NVIDIA is introducing CUDA Tile as part of CUDA 13.1, calling it the largest advancement to the NVIDIA CUDA platform since it was invented in 2006. CUDA Tile provides a virtual instruction set for tile-based parallel programming, enabling developers to express algorithms at a higher level than the traditional single-instruction, multiple-thread (SIMT) model. The announcement frames CUDA Tile as a way to reduce the complexity of writing high-performance GPU code across multiple architectures by abstracting low-level hardware details.
The article positions CUDA Tile alongside existing libraries such as NVIDIA CUDA-X and NVIDIA CUTLASS, which already help developers extract performance from GPUs. It explains that with evolving workloads, especially in Artificial Intelligence, tensors have become a fundamental data type and that NVIDIA has created specialized hardware to accelerate tensor operations. The hardware named in the article includes NVIDIA Tensor Cores (TC) and NVIDIA Tensor Memory Accelerators (TMA), both of which the article says are integral to every new GPU architecture. CUDA Tile aims to hide the specifics of these tensor hardware units so that code written with CUDA Tile remains compatible across current and future tensor core designs.
The piece contrasts the flexibility and fine-grained control afforded by the SIMT programming model with the additional effort required to achieve good performance across architectures. By introducing a higher-level tile abstraction and a virtual instruction set, CUDA Tile is presented as a path to write algorithms without needing to manage specialized programming models for tensor cores directly. The article focuses on the compatibility benefits and the potential for simplified development workflows for tensor-heavy workloads, particularly those common in Artificial Intelligence.
