def propagate(gpu_detector,
number=10,
nphotons=500000,
nthreads_per_block=64,
max_blocks=1024):
"Returns the average number of photons propagated on the GPU per second."
rng_states = gpu.get_rng_states(nthreads_per_block * max_blocks)
run_times = []
for i in tools.progress(list(range(number))):
pos = np.zeros((nphotons, 3))
dir = sample.uniform_sphere(nphotons)
reorder = tools.argsort_direction(dir)
dir = dir[reorder]
pol = normalize(np.cross(sample.uniform_sphere(nphotons), dir))
wavelengths = np.random.uniform(400, 800, size=nphotons)
photons = event.Photons(pos, dir, pol, wavelengths)
gpu_photons = gpu.GPUPhotons(photons)
t0 = time.time()
gpu_photons.propagate(gpu_detector, rng_states, nthreads_per_block,
max_blocks)
cuda.Context.get_current().synchronize()
elapsed = time.time() - t0
if i > 0:
# first kernel call incurs some driver overhead
run_times.append(elapsed)
return nphotons / ufloat((np.mean(run_times), np.std(run_times)))
def intersect(gpu_geometry,
number=100,
nphotons=500000,
nthreads_per_block=64,
max_blocks=1024):
"Returns the average number of ray intersections per second."
distances_gpu = ga.empty(nphotons, dtype=np.float32)
module = gpu.get_cu_module('mesh.h', options=('--use_fast_math', ))
gpu_funcs = gpu.GPUFuncs(module)
run_times = []
for i in tools.progress(list(range(number))):
pos = ga.zeros(nphotons, dtype=ga.vec.float3)
dir = sample.uniform_sphere(nphotons)
reorder = tools.argsort_direction(dir)
dir = ga.to_gpu(gpu.to_float3(dir[reorder]))
t0 = time.time()
gpu_funcs.distance_to_mesh(np.int32(pos.size),
pos,
dir,
gpu_geometry.gpudata,
distances_gpu,
block=(nthreads_per_block, 1, 1),
grid=(pos.size // nthreads_per_block + 1,
1))
cuda.Context.get_current().synchronize()
elapsed = time.time() - t0
if i > 0:
# first kernel call incurs some driver overhead
run_times.append(elapsed)
return nphotons / ufloat((np.mean(run_times), np.std(run_times)))
def pdf(gpu_detector, npdfs=10, nevents=100, nreps=16, ndaq=1,
nthreads_per_block=64, max_blocks=1024):
"""
Returns the average number of 100 MeV events per second that can be
histogrammed per second.
Args:
- gpu_instance, chroma.gpu.GPU
The GPU instance passed to the GPUDetector constructor.
- npdfs, int
The number of pdf generations to average.
- nevents, int
The number of 100 MeV events to generate for each PDF.
- nreps, int
The number of times to propagate each event and add to PDF
- ndaq, int
The number of times to run the DAQ simulation on the propagated
event and add it to the PDF.
"""
rng_states = gpu.get_rng_states(nthreads_per_block*max_blocks)
gpu_daq = gpu.GPUDaq(gpu_detector)
gpu_pdf = gpu.GPUPDF()
gpu_pdf.setup_pdf(gpu_detector.nchannels, 100, (-0.5, 999.5), 10, (-0.5, 9.5))
run_times = []
for i in tools.progress(range(npdfs)):
t0 = time.time()
gpu_pdf.clear_pdf()
vertex_gen = generator.vertex.constant_particle_gun('e-', (0,0,0),
(1,0,0), 100)
vertex_iter = itertools.islice(vertex_gen, nevents)
for ev in g4generator.generate_events(vertex_iter):
gpu_photons = gpu.GPUPhotons(ev.photons_beg, ncopies=nreps)
gpu_photons.propagate(gpu_detector, rng_states,
nthreads_per_block, max_blocks)
for gpu_photon_slice in gpu_photons.iterate_copies():
for idaq in xrange(ndaq):
gpu_daq.begin_acquire()
gpu_daq.acquire(gpu_photon_slice, rng_states,
nthreads_per_block, max_blocks)
gpu_channels = gpu_daq.end_acquire()
gpu_pdf.add_hits_to_pdf(gpu_channels, nthreads_per_block)
hitcount, pdf = gpu_pdf.get_pdfs()
elapsed = time.time() - t0
if i > 0:
# first kernel call incurs some driver overhead
run_times.append(elapsed)
return nevents*nreps*ndaq/ufloat((np.mean(run_times),np.std(run_times)))
def load_photons(number=100, nphotons=500000):
"""Returns the average number of photons moved to the GPU device memory
per second."""
