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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. In the traditional GPU programming model, using CUDA.jl, the programmer must manage individual threads explicitly: using CUDA function vadd(a, b, c, n) i = (blockIdx().x - 1) * blockDim().x + threadIdx().x if i <= n @inbounds c[i] = a[i] + b[i

cuTile BASIC is an expression of the CUDA Tile programming model in BASIC, built on top of the CUDA Tile IR specification. It enables you to write tile kernels in BASIC using a tile-based model, which is a natural fit for a programming language like BASIC which predates multi-threaded programming. cuTile BASIC is the perfect marriage of the power of GPUs with the anachronistic charm and syntactic simplicity of the BASIC programming language – an elegant language, from a more pixelated era. Manually numbering your lines of code never looked so good or ran so fast!

BASIC is one of the oldest programming languages around and as such, is revered by a whole generation of developers who remember the sound of a 300 baud dial-up modem handshaking fondly. For many such developers, BASIC was their first introduction to computer programming. Now, developers with BASIC still burned into their brains can take legacy applications onto NVIDIA GPU-accelerated computing for the first time. This unlocks performance and functionality the BASIC programming language could never have previously imagined – allowing your Lunar Lander to zip around the moon’s surface faster th

CUDA Tile C++ is an expression of the CUDA Tile programming model in C++, built on top of the CUDA Tile IR specification. It enables developers to write tile kernels in C++ and express GPU kernels using a tile-based model, rather than or in addition to a single instruction, multiple threads (SIMT) model.  As a refresher, in the tile model: Multi-dimensional arrays are the primary data storage. Tiles are portions of arrays that kernels operate on. Kernels are functions that are executed in parallel by blocks. Blocks are subsets of the GPU; operations on tiles are parallelized across all the