Develop High-Performance GPU Kernels in C++ with NVIDIA CUDA Tile

2026-05-26 · Source: NVIDIA Technical Blog · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Advanced, long

Summary

NVIDIA CUDA Tile programming, initially launched with CUDA 13.1 for Python, now supports C++ with the release of CUDA 13.3. This enables developers to write highly optimized GPU kernels using tile-based abstractions within existing C++ GPU codebases. CUDA Tile automates low-level GPU programming details like parallelism, asynchrony, and memory movement, making it portable across NVIDIA GPU architectures. The model utilizes multi-dimensional arrays, tiles, kernels, and blocks, offering an alternative or addition to the single instruction, multiple threads (SIMT) model. The article demonstrates its application through vector addition and matrix multiplication examples, highlighting optimizations such as "__restrict__" pointers and 16-byte memory alignment. Developers can profile Tile C++ kernels using NVIDIA Nsight Compute, which provides specific "Tile Statistics." Running CUDA Tile C++ requires a GPU with compute capability 8.x or newer, NVIDIA Driver R580 or later, and CUDA Toolkit 13.3.

Key takeaway

For GPU developers optimizing C++ CUDA kernels, adopting NVIDIA CUDA Tile with CUDA 13.3 simplifies performance-critical tasks. You can now abstract complex low-level GPU programming, such as explicit thread management and memory movement, into a more intuitive tile-based model. This allows you to focus on algorithms while the compiler handles hardware specifics, potentially accelerating development and improving portability across NVIDIA architectures. Consider migrating existing SIMT kernels to Tile C++ for streamlined, high-performance GPU code.

Key insights

CUDA Tile C++ simplifies high-performance GPU kernel development by abstracting low-level details through a portable tile-based programming model.

Principles

• Tile-based programming abstracts GPU hardware complexities.
• Multi-dimensional arrays and tiles are core data structures.
• Compiler handles parallelism, asynchrony, and memory movement.

Method

Define tensor spans, partition views with tile shapes, load/store tiles, and apply operations like "ct::mma". Use "__tile_global__" for kernels.

In practice

• Use "__restrict__" for non-aliased pointers.
• Align pointers to 16-byte boundaries for efficiency.
• Profile Tile C++ kernels with NVIDIA Nsight Compute.

Topics

• NVIDIA CUDA Tile
• C++ GPU Programming
• GPU Kernel Optimization
• Tile-based Programming
• NVIDIA CUDA 13.3
• Nsight Compute

Best for: Machine Learning Engineer, AI Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA Technical Blog.