How do you optimize GPU computing in Ubuntu?

03 Tem 14:37

I've been experimenting with optimizing GPU computing on Ubuntu without CUDA for a while now. Last month, while working on an image processing tool, I noticed an imbalance in both CPU and GPU usage. First, I did basic profiling and checked OpenGL/Vulkan support with the `glxinfo` and `vainfo` commands. Then, I adjusted the optimal kernel parameters for my NVIDIA GPU by adding `nvidia-drm.modeset=1` to the `GRUB_CMDLINE_LINUX_DEFAULT` section in `/etc/default/grub`, running `update-grub`, and rebooting. This improved screen refresh rate and GPU memory management. Later, when I encountered performance issues with OpenCL-based computations, I scanned the available OpenCL platforms with `clinfo`. I compared Mesa 3D's integrated OpenCL (OCL-ICD-LLVM) with ROCm's AMD GPU-compatible version. I preferred ROCm because it provided more consistent performance across both AMD and NVIDIA cards. Additionally, I optimized the configuration files in `/etc/OpenCL/vendors` to clarify which platform serves which GPU. Ultimately, I achieved about a 25% performance gain in OpenCL computations. Finally, I used profiling tools to investigate bottlenecks. Instead of `nvidia-smi`, I used `nvprof` and `rocm-smi` to analyze which kernels were consuming GPU resources. In some cases, I found that GPU memory size and bandwidth limits directly impacted computation speed. Therefore, when running compute-intensive workloads, I preferred disabling unnecessary graphical effects to focus more resources on the GPU. This strategy effectively "freed up" the GPU. As a tip, thoroughly research the exact versions of your GPU and drivers. For example, before using NVIDIA's 470 series drivers, I compared performance and found that newer versions' stability can sometimes be a deciding factor. Since Ubuntu frequently updates its kernel, even if you stick to the LTS version, you may need to update your settings 2-3 times a year.