NVIDIA CUDA Tile is one of the most significant additions to NVIDIA CUDA programming and unlocks automatic access to tensor cores and other specialized hardware. Earlier this year, NVIDIA released cuTile for Python , giving Python developers a natural way to write high-performance GPU kernels.  Now, the same programming model is available in Julia through cuTile.jl . In this blog post, we’ll explore how cuTile.jl simplifies the development of high-performance CUDA kernels, demonstrate its idiomatic Julia syntax, and discuss its performance parity with the existing cuTile Python implementation. What is tile-based GPU programming? Traditional GPU programming with CUDA requires developers to think about threads, warps, and memory hierarchies. While powerful, this approach requires the programmer to map algorithms onto hardware efficiently. With CUDA Tile, developers describe operations on tiles of data, and the compiler handles the mapping to hardware. Consider vector addition.…