Squeeze-and-Excitation Networks

Tile primitives for speedy kernels

🎉 Modern CUDA Learn Notes with PyTorch: fp32, fp16, bf16, fp8/int8, flash_attn, sgemm, sgemv, warp/block reduce, dot, elementwise, softmax, layernorm, rmsnorm.

FlashInfer: Kernel Library for LLM Serving

Instructions, Docker images, and examples for Nsight Compute and Nsight Systems

CUDA Matrix Multiplication Optimization

PyTorch-Based Fast and Efficient Processing for Various Machine Learning Applications with Diverse Sparsity