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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.jl Brings NVIDIA CUDA Tile-Based Programming to Julia | NVIDIA Technical BlogcuTile 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!
CUDA Tile Programming Now Available for BASIC! | NVIDIA Technical BlogBASIC 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 Programming Now Available for BASIC! | NVIDIA Technical BlogCUDA 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
Develop High-Performance GPU Kernels in C++ with NVIDIA CUDA Tile | NVIDIA Technical Blog… CUDA Tile IR Virtual Instruction Set for Tile Programming: Enables native programming of GPUs within the structured high-performance context of the tile programming model cuTile Python Python-Native, Tiled Kernel Development: Seamless high-level Python expression of the CUDA Tile programming model … …
… Launch Julia, and press from the REPL to enter the integrated package manager to install cuTile.jl: pkg add cuTile pkg if you want, run the test suite test cuTile The GitHub contains a full list of supported operations and detailed documentation on how cuTile.jl differs from both cuTile Python and … …
… Consider a matrix multiply GEMM kernel in BASIC, shown below: 10 REM GEMM: C M,N = A M,K B K,N 15 INPUT M, N, K, A , B 20 DIM A M, K , B K, N , C M, N 30 TILE A 128, 32 , B 32, 128 , C 128, 128 , ACC 128, 128 40 LET TILEM = INT BID / INT N / 128 50 LET TILEN = BID MOD INT N / 128 60 LET ACC = 0.0 7… …
… 10 REM GEMM: C M,N = A M,K B K,N 15 INPUT M, N, K, A , B 20 DIM A M, K , B K, N , C M, N 30 TILE A 128, 32 , B 32, 128 , C 128, 128 , ACC 128, 128 40 LET TILEM = INT BID / INT N / 128 50 LET TILEN = BID MOD INT N / 128 60 LET ACC = 0.0 70 FOR KI = 0 TO INT K / 32 - 1 80 LET ACC = MMA A TILEM, KI , … …
… Notice the applied optimizations and reduced verbosity. include include include "cuda tile.h" tile global void vectorAdd float restrict a, float restrict b, float restrict out, size t n { namespace ct = cuda::tiles; using namespace ct::literals; a = ct::assume aligned a, 16 ic ; b = ct::assume alig… …
… Verify Tile IR compilation After building, verify the installation by running the vector addition tutorial and confirming that the Tile IR backend is being used: Navigate to the tutorial directory cd python/tutorials Run the vector addition example with Tile IR enabled export ENABLE TILE=1 python 0… …
… """ gpu capability = torch.cuda.get device capability if gpu capability in 12, 0 , 12, 1 : RTX 50 series sm120, sm121 yield SimpleNamespace TILE M=64, TILE N=64, num ctas=1, occupancy=2 else: B200/GB200 sm100 - Try multiple tile sizes Autotuner will discover: - 64x64 is best for short sequences 102… …
… Release of CUDA Tile C++ With the release of CUDA 13.3, CUDA Tile support is extended to C++, enabling the large existing C++ codebase and developer base to create highly-optimized GPU tile kernels. …
… The following pip install command will install cuTile Python and pull in all the needed dependencies without requiring a separate system-wide installation of the CUDA Toolkit. pip install cuda-tile tileiras Core enhancements Core enhancements in CUDA 13.2 are detailed in this section. memcpy with a… …
… CUDA Tile C++ 벡터 덧셈 예제 SIMT용 CUDA C++에 익숙한 개발자라면 표준적인 벡터 덧셈 커널을 접해 봤을 것입니다. 데이터가 이미 GPU에 있다고 가정하면, CUDA SIMT의 벡터 덧셈 커널은 두 벡터를 받아 요소별로 더해 세 번째 벡터를 만듭니다. 작성하기 가장 간단한 CUDA 커널 가운데 하나로, 그 모습은 다음과 같습니다. global void vecAdd float A, float B, float C, int vectorLength { / calculate my thread index / int wor… …