Comments (4)
Is it because when the object out_normals is passed into the kernel function, the copy of *out_normals copied its data head address, instead of allocating new memory space in GPU? So that when the copy of *out_normals use the operator out_normals(y, x), it modified the corresponding data with respect to the original data head pointer address?
Yes, that is exactly how it works.
from badslam.
To elaborate a bit, there are two classes CUDABuffer
and CUDABuffer_
that behave quite differently. The former one, CUDABuffer
, is a bit higher-level and 'owns' the GPU buffer that it allocates. It is meant to be used from CPU code. For historical reasons (working with old CUDA versions), I could not pass such higher-level objects to CUDA code, since there had been incompatibilities. So I needed a light-weight helper class to access the same functionality from CUDA code. This is what CUDABuffer_
does. It is a simple wrapper over a few attributes of the buffer, and it can be retrieved with CUDABuffer::ToCUDA()
. Copying the CUDABuffer_
object merely creates a copy of the pointer to the buffer, not a copy of the buffer itself.
from badslam.
Thank you very much for your answer!
I have another question about applications/badslam/src/badslam/cuda_depth_processing.cu
.
For the CUDA kernel function ComputeMinMaxDepthCUDAKernel()
, it is a template function. The template arguments are block_width
and block_height
.
In ComputeMinMaxDepthCUDA()
, this function lauch the above CUDA kernel function.
However, I see it passes the template argument block_width
and block_height
without pre-definition.
I searched these two variables in whole project, and it only appears here. These two variables are not defined, how does c++ compiler recognize the two variables?
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block_width
and block_height
are defined by the macro CUDA_AUTO_TUNE_2D_TEMPLATED()
, which calls CUDA_AUTO_TUNE_2D_BORDER_TEMPLATED()
, in libvis/src/libvis/cuda/cuda_auto_tuner.h.
from badslam.
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