Why offload the points and weights to CPU before DT?

[lizhiqi49]

dmesh/diffdt/cgalwdt.py

Lines 26 to 38 in 8a76623

	with th.no_grad():
	t_positions, t_weights = points.positions, points.weights
	if t_positions.device != th.device('cpu'):
	t_positions = points.positions.cpu()
	if t_weights.device != th.device('cpu'):
	t_weights = points.weights.cpu()
	result = _C.delaunay_triangulation(t_positions,
	t_weights,
	weighted,
	parallelize,
	p_lock_grid_size,
	compute_cc)

I notice that the points and weights are offloaded to cpu before delaunay triangulation, wasn't this process executed in CUDA? And why make the differential DT under torch.no_grad() context?

[SonSang]

Hi, in the current implementation, we first run non-differentiable DT (on CPU) on the weighted point set.
Then, based on the result, we compute the existence probability of the faces. In that part, we use CUDA.
Please see our paper (Sec. 3.2, 4.1) for the details.

[lizhiqi49]

Got it, thanks for your nice reply! BTW, I don't know much about the implementation of DT, and I want to know whether DT running on CPU is an large overhead for computation time compared with being executed on CUDA (if possible)?

[SonSang]

Yes, unfortunately, the DT running on CPU is our computational bottleneck when it comes to a large-scale point cloud ( > 50K). We also searched for a possible CUDA implementation for DT, and there were several papers about it, we could not find suitable one for our paper. So we assume we need a whole new different approach if we truly want to handle a very large point cloud (~= 1M).

[lizhiqi49]

Thanks again for your nice reply! :)