weights)
def __del__(self):
del self.pos
del self.dir
del self.pol
del self.wavelengths
del self.t
del self.flags
del self.last_hit_triangles
# Free up GPU memory quickly if now available
gc.collect()
def __len__(self):
return self.pos.size
class GPUPhotons(object):
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 get(self):
pos = self.pos.get().view(np.float32).reshape((len(self.pos), 3))
dir = self.dir.get().view(np.float32).reshape((len(self.dir), 3))
pol = self.pol.get().view(np.float32).reshape((len(self.pol), 3))
wavelengths = self.wavelengths.get()
t = self.t.get()
last_hit_triangles = self.last_hit_triangles.get()
flags = self.flags.get()
weights = self.weights.get()
evidx = self.evidx.get()
return event.Photons(pos, dir, pol, wavelengths, t, last_hit_triangles,
flags, weights, evidx)
def get_hits(self, *args, **kwargs):
'''Return a map of GPUPhoton objects containing only photons that
have a particular bit set in their history word and were detected by
a channel.'''
flat_hits = self.get_flat_hits(*args, **kwargs)
hitmap = {}
for chan in np.unique(flat_hits.channel):
mask = (flat_hits.channel == chan).astype(bool)
hitmap[int(chan)] = flat_hits[mask]
return hitmap
def get_flat_hits(self,
gpu_detector,
target_flag=(0x1 << 2),
nthreads_per_block=64,
max_blocks=1024,
start_photon=None,
nphotons=None,
no_map=False):
'''GPUPhoton objects containing only photons that
have a particular bit set in their history word and were detected by
a channel.'''
cuda.Context.get_current().synchronize()
index_counter_gpu = ga.zeros(shape=1, dtype=np.uint32)
cuda.Context.get_current().synchronize()
if start_photon is None:
start_photon = 0
if nphotons is None:
nphotons = self.pos.size - start_photon
# First count how much space we need
for first_photon, photons_this_round, blocks in chunk_iterator(
nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.count_photon_hits(np.int32(start_photon +
first_photon),
np.int32(photons_this_round),
np.uint32(target_flag),
self.flags,
gpu_detector.solid_id_map,
self.last_hit_triangles,
gpu_detector.detector_gpu,
index_counter_gpu,
block=(nthreads_per_block, 1, 1),
grid=(blocks, 1))
cuda.Context.get_current().synchronize()
reduced_nphotons = int(index_counter_gpu.get()[0])
# Then allocate new storage space
pos = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
dir = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
pol = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
wavelengths = ga.empty(shape=reduced_nphotons, dtype=np.float32)
t = ga.empty(shape=reduced_nphotons, dtype=np.float32)
last_hit_triangles = ga.empty(shape=reduced_nphotons, dtype=np.int32)
flags = ga.empty(shape=reduced_nphotons, dtype=np.uint32)
weights = ga.empty(shape=reduced_nphotons, dtype=np.float32)
evidx = ga.empty(shape=reduced_nphotons, dtype=np.uint32)
channels = ga.empty(shape=reduced_nphotons, dtype=np.int32)
# And finaly copy hits, if there are any
if reduced_nphotons > 0:
index_counter_gpu.fill(0)
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.copy_photon_hits(
np.int32(start_photon + first_photon),
np.int32(photons_this_round),
np.uint32(target_flag),
gpu_detector.solid_id_map,
gpu_detector.detector_gpu,
index_counter_gpu,
self.pos,
self.dir,
self.wavelengths,
self.pol,
self.t,
self.flags,
self.last_hit_triangles,
self.weights,
self.evidx,
pos,
dir,
wavelengths,
pol,
t,
flags,
last_hit_triangles,
weights,
evidx,
channels,
block=(nthreads_per_block, 1, 1),
grid=(blocks, 1))
assert index_counter_gpu.get()[0] == reduced_nphotons
pos = pos.get().view(np.float32).reshape((len(pos), 3))
dir = dir.get().view(np.float32).reshape((len(dir), 3))
pol = pol.get().view(np.float32).reshape((len(pol), 3))
wavelengths = wavelengths.get()
t = t.get()
last_hit_triangles = last_hit_triangles.get()
flags = flags.get()
weights = weights.get()
evidx = evidx.get()
channels = channels.get()
hitmap = {}
return event.Photons(pos, dir, pol, wavelengths, t, last_hit_triangles,
flags, weights, evidx, channels)
def iterate_copies(self):
'''Returns an iterator that yields GPUPhotonsSlice objects
corresponding to the event copies stored in ``self``.'''
