Open acdupont opened 4 years ago
Hi @acdupont,
Your GPU is simply running out of memory here. Using 16 wavelenghts in Spectrum<Float, 16>
will definitely increase the memory usage of the renderer.
You can also try to lower the amount of samples per pixel, or the resolution of the renders. Otherwise, there is also the possibility to split the rendering process in multiple passes using the samples_per_pass
property of the integrator.
Nice, I didn't know about the samples_per_pass
setting. I successfully rendered on the GPU with the following settings:
256x256 resolution
256 samples per pixel
16 samples_per_pass
But this is low resolution and samples per pixel. I experimented with different settings like 1024x1024 image, or 512 samples per pixel, and I get a GPU allocation error. I then looked at how much memory was used by the CPU, using the following settings: 1024x1024 resolution 256 samples per pixel
I observe about 22M of memory usage in my system monitor.
I tried this run on my GPU using 8 samples_per_pass, but got the memory allocation error. I have 8G of memory on my GPU, why is so much more memory being used on the GPU? If 22M of memory is used when running on the CPU, is it expected that running on the GPU will take 8G+ memory?
Hi, I have a laptop with i9-8950HK CPU(6 cores, 12 threads) and 1080 GPU. When I tried gpu_rgb mode on cbox.xml, I found it was about only 2x faster than scalar_rgb mode(no embree), is it normal? Because I supposed it would be 20-30x faster....
We are currently in the process of refactoring the whole codebase on top of a full rewrite of the enoki library. This will very likely improve both performance and memory consumption for gpu_*
modes.
It is still going to take us a few weeks before we can release it. Stay tuned 😉
Hi, I have a laptop with i9-8950HK CPU(6 cores, 12 threads) and 1080 GPU. When I tried gpu_rgb mode on cbox.xml, I found it was about only 2x faster than scalar_rgb mode(no embree), is it normal? Because I supposed it would be 20-30x faster....
I have a CPU with 6 cores and 12 threads and a GTX 1080, too. But my gpu_rgb
version is even slower than scalar_rgb
on cbox.xml.. and it won't let me do 256 res/ 256 spp without samples_per_pass
.
I am trying to render the cbox-spectral on the GPU, and I am getting a cuda_malloc error. Debug printouts show that 6.5664 GB of memory is freed after the allocation error which is a huge amount of memory considering such a small scene.
The gpu configuration is as follows:
Here is output from debug level 2:
additionally, here is the last few lines of debug level 4:
Here are my gpu specs from
nvidia-smi
: