def __init__(s, nxs, ny, nz, target_device='all', print_device_info=True):
s.gpu_devices = utils.get_gpu_devices()
if print_device_info:
utils.print_gpu_info(s.gpu_devices)
utils.print_cpu_info()
ngpu_dev = len(s.gpu_devices)
s.context, s.queues = utils.create_context_queues(s.gpu_devices)
s.ngpu = len(s.gpu_devices)
s.Ls = 256
if target_device == int:
s.Gs = utils.get_optimal_global_work_size(
s.gpu_devices[target_device])
else:
s.Gs = utils.get_optimal_global_work_size(s.gpu_devices[0])
if type(nxs) == list:
if len(nxs) == s.ngpu:
s.nxs = nxs
s.nx_gpu = np.array(nxs).sum()
else:
print(
'Error: len(nxs) %d is not matched with the number of target devices %d.'
% (len(nxs), s.ngpu))
sys.exit()
elif type(nxs) == int:
if nxs % s.ngpu == 0:
s.nxs = [nxs / s.ngpu for i in xrange(s.ngpu)]
s.nx_gpu = nxs
else:
print(
'Error: nxs %d is not multiple of the number of target devices %d.'
% (nxs, s.ngpu))
sys.exit()
else:
print('Error: nxs type %s is invalid.' % type(nxs))
sys.exit()
s.ny, s.nz = ny, nz
s.check_grid_size()
s.allocations()
s.get_program(print_source=False)
def __init__(s, nxs, ny, nz, target_device='all', print_device_info=True):
s.gpu_devices = utils.get_gpu_devices()
if print_device_info:
utils.print_gpu_info(s.gpu_devices)
utils.print_cpu_info()
ngpu_dev = len(s.gpu_devices)
s.context, s.queues = utils.create_context_queues(s.gpu_devices)
s.ngpu = len(s.gpu_devices)
s.Ls = 256
if target_device == int:
s.Gs = utils.get_optimal_global_work_size(s.gpu_devices[target_device])
else:
s.Gs = utils.get_optimal_global_work_size(s.gpu_devices[0])
if type(nxs) == list:
if len(nxs) == s.ngpu:
s.nxs = nxs
s.nx_gpu = np.array(nxs).sum()
else:
print('Error: len(nxs) %d is not matched with the number of target devices %d.' %(len(nxs), s.ngpu))
sys.exit()
elif type(nxs) == int:
if nxs % s.ngpu == 0:
s.nxs = [nxs/s.ngpu for i in xrange(s.ngpu)]
s.nx_gpu = nxs
else:
print('Error: nxs %d is not multiple of the number of target devices %d.' %(nxs, s.ngpu))
sys.exit()
else:
print('Error: nxs type %s is invalid.' %type(nxs))
sys.exit()
s.ny, s.nz = ny, nz
s.check_grid_size()
s.allocations()
s.get_program(print_source=False)
def __init__(s, nxs, ny, nz, target_device='all', print_verbose=True):
s.print_verbose = print_verbose
s.gpu_devices = utils.get_gpu_devices(s.print_verbose)
if s.print_verbose:
utils.print_gpu_info(s.gpu_devices)
utils.print_cpu_info()
ngpu_dev = len(s.gpu_devices)
s.lsize = 256
s.gsizes = []
s.nnx = 1
s.ngpu = ngpu_dev
s.context, s.queues = utils.create_context_queues(s.gpu_devices)
td = target_device
if ngpu_dev > 0:
for device in s.gpu_devices:
s.gsizes.append( utils.get_optimal_global_work_size(device) )
if td == 'cpu':
s.ngpu = 0
target_str = 'CPU'
elif td in ['gpu%d' % i for i in range(ngpu_dev)]:
s.ngpu = 1
gpu_num = int(td.strip('gpu'))
s.gsizes = [ s.gsizes[gpu_num] ]
s.gpu_devices = [ s.gpu_devices[gpu_num] ]
s.context, s.queues = utils.create_context_queues(s.gpu_devices)
target_str = 'Single GPU #%d' % gpu_num
elif td in ['gpu']:
s.nnx = ngpu_dev
target_str = '%d GPUs' % s.ngpu
elif td in ['all']:
s.nnx = ngpu_dev + 1
target_str = 'CPU + %d GPUs' % s.ngpu
else:
print('Error: Invalid target_device option.')
print(' Possible options: %s' %(['all', 'cpu', 'gpu'] + ['gpu%d' % i for i in range(ngpu_dev)]))
sys.exit()
else:
if td in ['all', 'cpu']:
s.nnx = 1
s.ngpu = 0
target_str = 'CPU'
else:
print('Error: Invalid target_device option.')
print(' There are no GPU devices.')