for i in range(self.ncopies):
window = slice(self.true_nphotons * i,
self.true_nphotons * (i + 1))
yield GPUPhotonsSlice(
pos=self.pos[window],
dir=self.dir[window],
pol=self.pol[window],
wavelengths=self.wavelengths[window],
t=self.t[window],
last_hit_triangles=self.last_hit_triangles[window],
flags=self.flags[window],
weights=self.weights[window],
evidx=self.evidx[window])
@profile_if_possible
def propagate(self,
gpu_geometry,
rng_states,
nthreads_per_block=64,
max_blocks=1024,
max_steps=10,
use_weights=False,
scatter_first=0,
track=False):
"""Propagate photons on GPU to termination or max_steps, whichever
comes first.
May be called repeatedly without reloading photon information if
single-stepping through photon history.
..warning::
`rng_states` must have at least `nthreads_per_block`*`max_blocks`
number of curandStates.
"""
nphotons = self.pos.size
step = 0
input_queue = np.empty(shape=nphotons + 1, dtype=np.uint32)
input_queue[0] = 0
# Order photons initially in the queue to put the clones next to each other
for copy in range(self.ncopies):
input_queue[1 + copy::self.ncopies] = np.arange(
self.true_nphotons,
dtype=np.uint32) + copy * self.true_nphotons
input_queue_gpu = ga.to_gpu(input_queue)
output_queue = np.zeros(shape=nphotons + 1, dtype=np.uint32)
output_queue[0] = 1
output_queue_gpu = ga.to_gpu(output_queue)
if track:
step_photon_ids = []
step_photons = []
#save the first step for all photons in the input queue
step_photon_ids.append(input_queue_gpu[1:nphotons + 1].get())
step_photons.append(
self.copy_queue(input_queue_gpu[1:], nphotons).get())
while step < max_steps:
# Just finish the rest of the steps if the # of photons is low and not tracking
if not track and (nphotons < nthreads_per_block * 16 * 8
or use_weights):
nsteps = max_steps - step
else:
nsteps = 1
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.propagate(np.int32(first_photon),
np.int32(photons_this_round),
input_queue_gpu[1:],
output_queue_gpu,
rng_states,
self.pos,
self.dir,
self.wavelengths,
self.pol,
self.t,
self.flags,
self.last_hit_triangles,
self.weights,
self.evidx,
np.int32(nsteps),
np.int32(use_weights),
np.int32(scatter_first),
gpu_geometry.gpudata,
block=(nthreads_per_block, 1, 1),
grid=(blocks, 1))
if track: #save the next step for all photons in the input queue
step_photon_ids.append(input_queue_gpu[1:nphotons + 1].get())
step_photons.append(
self.copy_queue(input_queue_gpu[1:], nphotons).get())
step += nsteps
scatter_first = 0 # Only allow non-zero in first pass
if step < max_steps:
