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import numpy as np
from copy import copy
import time
import pycuda.driver as cuda
from pycuda.characterize import sizeof
from pycuda.compiler import SourceModule
from pycuda import gpuarray
import threading
import Queue
import src
class GPUThread(threading.Thread):
def __init__(self, device_id, geometry, jobs, output, nblocks=64, max_nthreads=100000):
threading.Thread.__init__(self)
self.device_id = device_id
self.geometry = copy(geometry)
self.jobs = jobs
self.output = output
self.nblocks = nblocks
self.max_nthreads = max_nthreads
self._stop = threading.Event()
def stop(self):
self._stop.set()
def stopped(self):
return self._stop.is_set()
def run(self):
device = cuda.Device(self.device_id)
context = device.make_context()
module = SourceModule(src.kernel, options=['-I' + src.dir], no_extern_c=True, cache_dir=False)
propagate = module.get_function('propagate')
fill_uniform = module.get_function('fill_uniform')
fill_uniform_sphere = module.get_function('fill_uniform_sphere')
init_rng = module.get_function('init_rng')
rng_states_gpu = cuda.mem_alloc(self.max_nthreads*sizeof('curandStateXORWOW', '#include <curand_kernel.h>'))
init_rng(np.int32(self.max_nthreads), rng_states_gpu, np.int32(self.device_id), np.int32(0), block=(self.nblocks,1,1), grid=(self.max_nthreads//self.nblocks+1,1))
self.geometry.load(module)
daq_module = SourceModule(src.daq, options=['-I' + src.dir], no_extern_c=True, cache_dir=False)
reset_earliest_time_int = daq_module.get_function('reset_earliest_time_int')
run_daq = daq_module.get_function('run_daq')
convert_sortable_int_to_float = daq_module.get_function('convert_sortable_int_to_float')
earliest_time_gpu = gpuarray.GPUArray(shape=(max(self.geometry.pmtids)+1,), dtype=np.float32)
earliest_time_int_gpu = gpuarray.GPUArray(shape=earliest_time_gpu.shape, dtype=np.uint32)
solid_map_gpu = gpuarray.to_gpu(self.geometry.solid_id.astype(np.int32))
while not self.stopped():
try:
position, nphotons = self.jobs.get(block=False, timeout=0.1)
except Queue.Empty:
continue
t0 = time.time()
gpu_kwargs = {'block': (self.nblocks,1,1), 'grid': (nphotons//self.nblocks+1,1)}
positions_gpu = gpuarray.empty(nphotons, dtype=gpuarray.vec.float3)
positions_gpu.fill(position)
directions_gpu = gpuarray.empty(nphotons, dtype=gpuarray.vec.float3)
fill_uniform_sphere(np.int32(nphotons), rng_states_gpu, directions_gpu, **gpu_kwargs)
wavelengths_gpu = gpuarray.empty(nphotons, dtype=np.float32)
fill_uniform(np.int32(nphotons), rng_states_gpu, wavelengths_gpu, np.float32(200), np.float32(800), **gpu_kwargs)
polarizations_gpu = gpuarray.empty(nphotons, dtype=gpuarray.vec.float3)
fill_uniform_sphere(np.int32(nphotons), rng_states_gpu, polarizations_gpu, **gpu_kwargs)
times_gpu = gpuarray.zeros(nphotons, dtype=np.float32)
histories_gpu = gpuarray.zeros(nphotons, dtype=np.uint32)
last_hit_triangles_gpu = gpuarray.empty(nphotons, dtype=np.int32)
last_hit_triangles_gpu.fill(-1)
max_steps = 10
propagate(np.int32(nphotons), rng_states_gpu, positions_gpu, directions_gpu, wavelengths_gpu, polarizations_gpu, times_gpu, histories_gpu, last_hit_triangles_gpu, np.int32(max_steps), **gpu_kwargs)
reset_earliest_time_int(np.float32(1e9), np.int32(len(earliest_time_int_gpu)), earliest_time_int_gpu, block=(self.nblocks,1,1), grid=(len(earliest_time_int_gpu)//self.nblocks+1,1))
run_daq(rng_states_gpu, np.uint32(0x1 << 2), np.float32(1.2e-9), np.int32(nphotons), times_gpu, histories_gpu, last_hit_triangles_gpu, solid_map_gpu, np.int32(len(earliest_time_int_gpu)), earliest_time_int_gpu, block=(self.nblocks,1,1), grid=(nphotons//self.nblocks+1,1))
convert_sortable_int_to_float(np.int32(len(earliest_time_int_gpu)), earliest_time_int_gpu, earliest_time_gpu, block=(self.nblocks,1,1), grid=(len(earliest_time_int_gpu)//self.nblocks+1,1))
cuda.Context.synchronize()
elapsed = time.time() - t0
#print 'device %i; elapsed %f sec' % (self.device_id, elapsed)
self.output.put(earliest_time_gpu.get())
self.jobs.task_done()
context.pop()
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