This month, Nvidia rolled out what might be one of the most important updates for its CUDA GPU software stack in years. The new data-analytics-id=”inline-link” href=”https://developer.nvidia.com/blog/nvidia-cuda-13-1-powers-next-gen-gpu-programming-with-nvidia-cuda-tile-and-performance-gains/” data-url=”https://developer.nvidia.com/blog/nvidia-cuda-13-1-powers-next-gen-gpu-programming-with-nvidia-cuda-tile-and-performance-gains/” target=”_blank” referrerpolicy=”no-referrer-when-downgrade” data-hl-processed=”none” data-mrf-recirculation=”inline-link”>CUDA 13.1 release introduces the data-analytics-id=”inline-link” href=”https://developer.nvidia.com/blog/focus-on-your-algorithm-nvidia-cuda-tile-handles-the-hardware/” data-url=”https://developer.nvidia.com/blog/focus-on-your-algorithm-nvidia-cuda-tile-handles-the-hardware/” target=”_blank” referrerpolicy=”no-referrer-when-downgrade” data-hl-processed=”none” data-mrf-recirculation=”inline-link”>CUDA Tile programming path, which elevates kernel development above the single-instruction, multiple-thread (SIMT) execution model, and aligns it with the tensor-heavy execution model of Blackwell-class processors and their successors.
By shifting to structured data blocks, or tiles, Nvidia is changing how developers design GPU workloads,…
