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#!/usr/bin/env python
import numpy as np
from pycuda import gpuarray as ga
import time
from uncertainties import ufloat
import sys
from chroma.gpu import GPU, to_float3
from chroma.camera import get_rays
from chroma.event import Photons
from chroma.sample import uniform_sphere
from chroma.generator.photon import G4ParallelGenerator
from chroma.optics import water_wcsim
from chroma.generator.vertex import constant_particle_gun
from chroma.tools import profile_if_possible
# Generator processes need to fork BEFORE the GPU context is setup
g4generator = G4ParallelGenerator(4, water_wcsim)
def progress(seq):
"Print progress while iterating over `seq`."
n = len(seq)
print '[' + ' '*21 + ']\r[',
sys.stdout.flush()
for i, item in enumerate(seq):
if i % (n//10) == 0:
print '.',
sys.stdout.flush()
yield item
print ']'
sys.stdout.flush()
def ray_trace(gpu, number=1000):
"""
Return the number of mean and standard deviation of the number of ray
intersections per second as a ufloat for the geometry loaded onto `gpu`.
.. note::
The rays are thrown from a camera sitting *outside* of the geometry.
Args:
- gpu, chroma.gpu.GPU
The GPU object with a geometry already loaded.
- number, int
The number of kernel calls to average.
"""
lb, ub = gpu.geometry.mesh.get_bounds()
scale = np.linalg.norm(ub-lb)
point = [0,scale,(lb[2]+ub[2])/2]
size = (800,600)
width, height = size
origins, directions = get_rays(point, size, 0.035, focal_length=0.018)
origins_gpu = ga.to_gpu(to_float3(origins))
directions_gpu = ga.to_gpu(to_float3(directions))
pixels_gpu = ga.zeros(width*height, dtype=np.int32)
run_times = []
for i in progress(range(number)):
t0 = time.time()
gpu.kernels.ray_trace(np.int32(pixels_gpu.size), origins_gpu, directions_gpu, pixels_gpu, block=(gpu.nthreads_per_block,1,1), grid=(pixels_gpu.size//gpu.nthreads_per_block+1,1))
gpu.context.synchronize()
elapsed = time.time() - t0
if i > 0:
# first kernel call incurs some driver overhead
run_times.append(elapsed)
return pixels_gpu.size/ufloat((np.mean(run_times),np.std(run_times)))
def load_photons(gpu, number=10, nphotons=500000):
"""
Return the mean and standard deviation of the number of photons loaded
onto `gpu` per second.
Args:
- gpu, chroma.gpu.GPU
The GPU object with a geometry already loaded.
- number, int
The number of loads to average
- nphotons, int
The number of photons to load per trial
"""
gpu.setup_propagate()
photons = Photons(np.zeros((nphotons,3)), uniform_sphere(nphotons), np.random.uniform(400,800,size=nphotons))
run_times = []
for i in progress(range(number)):
t0 = time.time()
gpu.load_photons(photons)
gpu.context.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 propagate(gpu, number=10, nphotons=500000):
"""
Return the mean and standard deviation of the number of photons propagated
per second as a ufloat for the geometry loaded onto `gpu`.
Args:
- gpu, chroma.gpu.GPU
The GPU object with a geometry already loaded.
- number, int
The number of kernel calls to average.
- nphotons, int
The number of photons to propagate per kernel call.
"""
gpu.setup_propagate()
run_times = []
for i in progress(range(number)):
photons = Photons(np.zeros((nphotons,3)), uniform_sphere(nphotons), np.random.uniform(400,800,size=nphotons))
gpu.load_photons(photons)
t0 = time.time()
gpu.propagate()
gpu.context.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)))
@profile_if_possible
def pdf(gpu, max_pmt_id, npdfs=10, nevents=100, nreps=1):
"""
Return the mean and standard deviation of the number of 100 MeV
events per second that can be histogrammed a ufloat for the
geometry loaded onto `gpu`.
Args:
- gpu, chroma.gpu.GPU
The GPU object with a geometry already loaded.
- max_pmt_id, int
The channel number of the highest PMT
- 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
"""
gpu.setup_propagate()
run_times = []
gpu.setup_propagate()
gpu.setup_daq(max_pmt_id)
gpu.setup_pdf(max_pmt_id, 100, (-0.5, 999.5), 10, (-0.5, 9.5))
vertex_gen = constant_particle_gun('e-', (0,0,0), (1,0,0), 100)
for i in progress(range(npdfs)):
t0 = time.time()
gpu.clear_pdf()
for ev in g4generator.generate_events(nevents, vertex_gen):
for j in xrange(nreps):
gpu.load_photons(ev.photon_start)
gpu.propagate()
gpu.run_daq()
gpu.add_hits_to_pdf()
hitcount, pdf = gpu.get_pdfs()
elapsed = time.time() - t0
if i > 0:
# first kernel call incurs some driver overhead
run_times.append(elapsed)
return nevents*nreps/ufloat((np.mean(run_times),np.std(run_times)))
if __name__ == '__main__':
from chroma.detectors import build_lbne_200kton, build_minilbne
lbne = build_lbne_200kton()
lbne.build(bits=11)
gpu = GPU()
gpu.load_geometry(lbne, print_usage=False)
#print '%s track steps/s' % ray_trace(gpu)
#print '%s loaded photons/s' % load_photons(gpu)
#print '%s propagated photons/s' % propagate(gpu)
print '%s histogrammed 100 MeV events/s' % pdf(gpu, max(lbne.pmtids))
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