def run(double_precision=False):
context = cl.create_some_context()
queue = cl.CommandQueue(context)
dtype = np.complex64 if not double_precision else np.complex128
n_run = 100 #set to 1 for testing for correct result
if n_run > 1:
nd_dataC = np.random.normal(size=(1024, 1024)).astype(dtype)
else:
nd_dataC = np.ones((1024, 1024), dtype=dtype) #set n_run to 1
nd_dataF = np.asfortranarray(nd_dataC)
dataC = cla.to_device(queue, nd_dataC)
dataF = cla.to_device(queue, nd_dataF)
nd_result = np.zeros_like(nd_dataC, dtype=dtype)
resultC = cla.to_device(queue, nd_result)
resultF = cla.to_device(queue, np.asfortranarray(nd_result))
result = resultF
axes_list = [(-2, -1), (-1, -2), None] #batched 2d transforms
if True:
print('out of place transforms', dataC.shape, dataC.dtype)
print('axes in out')
for axes in axes_list:
for data in (dataC, dataF):
for result in (resultC, resultF):
t_ms, gflops = 0, 0
try:
transform = FFT(context,
queue,
data,
result,
axes=axes)
#transform.plan.transpose_result = True #not implemented for some transforms (works e.g. for out of place, (2,1) C C)
print(
'%-10s %3s %3s' % (
axes,
'C' if data.flags.c_contiguous else 'F',
'C' if result.flags.c_contiguous else 'F',
),
end=' ',
)
tic = timeit.default_timer()
for i in range(n_run):
events = transform.enqueue()
#events = transform.enqueue(False)
for e in events:
e.wait()
toc = timeit.default_timer()
t_ms = 1e3 * (toc - tic) / n_run
gflops = 5e-9 * np.log2(np.prod(
transform.t_shape)) * np.prod(
transform.t_shape) * transform.batchsize / (
1e-3 * t_ms)
npfft_result = npfftn(nd_dataC, axes=axes)
if transform.plan.transpose_result:
npfft_result = np.swapaxes(npfft_result, axes[0],
axes[1])
max_error = np.max(abs(result.get() - npfft_result))
print('%8.1e' % max_error, end=' ')
assert_allclose(
result.get(),
npfft_result,
atol=1e-8 if double_precision else 1e-3,
rtol=1e-8 if double_precision else 1e-3)
#assert_array_almost_equal(abs(result.get() - npfftn(data.get(), axes = axes)),
# 1e-4)
except GpyFFT_Error as e:
print(e)
except AssertionError as e:
print(e)
except Exception as e:
print(e)
finally:
print('%5.2fms %6.2f Gflops' % (t_ms, gflops))
print('in place transforms', nd_dataC.shape, nd_dataC.dtype)
for axes in axes_list:
for nd_data in (nd_dataC, nd_dataF):
data = cla.to_device(queue, nd_data)
transform = FFT(context, queue, data, axes=axes)
#transform.plan.transpose_result = True #not implemented
tic = timeit.default_timer()
for i in range(n_run): # inplace transform fails for n_run > 1
events = transform.enqueue()
for e in events:
e.wait()
toc = timeit.default_timer()
t_ms = 1e3 * (toc - tic) / n_run
gflops = 5e-9 * np.log2(np.prod(transform.t_shape)) * np.prod(
transform.t_shape) * transform.batchsize / (1e-3 * t_ms)
print(
'%-10s %3s %5.2fms %6.2f Gflops' %
(axes, 'C' if data.flags.c_contiguous else 'F', t_ms, gflops))
def run():
context = cl.create_some_context()
queue = cl.CommandQueue(context)
n_run = 10 #set to 1 for proper testing
if n_run > 1:
#nd_dataC = np.zeros((1024, 1024), dtype = np.complex64) #for benchmark
nd_dataC = np.zeros((4,1024, 1024), dtype = np.complex64) #for benchmark
#nd_dataC = np.zeros((128,128,128), dtype = np.complex64) #for benchmark
else:
nd_dataC = np.ones((4,1024, 1024), dtype = np.complex64) #set n_run to 1
#nd_dataC = np.array([[1,2,3,4], [5,6,7,8]], dtype = np.complex64) #small array
nd_dataF = np.asfortranarray(nd_dataC)
dataC = cla.to_device(queue, nd_dataC)
dataF = cla.to_device(queue, nd_dataF)
nd_result = np.zeros_like(nd_dataC, dtype = np.complex64)
resultC = cla.to_device(queue, nd_result)
resultF = cla.to_device(queue, np.asfortranarray(nd_result))
result = resultF
#axes_list = [(0,), (1,), (0,1)] #is (1,0) the same?
