tilelang-cuda-basics

You can write high-performance GPU kernels using a Python-like DSL by leveraging TileLang CUDA to reduce development complexity and enable productive kernel design through standard patterns and built-in primitives.

Standard patterns for CUDA acceleration with TileLang include JIT decoration, T.Kernel usage, and built-in primitives like T.gemm and T.reduce, alongside grid and thread block configuration for parallel execution.

TileLang handles memory for parallel execution by allowing developers to configure grid and thread blocks, and by managing shared and local memory usage to optimize common GPU programming patterns.

Prior CUDA knowledge is helpful but not strictly required, as TileLang introduces core concepts, kernel structure, and elemental operations for matrix computations through a Python-like DSL.

Yes, you can use built-in primitives like T.gemm for matrix computations and T.reduce for reductions within JIT-decorated T.Kernel structures to execute performant GPU kernels.

The best way to start is by running the tilelang-cuda-basics tutorial, which guides you through compiling and running a simple CUDA kernel to understand the foundational memory models and programming patterns.