pos = np.zeros((nphotons, 3))
dir = sample.uniform_sphere(nphotons)
pol = normalize(np.cross(sample.uniform_sphere(nphotons), dir))
wavelengths = np.random.uniform(400, 800, size=nphotons)
photons = event.Photons(pos, dir, pol, wavelengths)
run_times = []
for i in tools.progress(list(range(number))):
t0 = time.time()
gpu_photons = gpu.GPUPhotons(photons)
cuda.Context.get_current().synchronize()
elapsed = time.time() - t0
if i > 0:
# first kernel call incurs some driver overhead
run_times.append(elapsed)
return nphotons / ufloat((np.mean(run_times), np.std(run_times)))
def pdf_eval(gpu_detector,
npdfs=10,
nevents=25,
nreps=16,
ndaq=128,
nthreads_per_block=64,
max_blocks=1024):
"""
Returns the average number of 100 MeV events that can be
histogrammed per second.
Args:
- gpu_instance, chroma.gpu.GPU
The GPU instance passed to the GPUDetector constructor.
- npdfs, int
The number of pdf generations to average.
- nevents, int
The number of 100 MeV events to generate for each PDF.
- nreps, int
The number of times to propagate each event and add to PDF
- ndaq, int
The number of times to run the DAQ simulation on the propagated
event and add it to the PDF.
"""
rng_states = gpu.get_rng_states(nthreads_per_block * max_blocks)
# Make data event
data_ev = next(
g4generator.generate_events(
itertools.islice(
generator.vertex.constant_particle_gun('e-', (0, 0, 0),
(1, 0, 0), 100), 1)))
gpu_photons = gpu.GPUPhotons(data_ev.photons_beg)
gpu_photons.propagate(gpu_detector, rng_states, nthreads_per_block,
max_blocks)
gpu_daq = gpu.GPUDaq(gpu_detector)
gpu_daq.begin_acquire()
gpu_daq.acquire(gpu_photons, rng_states, nthreads_per_block,
max_blocks).get()
data_ev_channels = gpu_daq.end_acquire()
# Setup PDF evaluation
gpu_daq = gpu.GPUDaq(gpu_detector, ndaq=ndaq)
gpu_pdf = gpu.GPUPDF()
gpu_pdf.setup_pdf_eval(data_ev_channels.hit,
data_ev_channels.t,
data_ev_channels.q,
0.05, (-0.5, 999.5),
1.0, (-0.5, 20),
min_bin_content=20,
time_only=True)
run_times = []
for i in tools.progress(list(range(npdfs))):
t0 = time.time()
gpu_pdf.clear_pdf_eval()
vertex_gen = generator.vertex.constant_particle_gun(
'e-', (0, 0, 0), (1, 0, 0), 100)
vertex_iter = itertools.islice(vertex_gen, nevents)
for ev in g4generator.generate_events(vertex_iter):
gpu_photons = gpu.GPUPhotons(ev.photons_beg, ncopies=nreps)
gpu_photons.propagate(gpu_detector, rng_states, nthreads_per_block,
max_blocks)
for gpu_photon_slice in gpu_photons.iterate_copies():
gpu_daq.begin_acquire()
gpu_photon_slice = gpu_photon_slice.select(
event.SURFACE_DETECT)
gpu_daq.acquire(gpu_photon_slice, rng_states,
nthreads_per_block, max_blocks)
gpu_channels = gpu_daq.end_acquire()
gpu_pdf.accumulate_pdf_eval(gpu_channels, nthreads_per_block)
cuda.Context.get_current().synchronize()
elapsed = time.time() - t0
if i > 0:
# first kernel call incurs some driver overhead
run_times.append(elapsed)
return nevents * nreps * ndaq / ufloat(
(np.mean(run_times), np.std(run_times)))