#axes_list = [(1,0), (0,1), (1,2), (2,1)]
#axes_list = [(1,2), (2,1)]
axes_list = [(1,0), (0,1), (1,2), (2,1), (0,1,2), (2,1,0)]
if True:
print('out of place transforms', dataC.shape)
print('axes in out')
for axes in axes_list:
for data in (dataC, dataF):
for result in (resultC, resultF):
try:
transform = FFT(context, queue, (data,), (result,), axes = axes)
#transform.plan.transpose_result = True #not implemented for some transforms (works e.g. for out of place, (2,1) C C)
tic = time.clock()
for i in range(n_run):
events = transform.enqueue()
#events = transform.enqueue(False)
for e in events:
e.wait()
toc = time.clock()
t_ms = 1e3*(toc-tic)/n_run
gflops = 5e-9 * np.log2(np.prod(transform.t_shape))*np.prod(transform.t_shape) * transform.batchsize / (1e-3*t_ms)
print('%-10s %3s %3s %5.2fms %6.2f Gflops' % (
axes,
'C' if data.flags.c_contiguous else 'F',
'C' if result.flags.c_contiguous else 'F',
t_ms, gflops
))
assert_array_almost_equal(result.get(), npfftn(data.get(), axes = axes))
except GpyFFT_Error as e:
print(e)
except AssertionError as e:
print(e)
print()
print('in place transforms', nd_dataC.shape)
for axes in axes_list:
for nd_data in (nd_dataC, nd_dataF):
data = cla.to_device(queue, nd_data)
transform = FFT(context, queue, (data,), axes = axes)
#transform.plan.transpose_result = True #not implemented
tic = time.clock()
for i in range(n_run): # inplace transform fails for n_run > 1
events = transform.enqueue()
for e in events:
e.wait()
toc = time.clock()
t_ms = 1e3*(toc-tic)/n_run
gflops = 5e-9 * np.log2(np.prod(transform.t_shape))*np.prod(transform.t_shape) * transform.batchsize / (1e-3*t_ms)
print('%-10s %3s %5.2fms %6.2f Gflops' % (
axes,
'C' if data.flags.c_contiguous else 'F',
t_ms, gflops
))
tic = time.clock()
for i in range(n_run):
events = transform.enqueue()
#events = transform.enqueue(False)
for e in events:
e.wait()
toc = time.clock()
t_ms = 1e3*(toc-tic)/n_run
gflops = 5e-9 * np.log2(np.prod(transform.t_shape))*np.prod(transform.t_shape) * transform.batchsize / (1e-3*t_ms)
print('%-10s %3s %3s %5.2fms %4d Gflops'%(
axes,
'C' if data.flags.c_contiguous else 'F',
'C' if result.flags.c_contiguous else 'F',
t_ms, gflops
))
assert_array_almost_equal(result.get(), npfftn(data.get(), axes = axes))
except gpyfft.GpyFFT_Error as e:
print(e)
except AssertionError as e:
print(e)
print()
print('in place transforms', nd_dataC.shape)
for axes in axes_list:
for nd_data in (nd_dataC, nd_dataF):
data = cla.to_device(queue, nd_data)
transform = FFT(context, queue, (data,), axes = axes)
#transform.plan.transpose_result = True #not implemented
tic = time.clock()
for i in range(n_run): #inplace transform fails for n_run > 1
def run():
context = cl.create_some_context()
queue = cl.CommandQueue(context)
n_run = 10 #set to 1 for proper testing
if n_run > 1:
#nd_dataC = np.zeros((1024, 1024), dtype = np.complex64) #for benchmark
nd_dataC = np.zeros((4, 1024, 1024),
dtype=np.complex64) #for benchmark
#nd_dataC = np.zeros((128,128,128), dtype = np.complex64) #for benchmark
else:
nd_dataC = np.ones((4, 1024, 1024),
dtype=np.complex64) #set n_run to 1
#nd_dataC = np.array([[1,2,3,4], [5,6,7,8]], dtype = np.complex64) #small array
nd_dataF = np.asfortranarray(nd_dataC)
dataC = cla.to_device(queue, nd_dataC)
dataF = cla.to_device(queue, nd_dataF)
nd_result = np.zeros_like(nd_dataC, dtype=np.complex64)
resultC = cla.to_device(queue, nd_result)
resultF = cla.to_device(queue, np.asfortranarray(nd_result))
result = resultF
#axes_list = [(0,), (1,), (0,1)] #is (1,0) the same?
