def __call__(self, input_ary, output_ary=None, allocator=None,
stream=None):
allocator = allocator or input_ary.allocator
if output_ary is None:
output_ary = input_ary
if isinstance(output_ary, (str, unicode)) and output_ary == "new":
output_ary = cl_array.empty_like(input_ary, allocator=allocator)
if input_ary.shape != output_ary.shape:
raise ValueError("input and output must have the same shape")
if not input_ary.flags.forc:
raise RuntimeError("ScanKernel cannot "
"deal with non-contiguous arrays")
n, = input_ary.shape
if not n:
return output_ary
unit_size = self.scan_wg_size * self.scan_wg_seq_batches
dev = driver.Context.get_device()
max_groups = 3*dev.get_attribute(
driver.device_attribute.MULTIPROCESSOR_COUNT)
from pytools import uniform_interval_splitting
interval_size, num_groups = uniform_interval_splitting(
n, unit_size, max_groups);
block_results = allocator(self.dtype.itemsize*num_groups)
dummy_results = allocator(self.dtype.itemsize)
# first level scan of interval (one interval per block)
self.scan_intervals_knl.prepared_async_call(
(num_groups, 1), (self.scan_wg_size, 1, 1), stream,
input_ary.gpudata,
n, interval_size,
output_ary.gpudata,
block_results)
# second level inclusive scan of per-block results
self.scan_intervals_knl.prepared_async_call(
(1,1), (self.scan_wg_size, 1, 1), stream,
block_results,
num_groups, interval_size,
block_results,
dummy_results)
# update intervals with result of second level scan
self.final_update_knl.prepared_async_call(
(num_groups, 1,), (self.update_wg_size, 1, 1), stream,
output_ary.gpudata,
n, interval_size,
block_results)
return output_ary
def __call__(self,
input_ary,
output_ary=None,
allocator=None,
stream=None):
allocator = allocator or input_ary.allocator
if output_ary is None:
output_ary = input_ary
if isinstance(output_ary, (str, unicode)) and output_ary == "new":
output_ary = cl_array.empty_like(input_ary,
allocator=allocator)
if input_ary.shape != output_ary.shape:
raise ValueError("input and output must have the same shape")
if not input_ary.flags.forc:
raise RuntimeError("ScanKernel cannot "
"deal with non-contiguous arrays")
n, = input_ary.shape
if not n:
return output_ary
unit_size = self.scan_wg_size * self.scan_wg_seq_batches
dev = driver.Context.get_device()
max_groups = 3 * dev.get_attribute(
driver.device_attribute.MULTIPROCESSOR_COUNT)
from pytools import uniform_interval_splitting
interval_size, num_groups = uniform_interval_splitting(
n, unit_size, max_groups)
block_results = allocator(self.dtype.itemsize * num_groups)
dummy_results = allocator(self.dtype.itemsize)
# first level scan of interval (one interval per block)
self.scan_intervals_knl.prepared_async_call(
(num_groups, 1), (self.scan_wg_size, 1, 1), stream,
input_ary.gpudata, n, interval_size, output_ary.gpudata,
block_results)
# second level inclusive scan of per-block results
self.scan_intervals_knl.prepared_async_call(
(1, 1), (self.scan_wg_size, 1, 1), stream, block_results,
num_groups, interval_size, block_results, dummy_results)
# update intervals with result of second level scan
self.final_update_knl.prepared_async_call((
num_groups,
1,
), (self.update_wg_size, 1, 1), stream, output_ary.gpudata, n,
interval_size,
block_results)
return output_ary
def __call__(self, input_ary, output_ary=None, allocator=None,
queue=None):
allocator = allocator or input_ary.allocator
queue = queue or input_ary.queue or output_ary.queue
if output_ary is None:
output_ary = input_ary
if isinstance(output_ary, (str, unicode)) and output_ary == "new":
output_ary = cl_array.empty_like(input_ary, allocator=allocator)
if input_ary.shape != output_ary.shape:
raise ValueError("input and output must have the same shape")
if not input_ary.flags.forc:
raise RuntimeError("ScanKernel cannot "
"deal with non-contiguous arrays")
n, = input_ary.shape
if not n:
return output_ary
unit_size = self.scan_wg_size * self.scan_wg_seq_batches
max_groups = 3*max(dev.max_compute_units for dev in self.devices)
from pytools import uniform_interval_splitting
interval_size, num_groups = uniform_interval_splitting(
n, unit_size, max_groups);
block_results = allocator(self.dtype.itemsize*num_groups)
dummy_results = allocator(self.dtype.itemsize)
# first level scan of interval (one interval per block)
self.scan_intervals_knl(
queue, (num_groups*self.scan_wg_size,), (self.scan_wg_size,),
input_ary.data,
n, interval_size,
output_ary.data,
block_results)
# second level inclusive scan of per-block results
self.scan_intervals_knl(
queue, (self.scan_wg_size,), (self.scan_wg_size,),
block_results,
num_groups, interval_size,
block_results,
dummy_results)
# update intervals with result of second level scan
self.final_update_knl(
queue, (num_groups*self.update_wg_size,), (self.update_wg_size,),
output_ary.data,
n, interval_size,
block_results)
return output_ary
def __call__(self,
input_ary,
output_ary=None,
allocator=None,
queue=None):
allocator = allocator or input_ary.allocator
queue = queue or input_ary.queue or output_ary.queue
if output_ary is None:
output_ary = input_ary
if isinstance(output_ary, (str, unicode)) and output_ary == "new":
output_ary = cl_array.empty_like(input_ary,
allocator=allocator)
if input_ary.shape != output_ary.shape:
raise ValueError("input and output must have the same shape")
if not input_ary.flags.forc:
raise RuntimeError("ScanKernel cannot "
"deal with non-contiguous arrays")
n, = input_ary.shape
if not n:
return output_ary
unit_size = self.scan_wg_size * self.scan_wg_seq_batches
max_groups = 3 * max(dev.max_compute_units for dev in self.devices)
from pytools import uniform_interval_splitting
interval_size, num_groups = uniform_interval_splitting(
n, unit_size, max_groups)
block_results = allocator(self.dtype.itemsize * num_groups)
dummy_results = allocator(self.dtype.itemsize)
# first level scan of interval (one interval per block)
self.scan_intervals_knl(queue, (num_groups * self.scan_wg_size, ),
(self.scan_wg_size, ), input_ary.data, n,
interval_size, output_ary.data,
block_results)
# second level inclusive scan of per-block results
self.scan_intervals_knl(queue, (self.scan_wg_size, ),
(self.scan_wg_size, ), block_results,
num_groups, interval_size, block_results,
dummy_results)
# update intervals with result of second level scan
self.final_update_knl(queue, (num_groups * self.update_wg_size, ),
(self.update_wg_size, ), output_ary.data, n,
interval_size, block_results)
return output_ary
