6/29/2023 0 Comments Houdini gpu rendering![]() ![]() the openCL Kernels (code/snippets) can only handle the data from local GPU/Memory which it got send from the Host program (OpenCL wrangle node). the host program detect hardware GPU and complies and send the kernels to the GPU and feed the kernels with Data. in Houdini the openCL wrangle is the host program. ![]() ![]() the host program is usually written mid/high level language like (C, python etc.) the host program handles data management, complies kernels etc. written stuff for GPU needs 2 things, a host program and a kernel. I realize that might be great for GPU render engines like Octane or Redshift, but does it give Houdini an incredible amount of extra performance? Linking two expensive cards together like that, what kind of scenerio would be the limit in a sense? When might Houdini hit a bottleneck if a studio or professional that could afford a configuration like that?ĭoes OpenCL use linked cards like that too? Large amount of VRAM? Is that a huge advantage over the one if you linked those via NVLink? Could a single Titan RTX handle most anything Houdini throws at it, or would someone see a dramatic increase in performance, and how so, if they added another Titan RTX. What would be most ideal? Like, if you were doing massive simulations or were to hypothetically use a Quadro RTX card, is that better overall, or more suited to just have one card? I don't really understand how it utilizes multiple cards if at all, and if another card is a bit of a waste. Does Houdini just use one pair, one card, all four, or would it be best to set the environment variable in a way so that one pair is used for GPU, and the other is OpenCL does it matter? Say you have two 4 2080ti's linked in pairs with NVLink. Could someone please explain how exactly Houdini uses the GPU in a few different scenerios? Apart from drawing the viewport, what else is actually going on under the hood? How and when does Houdini use the GPU over the CPU? or both? ![]()
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