PyTorch kernel performance deep dive with KernelBench

This article explores how GPU kernels operate under the hood in modern PyTorch pipelines, contrasting raw CUDA with Triton and examining core optimizations like operator fusion, streaming online softmax, and tiled matrix multiplication. The author explains the roofline model to show how memory bandwidth constraints dominate everyday deep-learning operations. Finally, the piece reviews KernelBench to evaluate whether frontier language models can autonomously write production kernels that outperform optimizing compilers and human engineers. Open article →