NVIDIA CUDA 13.3 Enhances GPU Development with Tile Programming in C++, Compiler Autotuning, and Python Updates | NVIDIA Technical Blog

CUDA 13.3 introduces CUDA Tile programming in C++ for high-level, tile-based kernel development, automating parallelism and memory management for portability. CUDA Python 1.0 is released, stabilizing the ecosystem with features like green contexts (partitioning SMs for latency-sensitive kernels), process checkpointing (snapshotting and restoring CUDA state), and inter-process sharing (IPC) for GPU memory. CCCL Python 1.0 adds host-callable parallel algorithms and supports Python lambdas as operators. A new Numba CUDA MLIR backend is introduced, offering a faster JIT compile experience and improved diagnostics for Python GPU programming.