#axes_list = [(1,0), (0,1), (1,2), (2,1)]
#axes_list = [(1,2), (2,1)]
axes_list = [(1, 0), (0, 1), (1, 2), (2, 1), (0, 1, 2), (2, 1, 0)]
if True:
print('out of place transforms', dataC.shape)
print('axes in out')
for axes in axes_list:
for data in (dataC, dataF):
for result in (resultC, resultF):
try:
transform = FFT(context,
queue, (data, ), (result, ),
axes=axes)
#transform.plan.transpose_result = True #not implemented for some transforms (works e.g. for out of place, (2,1) C C)
tic = time.clock()
for i in range(n_run):
events = transform.enqueue()
#events = transform.enqueue(False)
for e in events:
e.wait()
toc = time.clock()
t_ms = 1e3 * (toc - tic) / n_run
gflops = 5e-9 * np.log2(np.prod(
transform.t_shape)) * np.prod(
transform.t_shape) * transform.batchsize / (
1e-3 * t_ms)
print('%-10s %3s %3s %5.2fms %6.2f Gflops' %
(axes, 'C' if data.flags.c_contiguous else 'F',
'C' if result.flags.c_contiguous else 'F', t_ms,
gflops))
assert_array_almost_equal(
result.get(), npfftn(data.get(), axes=axes))
except GpyFFT_Error as e:
print(e)
except AssertionError as e:
print(e)
print()
print('in place transforms', nd_dataC.shape)
for axes in axes_list:
for nd_data in (nd_dataC, nd_dataF):
data = cla.to_device(queue, nd_data)
transform = FFT(context, queue, (data, ), axes=axes)
#transform.plan.transpose_result = True #not implemented
tic = time.clock()
for i in range(n_run): # inplace transform fails for n_run > 1
events = transform.enqueue()
for e in events:
e.wait()
toc = time.clock()
t_ms = 1e3 * (toc - tic) / n_run
gflops = 5e-9 * np.log2(np.prod(transform.t_shape)) * np.prod(
transform.t_shape) * transform.batchsize / (1e-3 * t_ms)
print(
'%-10s %3s %5.2fms %6.2f Gflops' %
(axes, 'C' if data.flags.c_contiguous else 'F', t_ms, gflops))
tic = time.clock()
for i in range(n_run):
events = transform.enqueue()
#events = transform.enqueue(False)
for e in events:
e.wait()
toc = time.clock()
t_ms = 1e3*(toc-tic)/n_run
gflops = 5e-9 * np.log2(np.prod(transform.t_shape))*np.prod(transform.t_shape) * transform.batchsize / (1e-3*t_ms)
print '%-10s %3s %3s %5.2fms %4d Gflops'%(
axes,
'C' if data.flags.c_contiguous else 'F',
'C' if result.flags.c_contiguous else 'F',
t_ms, gflops
)
assert_array_almost_equal(result.get(), npfftn(data.get(), axes = axes))
except gpyfft.GpyFFT_Error, e:
print e
except AssertionError, e:
print e
print
print 'in place transforms', nd_dataC.shape
for axes in axes_list:
for nd_data in (nd_dataC, nd_dataF):
data = cla.to_device(queue, nd_data)
transform = FFT(context, queue, (data,), axes = axes)
#transform.plan.transpose_result = True #not implemented
tic = time.clock()
for i in range(n_run): #inplace transform fails for n_run > 1
def run(double_precision=False):
context = cl.create_some_context()
queue = cl.CommandQueue(context)
dtype = np.complex64 if not double_precision else np.complex128
n_run = 100 #set to 1 for proper testing
if n_run > 1:
nd_dataC = np.random.normal(size=(4,1024, 1024)).astype(dtype) #faster than 1024x1024?
