I'm diving into the world of local large language models and doing some research on optimizing GPU performance, particularly on AMD GPUs and open-source projects. I've read that multiple GPUs don't utilize their parallel capabilities as effectively as they could, and I'm curious about how to improve this. I have a keen interest in low-level programming languages as well and wonder what the most basic language is for contributing to this area. I'm completely new to GPU and AI development, so any tips or guidance would be greatly appreciated!
3 Answers
Before diving into optimizations, it's crucial to understand how the system you're working on operates. If you're focusing on neural networks, for instance, familiarize yourself with the computations during forward and backward passes. Explore how existing frameworks are set up and identify their limitations. Your speedup will mostly hinge on the number of compute units and the overhead related to communication between them. Start with simpler tasks and gradually ramp up your complexity. Typically, you'll find opportunities for improvement in the communication overhead between GPUs.
It's interesting that you aren't keen on using CUDA, yet you're looking to help hardware manufacturers. Starting with CUDA might be beneficial, as it provides a strong foundation for understanding GPU efficiency. It'd give you insights into optimizations directly related to its capabilities.
Although I haven't done extensive GPU development, I know from game development that the prevalent languages for this work are C and C++. Many AI and ML projects use Python, but they often integrate with lower-level code through a C API. Focusing on these languages could be your path to making meaningful contributions.
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