The article provides a detailed walkthrough of implementing a naive CUDA port and optimizing matrix multiplication (matmul) kernels for better GPU performance. It explains the challenges of under-utilizing CUDA cores and memory load coalescing issues, and how to address them by leveraging more threads and efficient cooperation within blocks. The article concludes with a preliminary GPU implementation achieving a throughput of 51.7 tokens per second, demonstrating significant performance gains through these optimizations.
Key takeaways:
- The article discusses building a large language model (LLM) inference engine from scratch using C++ and CUDA without relying on external libraries, aiming to optimize single-GPU inference throughput.
- It highlights the importance of understanding the full stack of LLM inference, especially as AI models are increasingly deployed on edge devices, and emphasizes the need for optimizations to improve inference speed.
- The article provides a detailed overview of LLM architectures and inference mechanics, focusing on the Mistral v0.2 architecture and discussing various optimizations like multithreading, weight quantization, and SIMD.
- It explores the challenges and solutions in implementing efficient matrix multiplication (matmul) on GPUs using CUDA, emphasizing the importance of thread utilization and memory read coalescing for achieving high throughput.