else:
nd_dataC = np.ones((4,1024, 1024), dtype = dtype) #set n_run to 1
nd_dataF = np.asfortranarray(nd_dataC)
dataC = cla.to_device(queue, nd_dataC)
dataF = cla.to_device(queue, nd_dataF)
nd_result = np.zeros_like(nd_dataC, dtype = dtype)
resultC = cla.to_device(queue, nd_result)
resultF = cla.to_device(queue, np.asfortranarray(nd_result))
result = resultF
axes_list = [(1,2), (2,1)] #batched 2d transforms
if True:
print('out of place transforms', dataC.shape, dataC.dtype)
print('axes in out')
for axes in axes_list:
for data in (dataC,
dataF):
for result in (resultC,
resultF):
try:
transform = FFT(context, queue, data, result, axes = axes)
#transform.plan.transpose_result = True #not implemented for some transforms (works e.g. for out of place, (2,1) C C)
print('%-10s %3s %3s'
% (
axes,
'C' if data.flags.c_contiguous else 'F',
'C' if result.flags.c_contiguous else 'F',
),
end=' ',
)
tic = timeit.default_timer()
for i in range(n_run):
events = transform.enqueue()
#events = transform.enqueue(False)
for e in events:
e.wait()
toc = timeit.default_timer()
t_ms = 1e3*(toc-tic)/n_run
gflops = 5e-9 * np.log2(np.prod(transform.t_shape))*np.prod(transform.t_shape) * transform.batchsize / (1e-3*t_ms)
npfft_result = npfftn(nd_dataC, axes = axes)
if transform.plan.transpose_result:
npfft_result = np.swapaxes(npfft_result, axes[0], axes[1])
max_error = np.max(abs(result.get() - npfft_result))
print('%8.1e'%max_error, end=' ')
assert_allclose(result.get(), npfft_result,
atol = 1e-8 if double_precision else 1e-3,
rtol = 1e-8 if double_precision else 1e-3)
#assert_array_almost_equal(abs(result.get() - npfftn(data.get(), axes = axes)),
# 1e-4)
except GpyFFT_Error as e:
print(e)
t_ms, gflops = 0, 0
except AssertionError as e:
print(e)
finally:
print('%5.2fms %6.2f Gflops' % (t_ms, gflops) )
print('in place transforms', nd_dataC.shape, nd_dataC.dtype)
for axes in axes_list:
for nd_data in (nd_dataC, nd_dataF):
data = cla.to_device(queue, nd_data)
transform = FFT(context, queue, data, axes = axes)
#transform.plan.transpose_result = True #not implemented
tic = timeit.default_timer()
for i in range(n_run): # inplace transform fails for n_run > 1
events = transform.enqueue()
for e in events:
e.wait()
toc = timeit.default_timer()
t_ms = 1e3*(toc-tic)/n_run
gflops = 5e-9 * np.log2(np.prod(transform.t_shape))*np.prod(transform.t_shape) * transform.batchsize / (1e-3*t_ms)
print('%-10s %3s %5.2fms %6.2f Gflops' % (
axes,
'C' if data.flags.c_contiguous else 'F',
t_ms, gflops
))