temp = input_queue_gpu
input_queue_gpu = output_queue_gpu
output_queue_gpu = temp
# Assign with a numpy array of length 1 to silence
# warning from PyCUDA about setting array with different strides/storage orders.
output_queue_gpu[:1].set(np.ones(shape=1, dtype=np.uint32))
nphotons = input_queue_gpu[:1].get()[0] - 1
if nphotons == 0:
break
if ga.max(self.flags).get() & (1 << 31):
print("WARNING: ABORTED PHOTONS", file=sys.stderr)
cuda.Context.get_current().synchronize()
if track:
return step_photon_ids, step_photons
@profile_if_possible
def copy_queue(self,
queue_gpu,
nphotons,
nthreads_per_block=64,
max_blocks=1024,
start_photon=0):
# Allocate new storage space
pos = ga.empty(shape=nphotons, dtype=ga.vec.float3)
dir = ga.empty(shape=nphotons, dtype=ga.vec.float3)
pol = ga.empty(shape=nphotons, dtype=ga.vec.float3)
wavelengths = ga.empty(shape=nphotons, dtype=np.float32)
t = ga.empty(shape=nphotons, dtype=np.float32)
last_hit_triangles = ga.empty(shape=nphotons, dtype=np.int32)
flags = ga.empty(shape=nphotons, dtype=np.uint32)
weights = ga.empty(shape=nphotons, dtype=np.float32)
evidx = ga.empty(shape=nphotons, dtype=np.uint32)
# And finaly copy photons, if there are any
if nphotons > 0:
for first_photon, photons_this_round, blocks in chunk_iterator(
nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.copy_photon_queue(
np.int32(start_photon + first_photon),
np.int32(photons_this_round),
queue_gpu,
self.pos,
self.dir,
self.wavelengths,
self.pol,
self.t,
self.flags,
self.last_hit_triangles,
self.weights,
self.evidx,
pos,
dir,
wavelengths,
pol,
t,
flags,
last_hit_triangles,
weights,
evidx,
block=(nthreads_per_block, 1, 1),
grid=(blocks, 1))
return GPUPhotonsSlice(pos, dir, pol, wavelengths, t,
last_hit_triangles, flags, weights, evidx)
@profile_if_possible
def select(self,
target_flag,
nthreads_per_block=64,
max_blocks=1024,
start_photon=None,
nphotons=None):
'''Return a new GPUPhoton object containing only photons that
have a particular bit set in their history word.'''
cuda.Context.get_current().synchronize()
index_counter_gpu = ga.zeros(shape=1, dtype=np.uint32)
cuda.Context.get_current().synchronize()
if start_photon is None:
start_photon = 0
if nphotons is None:
nphotons = self.pos.size - start_photon
# First count how much space we need
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.count_photons(np.int32(start_photon + first_photon),
np.int32(photons_this_round),
np.uint32(target_flag),
index_counter_gpu,
self.flags,
block=(nthreads_per_block, 1, 1),
grid=(blocks, 1))
cuda.Context.get_current().synchronize()
reduced_nphotons = int(index_counter_gpu.get()[0])
# Then allocate new storage space
pos = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
dir = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
pol = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
wavelengths = ga.empty(shape=reduced_nphotons, dtype=np.float32)
t = ga.empty(shape=reduced_nphotons, dtype=np.float32)
last_hit_triangles = ga.empty(shape=reduced_nphotons, dtype=np.int32)
flags = ga.empty(shape=reduced_nphotons, dtype=np.uint32)
weights = ga.empty(shape=reduced_nphotons, dtype=np.float32)
evidx = ga.empty(shape=reduced_nphotons, dtype=np.uint32)
# And finaly copy photons, if there are any
if reduced_nphotons > 0:
index_counter_gpu.fill(0)
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.copy_photons(np.int32(start_photon +
first_photon),
np.int32(photons_this_round),
np.uint32(target_flag),
index_counter_gpu,
self.pos,
self.dir,
self.wavelengths,
self.pol,
self.t,
self.flags,
self.last_hit_triangles,
self.weights,
self.evidx,
pos,
dir,
wavelengths,
pol,
t,
flags,
last_hit_triangles,
weights,
evidx,
block=(nthreads_per_block, 1, 1),
grid=(blocks, 1))
assert index_counter_gpu.get()[0] == reduced_nphotons
return GPUPhotonsSlice(pos, dir, pol, wavelengths, t,
last_hit_triangles, flags, weights, evidx)
def __del__(self):
del self.pos
del self.dir
del self.pol
del self.wavelengths
del self.t
del self.flags
del self.last_hit_triangles
# Free up GPU memory quickly if now available
gc.collect()
def __len__(self):
return self.pos.size
class GPUPhotonsHit(object):
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 marshall_photons_npl(self, npl):
pass
def marshall_photons(self, photons, ncopies):
"""
Assign the provided photons to the beginning (possibly
the entire array if ncopies is 1
"""
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)
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))
# Replicate the photons to the rest of the slots if needed
if ncopies > 1:
max_blocks = 1024
nthreads_per_block = 64
block = (nthreads_per_block, 1, 1)
for first_photon, photons_this_round, blocks in chunk_iterator(
nphotons, nthreads_per_block, max_blocks):
pass
grid = (blocks, 1)
args = (
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),
)
self.gpu_funcs.photon_duplicate(*args, block=block, grid=grid)
pass
pass
def get(self, npl=0, hit=0):
log.info("get npl:%d hit:%d " % (npl, hit))
pos = self.pos.get().view(np.float32).reshape((len(self.pos), 3))
dir = self.dir.get().view(np.float32).reshape((len(self.dir), 3))
pol = self.pol.get().view(np.float32).reshape((len(self.pol), 3))
wavelengths = self.wavelengths.get()
t = self.t.get()
last_hit_triangles = self.last_hit_triangles.get()
flags = self.flags.get()
weights = self.weights.get()
if npl:
nall = len(pos)
a = np.zeros((nall, 4, 4), dtype=np.float32)
a[:, 0, :3] = pos
a[:, 0, 3] = t
a[:, 1, :3] = dir
a[:, 1, 3] = wavelengths
a[:, 2, :3] = pol
a[:, 2, 3] = weights
assert len(last_hit_triangles) == len(flags)
pmtid = np.zeros(nall, dtype=np.int32)
# a kludge setting of pmtid into lht using the map argument of propagate_hit.cu
SURFACE_DETECT = 0x1 << 2
detected = np.where(flags & SURFACE_DETECT)
pmtid[detected] = last_hit_triangles[
detected] # sparsely populate, leaving zeros for undetected
a[:, 3, 0] = np.arange(nall,
dtype=np.int32).view(a.dtype) # photon_id
a[:, 3, 1] = 0 # used in comparison againt vbo prop
a[:, 3, 2] = flags.view(a.dtype) # history flags
a[:, 3, 3] = pmtid.view(a.dtype) # channel_id ie PmtId
if hit:
return a[pmtid > 0].view(NPY)
else:
return a.view(NPY)
pass
else: # the old way
return event.Photons(pos, dir, pol, wavelengths, t,
last_hit_triangles, flags, weights)
def iterate_copies(self):
'''Returns an iterator that yields GPUPhotonsSlice objects
corresponding to the event copies stored in ``self``.'''
for i in xrange(self.ncopies):
window = slice(self.true_nphotons * i,
self.true_nphotons * (i + 1))
yield GPUPhotonsSlice(
pos=self.pos[window],
dir=self.dir[window],
pol=self.pol[window],
wavelengths=self.wavelengths[window],
t=self.t[window],
last_hit_triangles=self.last_hit_triangles[window],
flags=self.flags[window],
weights=self.weights[window])
def upload_queues(self, nwork):
"""
# Order photons initially in the queue to put the clones next to each other
#. input_queue starts as [0,0,1,2,3,.....,nwork]
#. output_queue starts as [1,0,0,0,0,....]
