How to Optimize a CUDA Matmul Kernel for cuBLAS-Like Performance: A Worklog (siboehm.com)

A recent worklog details an ongoing project to optimize a CUDA implementation of matrix multiplication, aiming to achieve performance comparable to NVIDIA's cuBLAS library. The author systematically applies various optimization techniques to incrementally improve performance metrics. These optimizations include coalescing global memory accesses, utilizing shared memory, and advanced block tiling strategies, leading to considerable gains in computational efficiency. By refining the kernel from a naive approach to a highly optimized version, the final implementation reaches almost 94% of cuBLAS performance. This work is significant for the AI/ML community as matrix multiplication (GEMM) is fundamental to deep learning model training and inference, representing a majority of the floating-point operations performed during these processes. The author emphasizes the importance of understanding GPU architecture, memory access patterns, and thread management to derive optimal performance from CUDA programs. Key technical insights include the impact of global memory coalescing and the utilization of shared memory to minimize latency, which are crucial for enhancing throughput on modern GPUs. This project not only serves as a practical guide for developers but also contributes to the wider discourse on GPU performance optimization in deep learning applications.