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
fields = Fields(0, 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()
copy_eh_buf = fields.get_buf(str_f)
copy_eh = np.zeros_like(eh)
cuda.memcpy_dtoh(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))
fields.context_pop()
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))
#exch = node.ExchangeMpiBufferBlock(fields, direction)
#exch = node.ExchangeMpiBufferBlockSplit(fields, direction)
exch = node.ExchangeMpiBufferNonBlockSplitEnqueue(fields, direction, tmax)
if '+' in direction: cpu.Core(exch.cpuf_p)
if '-' in direction: cpu.Core(exch.cpuf_m)
is_master = True if rank == 0 else False
if is_plot:
Pbc(fields, 'yz')
getf = GetFields(fields, 'ez', (0, 0, 0.5), (-1, -1, 0.5))
tfunc = lambda tstep: 40 * np.sin(0.05 * tstep)
if rank < size - 1:
IncidentDirect(fields, 'ez', (220, 0.5, 0), (220, 0.5, -1), tfunc)
#if rank > 0:
# IncidentDirect(fields, 'ez', (20, 0.5, 0), (20, 0.5, -1), tfunc)
if is_master:
# plot
import matplotlib.pyplot as plt
plt.ion()
fig = plt.figure(figsize=(12, 8))
arr = np.zeros((size * nx, ny), fields.dtype)
for i in range(1, size):
plt.plot((i * nx, i * nx), (0, ny), color='k', linewidth=1.2)
imag = plt.imshow(arr.T,
interpolation='nearest',
origin='lower',
vmin=-0.7,
import matplotlib as mpl
mpl.rc('image', interpolation='nearest', origin='lower')
#plt.ion()
fig = plt.figure(figsize=(14, 8))
# gpu device
gpu_devices = common_gpu.gpu_device_list(print_info=False)
context = cl.Context(gpu_devices)
device = gpu_devices[0]
# z-axis
nx, ny, nz = 180, 160, 2
fields = Fields(context, device, nx, ny, nz)
Pbc(fields, 'xyz')
Core(fields)
IncidentDirect(fields, 'ey', (20, 0, 0), (20, ny - 1, nz - 1), tfunc)
IncidentDirect(fields, 'ex', (0, 20, 0), (nx - 1, 20, nz - 1), tfunc)
for tstep in xrange(1, tmax + 1):
fields.update_e()
fields.update_h()
ax1 = fig.add_subplot(2, 3, 1)
getf = GetFields(fields, 'ey', (0, 0, nz / 2), (nx - 1, ny - 1, nz / 2))
getf.get_event().wait()
ax1.imshow(getf.get_fields().T, vmin=-1.1, vmax=1.1)
ax1.set_title('%s, ey[20,:,:]' % repr(fields.ns))
ax1.set_xlabel('x')
ax1.set_ylabel('y')
ax2 = fig.add_subplot(2, 3, 4)
ax1 = fig.add_subplot(1, 3, 1)
ax2 = fig.add_subplot(1, 3, 2)
ax3 = fig.add_subplot(1, 3, 3)
# gpu device
gpu_devices = common_gpu.gpu_device_list(print_info=False)
context = cl.Context(gpu_devices)
device = gpu_devices[0]
# xy-plane
nx, ny, nz = 180, 160, 2
fields = Fields(context, device, nx, ny, nz)
Pbc(fields, 'z')
Pml(fields, ('+-', '+-', ''), npml)
Core(fields)
IncidentDirect(fields, 'ez', (0.4, 0.3, 0), (0.4, 0.3, -1), tfunc)
getf = GetFields(fields, 'ez', (0, 0, 0.5), (-1, -1, 0.5))
for tstep in xrange(1, tmax+1):
fields.update_e()
fields.update_h()
getf.get_event().wait()
ax1.imshow(getf.get_fields().T, vmin=-1.1, vmax=1.1)
ax1.set_title('xy-plane')
ax1.set_xlabel('x')
ax1.set_ylabel('y')
# yz-plane
nx, ny, nz = 2, 180, 160
fields = Fields(context, device, nx, ny, nz)