"""
input_queue = np.empty(shape=nwork + 1, dtype=np.uint32)
input_queue[0] = 0
for copy in xrange(self.ncopies):
input_queue[1 + copy::self.ncopies] = np.arange(
self.true_nphotons,
dtype=np.uint32) + copy * self.true_nphotons
output_queue = np.zeros(shape=nwork + 1, dtype=np.uint32)
output_queue[0] = 1
self.input_queue_gpu = ga.to_gpu(input_queue)
self.output_queue_gpu = ga.to_gpu(output_queue)
def swap_queues(self):
"""
Swaps queues and returns photons remaining to propagate
#. output_queue[0] = 1 initially, this avoids enqueued photon_id
stomping on output_queue[0] as atomicAdd returns the non-incremented::
230 // Not done, put photon in output queue
231 if ((p.history & (NO_HIT | BULK_ABSORB | SURFACE_DETECT | SURFACE_ABSORB | NAN_ABORT)) == 0)
232 {
// pulling queue ticket
233 int out_idx = atomicAdd(output_queue, 1); // atomic add 1 to slot zero value, returns non-incremented original value
234 output_queue[out_idx] = photon_id;
235 }
#. At kernel tail non-completed photon threads enqueue their photon_id
into a slot in the output_queue. The slot to use is obtained
by atomic incrementing output_queue[0], ensuring orderly queue.
#. after kernel completes output_queue[0] contains the
number of photon_id enqued in output_queue[1:]
"""
temp = self.input_queue_gpu
self.input_queue_gpu = self.output_queue_gpu
self.output_queue_gpu = temp
self.output_queue_gpu[:1].set(np.ones(shape=1, dtype=np.uint32))
slot0minus1 = self.input_queue_gpu[:1].get(
)[0] - 1 # which was just now the output_queue before swap
log.debug("swap_queues slot0minus1 %s " % slot0minus1)
return slot0minus1
@profile_if_possible
def propagate_hit(self, gpu_geometry, rng_states, parameters):
"""Propagate photons on GPU to termination or max_steps, whichever
comes first.
May be called repeatedly without reloading photon information if
single-stepping through photon history.
..warning::
`rng_states` must have at least `nthreads_per_block`*`max_blocks`
number of curandStates.
got one abort::
In [1]: a = ph("hhMOCK")
In [9]: f = a[:,3,2].view(np.uint32)
In [12]: np.where( f & 1<<31 )
Out[12]: (array([279]),)
failed to just mock that one::
RANGE=279:280 MockNuWa MOCK
"""
nphotons = self.pos.size
nwork = nphotons
nthreads_per_block = parameters['threads_per_block']
max_blocks = parameters['max_blocks']
max_steps = parameters['max_steps']
use_weights = False
scatter_first = 0
self.upload_queues(nwork)
solid_id_map_gpu = gpu_geometry.solid_id_map
solid_id_to_channel_id_gpu = gpu_geometry.solid_id_to_channel_id_gpu
small_remainder = nthreads_per_block * 16 * 8
block = (nthreads_per_block, 1, 1)
results = {}
results['name'] = "propagate_hit"
results['nphotons'] = nphotons
results['nwork'] = nwork
results['nsmall'] = small_remainder
results['COLUMNS'] = "name:s,nphotons:i,nwork:i,nsmall:i"
step = 0
times = []
npass = 0
nabort = 0
while step < max_steps:
npass += 1
if nwork < small_remainder or use_weights:
nsteps = max_steps - step # Just finish the rest of the steps if the # of photons is low
log.debug(
"increase nsteps for stragglers: small_remainder %s nwork %s nsteps %s max_steps %s "
% (small_remainder, nwork, nsteps, max_steps))
else:
nsteps = 1
pass
log.info("nphotons %s nwork %s step %s max_steps %s nsteps %s " %
(nphotons, nwork, step, max_steps, nsteps))
abort = False
for first_photon, photons_this_round, blocks in chunk_iterator(
nwork, nthreads_per_block, max_blocks):
if abort:
nabort += 1
else:
grid = (blocks, 1)
args = (
np.int32(first_photon),
np.int32(photons_this_round),
self.input_queue_gpu[1:].gpudata,
self.output_queue_gpu.gpudata,
rng_states,
self.pos.gpudata,