print(' Possible options: %s' %(['all', 'cpu']))
sys.exit()
if type(nxs) == list:
if len(nxs) == s.nnx:
s.nxs = nxs
s.nx_total = np.array(nxs).sum()
else:
print('Error: len(nxs) %d is not matched with the number of target devices %d.' %(len(nxs), s.nnx))
sys.exit()
elif type(nxs) == int:
s.nx_total = nxs
if s.nnx == 1:
s.nxs = [nxs]
else:
#s.nxs = utils.get_optimal_nxs()
s.nxs = [nxs/s.ngpu for i in xrange(s.ngpu)]
else:
print('Error: nxs type %s is invalid.' % type(nxs))
print(' Possible types: %s' %(['list', 'int']))
sys.exit()
if s.print_verbose:
print('Target Device : %s' % target_str)
print('s.nnx = %d' % s.nnx)
print('s.ngpu = %d' % s.ngpu)
print('s.nxs = %s' % s.nxs)
print('')
s.ny, s.nz = ny, nz
s.check_grid_size()
s.allocations()
s.get_program(print_ksource=False)
s.prepare_updates()
if __name__ == '__main__':
#nx, ny, nz = 240, 256, 256 # 540 MB
nx, ny, nz = 512, 480, 480 # 3.96 GB
#nx, ny, nz = 480, 480, 480 # 3.71 GB
tmax, tgap = 200, 10
gpu_id = 0
import utils
gpu_devices = utils.get_gpu_devices()
utils.print_gpu_info(gpu_devices)
context, queues = utils.create_context_queues(gpu_devices)
queue = queues[gpu_id]
gs = utils.get_optimal_global_work_size(gpu_devices[gpu_id])
emf = EMField3dGpu(context, queue, nx, ny, nz)
fdtd = Fdtd3dSingleGpu(emf, gs)
src = Fdtd3dSrcGpu(emf, 'ez', gs)
# Plot
import matplotlib.pyplot as plt
plt.ion()
f = np.ones((nx, ny, nz), 'f')
imsh = plt.imshow(f[:, :, nz / 2].T,
cmap=plt.cm.hot,
origin='lower',
vmin=0,
vmax=0.005)
plt.colorbar()
if __name__ == '__main__': #nx, ny, nz = 240, 256, 256 # 540 MB nx, ny, nz = 512, 480, 480 # 3.96 GB #nx, ny, nz = 480, 480, 480 # 3.71 GB tmax, tgap = 200, 10 gpu_id = 0 import utils gpu_devices = utils.get_gpu_devices() utils.print_gpu_info(gpu_devices) context, queues = utils.create_context_queues(gpu_devices) queue = queues[gpu_id] gs = utils.get_optimal_global_work_size( gpu_devices[gpu_id] ) emf = EMField3dGpu(context, queue, nx, ny, nz) fdtd = Fdtd3dSingleGpu(emf, gs) src = Fdtd3dSrcGpu(emf, 'ez', gs) # Plot import matplotlib.pyplot as plt plt.ion() f = np.ones((nx, ny, nz), 'f') imsh = plt.imshow(f[:,:,nz/2].T, cmap=plt.cm.hot, origin='lower', vmin=0, vmax=0.005) plt.colorbar() # Main loop
def __init__(s, nxs, ny, nz, target_device='all', print_verbose=True):
s.print_verbose = print_verbose
s.gpu_devices = utils.get_gpu_devices(s.print_verbose)
if s.print_verbose:
utils.print_gpu_info(s.gpu_devices)
utils.print_cpu_info()
ngpu_dev = len(s.gpu_devices)
s.lsize = 256
s.gsizes = []
s.nnx = 1
s.ngpu = ngpu_dev
s.context, s.queues = utils.create_context_queues(s.gpu_devices)
td = target_device
if ngpu_dev > 0:
for device in s.gpu_devices:
s.gsizes.append(utils.get_optimal_global_work_size(device))
if td == 'cpu':
s.ngpu = 0
target_str = 'CPU'
elif td in ['gpu%d' % i for i in range(ngpu_dev)]:
s.ngpu = 1
gpu_num = int(td.strip('gpu'))
s.gsizes = [s.gsizes[gpu_num]]
s.gpu_devices = [s.gpu_devices[gpu_num]]
s.context, s.queues = utils.create_context_queues(
s.gpu_devices)
target_str = 'Single GPU #%d' % gpu_num
elif td in ['gpu']:
s.nnx = ngpu_dev
target_str = '%d GPUs' % s.ngpu
elif td in ['all']:
s.nnx = ngpu_dev + 1
target_str = 'CPU + %d GPUs' % s.ngpu
else:
print('Error: Invalid target_device option.')
print(' Possible options: %s' %
(['all', 'cpu', 'gpu'] +
['gpu%d' % i for i in range(ngpu_dev)]))
sys.exit()
else:
if td in ['all', 'cpu']:
s.nnx = 1
s.ngpu = 0
target_str = 'CPU'
else:
print('Error: Invalid target_device option.')
print(' There are no GPU devices.')
print(' Possible options: %s' % (['all', 'cpu']))
sys.exit()
if type(nxs) == list:
if len(nxs) == s.nnx:
s.nxs = nxs
s.nx_total = np.array(nxs).sum()
else:
print(
'Error: len(nxs) %d is not matched with the number of target devices %d.'
% (len(nxs), s.nnx))
sys.exit()
elif type(nxs) == int:
s.nx_total = nxs
if s.nnx == 1:
s.nxs = [nxs]
else:
#s.nxs = utils.get_optimal_nxs()
s.nxs = [nxs / s.ngpu for i in xrange(s.ngpu)]
else:
print('Error: nxs type %s is invalid.' % type(nxs))
print(' Possible types: %s' % (['list', 'int']))
sys.exit()
if s.print_verbose:
print('Target Device : %s' % target_str)
print('s.nnx = %d' % s.nnx)
print('s.ngpu = %d' % s.ngpu)
print('s.nxs = %s' % s.nxs)
print('')
s.ny, s.nz = ny, nz
s.check_grid_size()
s.allocations()
s.get_program(print_ksource=False)
s.prepare_updates()
