TileLang is a Python‑style DSL built on TVM that separates algorithm logic from hardware scheduling, offers beginner to expert interfaces, supports multiple GPU and CPU backends, and delivers performance on par with or better than existing AI kernels, as demonstrated with GEMM, FlashAttention and other benchmarks.
The article explains why standard GEMM libraries may fall short for special matrix shapes, introduces NVIDIA’s open‑source CUTLASS library, details its hierarchical tiling architecture, and walks through a complete device‑API example that customizes tile sizes and data layouts to achieve near‑hand‑written kernel performance on modern GPUs.
This article presents a comprehensive benchmark of DeepGEMM, Cutlass, and Triton on NVIDIA H20 and H800 GPUs, analyzing TFLOPS, bandwidth, latency, and speedup across various matrix sizes, and concludes which library is optimal for different workload scenarios.
DeepGEMM is an open‑source FP8‑precision GEMM library that delivers up to 1350 TFLOPS on NVIDIA Hopper GPUs, offering JIT‑compiled, lightweight code (~300 lines) for dense and MoE matrix multiplication, with easy deployment, configurable environment variables, and performance advantages over CUTLASS for large AI models.