from kemp.fdtd3d.gpu import Fields, Core, Pbc, IncidentDirect, GetFields nx, ny, nz = 160, 140, 32 tmax, tgap = 150, 10 # instances gpu_devices = common_gpu.gpu_device_list(print_info=False) context = cl.Context(gpu_devices) device = gpu_devices[0] fields = Fields(context, device, nx, ny, nz) Core(fields) Pbc(fields, 'xyz') tfunc = lambda tstep: np.sin(0.05 * tstep) IncidentDirect(fields, 'ez', (20, 0, 0), (20, ny-1, nz-1), tfunc) #IncidentDirect(fields, 'ez', (0, 20, 0), (nx-1, 20, nz-1), tfunc) getf = GetFields(fields, 'ez', (0, 0, nz/2), (nx-1, ny-1, nz/2)) print fields.instance_list # plot import matplotlib.pyplot as plt plt.ion() fig = plt.figure(figsize=(12,8)) imag = plt.imshow(np.zeros((nx, ny), fields.dtype).T, interpolation='nearest', origin='lower', vmin=-1.1, vmax=1.1) plt.colorbar() # main loop from datetime import datetime t0 = datetime.now()
import sys, os
sys.path.append(os.path.expanduser('~'))
from kemp.fdtd3d.util import common_gpu
from kemp.fdtd3d.gpu import Fields, Core, Pbc, IncidentDirect, GetFields
nx, ny, nz = 160, 140, 32
tmax, tgap = 150, 10
# instances
gpu_devices = common_gpu.gpu_device_list(print_info=False)
context = cl.Context(gpu_devices)
device = gpu_devices[0]
fields = Fields(context, device, nx, ny, nz)
Core(fields)
Pbc(fields, 'xyz')
'''
tfunc = lambda tstep: np.sin(0.05 * tstep)
IncidentDirect(fields, 'ez', (20, 0, 0), (20, ny-1, nz-1), tfunc)
#IncidentDirect(fields, 'ez', (0, 20, 0), (nx-1, 20, nz-1), tfunc)
getf = GetFields(fields, 'ez', (0, 0, nz/2), (nx-1, ny-1, nz/2))
print fields.instance_list
# plot
import matplotlib.pyplot as plt
plt.ion()
fig = plt.figure(figsize=(12,8))
imag = plt.imshow(np.zeros((nx, ny), fields.dtype).T, interpolation='nearest', origin='lower', vmin=-1.1, vmax=1.1)
plt.colorbar()
# main loop
nx, ny, nz = 2, 250, 300
tmax, tgap = 1000, 10
npml = 10
# instances
gpu_devices = common_gpu.gpu_device_list(print_info=False)
context = cl.Context(gpu_devices)
device = gpu_devices[0]
fields = Fields(context, device, nx, ny, nz)
Pbc(fields, 'x')
Pml(fields, ('', '+-', '+-'), npml)
Core(fields)
tfunc = lambda tstep: 50 * np.sin(0.05 * tstep)
IncidentDirect(fields, 'ex', (0, 0.4, 0.3), (-1, 0.4, 0.3), tfunc)
getf = GetFields(fields, 'ex', (0.5, 0, 0), (0.5, -1, -1))
print fields.instance_list
# plot
import matplotlib.pyplot as plt
plt.ion()
fig = plt.figure(figsize=(12, 8))
imag = plt.imshow(np.zeros((ny, nz), fields.dtype).T,
interpolation='nearest',
origin='lower',
vmin=-1.1,
vmax=1.1)
plt.xlabel('y')
plt.ylabel('z')
from kemp.fdtd3d.util import common_gpu
from kemp.fdtd3d.gpu import Fields, Core, Pbc, IncidentDirect, GetFields
nx, ny, nz = 2, 160, 140
tmax, tgap = 150, 10
# instances
gpu_devices = common_gpu.gpu_device_list(print_info=False)
context = cl.Context(gpu_devices)
device = gpu_devices[0]
fields = Fields(context, device, nx, ny, nz)
Pbc(fields, 'xyz')
Core(fields)
tfunc = lambda tstep: np.sin(0.05 * tstep)
IncidentDirect(fields, 'ex', (0, 20, 0), (-1, 20, -1), tfunc)
#IncidentDirect(fields, 'ex', (0, 0, 20), (-1, -1, 20), tfunc)
getf = GetFields(fields, 'ex', (0.5, 0, 0), (0.5, -1, -1))
print fields.instance_list
# plot
import matplotlib.pyplot as plt
plt.ion()
fig = plt.figure(figsize=(12, 8))
imag = plt.imshow(np.zeros((ny, nz), fields.dtype).T,
interpolation='nearest',
origin='lower',
vmin=-1.1,
vmax=1.1)
plt.xlabel('y')