#NVIDIA's impressive $3 trillion valuation owes much to its mastery of matrix multiplication, a critical tool at the core of machine learning development.

Here’s a peek at how to get up to 93% of NVIDIA's cuBLAS library performance:
1. Basic Matrix Multiplication: Starts our journey with basic operations, yielding 309 GFLOPs/s.
2. Memory Optimization: Advances through techniques like memory coalescing to enhance performance to 1986 GFLOPs/s.
3. Efficiency Scaling: Utilizes block and warp tiling to push limits up to 21779 GFLOPs/s, representing 93.7% of cuBLAS’s capabilities.

For an in-depth look at each kernel’s optimization and its impact, check out the detailed analysis here: https://siboehm.com/articles/22/CUDA-MMM

Arjun Jain says that he remembers, back in 2008, in the very early days of CUDA, you couldn’t even write a printf inside a kernel and had to transfer memory back to the CPU just to debug and print it—we’ve definitely come a long way!

At Fast Code AI, we specialize in solving such tough challenges, continually pushing the boundaries of what's possible in computational performance and innovation with #excellence and #integrity.