Ph-trees can do range and closest queries across N dimensions very quickly. I have not used it for 1 dimension, but I’d imagine it would work fine.

https://github.com/tzaeschke/phtree

Can you share sample code I can try or documentation I can follow of using an AMD GPU in that way (shared, virtualized, using only open source drivers)?

You really piqued my interest. I use docker/podman.

W/ an AMD graphics card, eglinfo on the host shows the card is AMD Radeon and driver is matching that.

In the container, without --gpus=all, it shows the card is unknown and the driver is “swrast” (so just CPU fallback).

To make --gpus=all work, it gives the error

docker: Error response from daemon: could not select device driver “” with capabilities: [[gpu]

I was doing a bad job searching before. I found that AMD can share the GPU, it just works a little differently in terms of how to launch the container. https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/amdgpu-install.html#amdgpu-install-dkms

But sadly my AMD GPU is too old/junk to have current driver support.

Anyways, appreciate the reply! Now I can mod my code to run on cheaper cloud instances.

(Note I’m an OpenGL/3D app developer, but probably OpenCL works about the same architecturally)

AFIK it’s only NVIDIA that allows containers shared access to a GPU on the host.

With the majority of code being deployed in containers, you end up locked into the NVIDIA ecosystem even if you use OpenCL. So I guess people just use CUDA since they are limited by the container requirement anyways.

That’s from my experience using OpenGL headless. If I’m wrong please correct me; I’d prefer being GPU agnostic.