remove debug helpers
@@ -144,33 +144,6 @@ cpdef inline check_result(int result):
raise CuFFTError(result)
-# for debugging
-def _multi_gpu_check_data_integraty(intptr_t in_arr=0):
- cdef intptr_t ptr
- cdef XtArray* xtArr
-
- if in_arr == 0:
- return
- xtArr = <XtArray*>in_arr
-
- print(xtArr.version)
- print(xtArr.descriptor.version)
- print(xtArr.descriptor.nGPUs)
- for i in range(MAX_CUDA_DESCRIPTOR_GPUS):
- print(xtArr.descriptor.GPUs[i], end=' ')
- print()
- for i in range(MAX_CUDA_DESCRIPTOR_GPUS):
- print(<intptr_t>xtArr.descriptor.data[i], end=' ')
- print()
- for i in range(MAX_CUDA_DESCRIPTOR_GPUS):
- print(xtArr.descriptor.size[i], end=' ')
- print()
- print(<intptr_t>xtArr.descriptor.cudaXtState)
- print(xtArr.library)
- print(<int>xtArr.subFormat)
- print(<intptr_t>xtArr.libDescriptor)
-
-
# This is necessary for single-batch transforms: "when batch is one, data is
# left in the GPU memory in a permutation of the natural output", see
# https://docs.nvidia.com/cuda/cufft/index.html#multiple-GPU-cufft-intermediate-helper # NOQA
@@ -660,11 +633,6 @@ class Plan1d(object):
'out dtype mismatch: found {}, expected {}'.format(
out.dtype, a.dtype))
- # for debugging
- def _multi_gpu_check_data_integraty(self, intptr_t in_arr=0):
- cdef intptr_t ptr = self.xtArr
- _multi_gpu_check_data_integraty(ptr)
-
class PlanNd(object):
def __init__(self, object shape, object inembed, int istride,