self.dir.gpudata,
self.wavelengths.gpudata,
self.pol.gpudata,
self.t.gpudata,
self.flags.gpudata,
self.last_hit_triangles.gpudata,
self.weights.gpudata,
np.int32(nsteps),
np.int32(use_weights),
np.int32(scatter_first),
gpu_geometry.gpudata,
solid_id_map_gpu.gpudata,
solid_id_to_channel_id_gpu.gpudata,
)
log.info(
"propagate_hit_kernel.prepared_timed_call grid %s block %s first_photon %s photons_this_round %s "
% (repr(grid), repr(block), first_photon,
photons_this_round))
get_time = self.propagate_hit_kernel.prepared_timed_call(
grid, block, *args)
t = get_time()
times.append(t)
if t > self.max_time:
abort = True
log.warn(
"kernel launch time %s > max_time %s : ABORTING " %
(t, self.max_time))
pass
pass
pass
log.info("step %s propagate_hit_kernel times %s " %
(step, repr(times)))
pass
step += nsteps
scatter_first = 0 # Only allow non-zero in first pass
if step < max_steps:
nwork = self.swap_queues()
pass
pass
log.info("calling max ")
if ga.max(self.flags).get() & (1 << 31):
log.warn("ABORTED PHOTONS")
log.info("done calling max ")
cuda.Context.get_current().synchronize()
results['npass'] = npass
results['nabort'] = nabort
results['nlaunch'] = len(times)
results['tottime'] = sum(times)
results['maxtime'] = max(times)
results['mintime'] = min(times)
results[
'COLUMNS'] += ",npass:i,nabort:i,nlaunch:i,tottime:f,maxtime:f,mintime:f"
return results
@profile_if_possible
def select(self,
target_flag,
nthreads_per_block=64,
max_blocks=1024,
start_photon=None,
nphotons=None):
'''Return a new GPUPhoton object containing only photons that
have a particular bit set in their history word.'''
cuda.Context.get_current().synchronize()
index_counter_gpu = ga.zeros(shape=1, dtype=np.uint32)
cuda.Context.get_current().synchronize()
if start_photon is None:
start_photon = 0
if nphotons is None:
nphotons = self.pos.size - start_photon
# First count how much space we need
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.count_photons(np.int32(start_photon + first_photon),
np.int32(photons_this_round),
np.uint32(target_flag),
index_counter_gpu,
self.flags,
block=(nthreads_per_block, 1, 1),
grid=(blocks, 1))
cuda.Context.get_current().synchronize()
reduced_nphotons = int(index_counter_gpu.get()[0])
# Then allocate new storage space
pos = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
dir = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
pol = ga.empty(shape=reduced_nphotons, dtype=ga.vec.float3)
wavelengths = ga.empty(shape=reduced_nphotons, dtype=np.float32)
t = ga.empty(shape=reduced_nphotons, dtype=np.float32)
last_hit_triangles = ga.empty(shape=reduced_nphotons, dtype=np.int32)
flags = ga.empty(shape=reduced_nphotons, dtype=np.uint32)
weights = ga.empty(shape=reduced_nphotons, dtype=np.float32)
# And finaly copy photons, if there are any
if reduced_nphotons > 0:
index_counter_gpu.fill(0)
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.copy_photons(np.int32(start_photon +
first_photon),
np.int32(photons_this_round),
np.uint32(target_flag),
index_counter_gpu,
self.pos,
self.dir,
self.wavelengths,
self.pol,
self.t,
self.flags,
self.last_hit_triangles,
self.weights,
pos,
dir,
wavelengths,
pol,
t,
flags,
last_hit_triangles,
weights,
block=(nthreads_per_block, 1, 1),
grid=(blocks, 1))
assert index_counter_gpu.get()[0] == reduced_nphotons
return GPUPhotonsHitSlice(pos, dir, pol, wavelengths, t,
last_hit_triangles, flags, weights)
def __del__(self):
del self.pos
del self.dir
del self.pol
del self.wavelengths
del self.t
del self.flags
del self.last_hit_triangles
# Free up GPU memory quickly if now available
gc.collect()
def __len__(self):
return self.pos.size