def runTest(self):
nx, ny, nz, str_f, pt0, pt1, is_array = self.args
slice_xyz = common.slices_two_points(pt0, pt1)
# generate random source
if is_array:
shape = common.shape_two_points(pt0, pt1)
value = np.random.rand(*shape).astype(np.float32)
else:
value = np.random.ranf()
# instance
gpu_devices = common_gpu.gpu_device_list(print_info=False)
context = cl.Context(gpu_devices)
device = gpu_devices[0]
qtask = QueueTask()
fields = Fields(context, device, qtask, nx, ny, nz, '', 'single')
tfunc = lambda tstep: np.sin(0.03*tstep)
incident = IncidentDirect(fields, str_f, pt0, pt1, tfunc, value)
# host allocations
eh = np.zeros(fields.ns_pitch, dtype=fields.dtype)
# verify
eh[slice_xyz] = fields.dtype(value) * fields.dtype(tfunc(1))
fields.update_e()
fields.update_h()
fields.enqueue_barrier()
copy_eh_buf = fields.get_buf(str_f)
copy_eh = np.zeros_like(eh)
cl.enqueue_copy(fields.queue, copy_eh, copy_eh_buf)
original = eh[slice_xyz]
copy = copy_eh[slice_xyz]
norm = np.linalg.norm(original - copy)
self.assertEqual(norm, 0, '%s, %g' % (self.args, norm))
def runTest(self):
nx, ny, nz, str_f, pt0, pt1, is_array = self.args
slice_xyz = common.slices_two_points(pt0, pt1)
# generate random source
if is_array:
shape = common.shape_two_points(pt0, pt1)
value = np.random.rand(*shape).astype(np.float32)
else:
value = np.random.ranf()
# instance
gpu_devices = common_gpu.gpu_device_list(print_info=False)
context = cl.Context(gpu_devices)
device = gpu_devices[0]
qtask = QueueTask()
fields = Fields(context, device, qtask, nx, ny, nz, '', 'single')
tfunc = lambda tstep: np.sin(0.03 * tstep)
incident = IncidentDirect(fields, str_f, pt0, pt1, tfunc, value)
# host allocations
eh = np.zeros(fields.ns_pitch, dtype=fields.dtype)
# verify
eh[slice_xyz] = fields.dtype(value) * fields.dtype(tfunc(1))
fields.update_e()
fields.update_h()
fields.enqueue_barrier()
copy_eh_buf = fields.get_buf(str_f)
copy_eh = np.zeros_like(eh)
cl.enqueue_copy(fields.queue, copy_eh, copy_eh_buf)
original = eh[slice_xyz]
copy = copy_eh[slice_xyz]
norm = np.linalg.norm(original - copy)
self.assertEqual(norm, 0, '%s, %g' % (self.args, norm))
if tstep % 10 == 0 and is_plot:
getf.get_event().wait()
np.save('rank%d_%d' % (rank, tstep), getf.get_fields())
if is_master:
print('[%s] %d/%d (%d %%)\r' % (datetime.now() - t0, tstep, tmax, float(tstep)/tmax*100)),
sys.stdout.flush()
for i in range(size):
load_fail = True
while load_fail:
try:
arr[i*nx:(i+1)*nx,:] = np.load('rank%d_%d.npy' % (i, tstep))
load_fail = False
except:
sleep(0.1)
imag.set_array(arr.T)
#plt.savefig('./png/%.6d.png' % tstep)
plt.draw()
if is_plot:
if is_master: sleep(1)
else:
fields.enqueue_barrier()
if is_master:
dt = time() - t00
print dt
print('[%s] %d/%d (%d %%) %f Mpoint/s' % (datetime.now() - t0, tstep, tmax, float(tstep)/tmax*100, nx*ny*nz*tmax/dt/1e6) )
print(
'[%s] %d/%d (%d %%)\r' %
(datetime.now() - t0, tstep, tmax, float(tstep) / tmax * 100)),
sys.stdout.flush()
for i in range(size):
load_fail = True
while load_fail:
try:
arr[i * nx:(i + 1) * nx, :] = np.load('rank%d_%d.npy' %
(i, tstep))
load_fail = False
except:
sleep(0.1)
imag.set_array(arr.T)
#plt.savefig('./png/%.6d.png' % tstep)
plt.draw()
if is_plot:
if is_master: sleep(1)
else:
fields.enqueue_barrier()
if is_master:
dt = time() - t00
print dt
print('[%s] %d/%d (%d %%) %f Mpoint/s' %
(datetime.now() - t0, tstep, tmax, float(tstep) / tmax * 100,
nx * ny * nz * tmax / dt / 1e6))
