In CUDA programs, you can place only the GPU kernels in .cu files while keeping the rest of your code...
In CUDA programs, you can place only the GPU kernels in .cu files while keeping the rest of your code in .cpp files.
This organization confines CUDA-specific code to a limited set of files, making the overall program easier to maintain and understand.
File Structure
This example uses the following two files:
kernel.cu
Developers can now use NVIDIA CUDA Tile programming within large existing C++ GPU codebases to develop highly optimized GPU…
A Coding Implementation to Master GPU Computing with CuPy, Custom CUDA Kernels, Streams, Sparse Matrices, and Profiling
NVIDIA CUDA Tile (cuTile) is a tile-based programming model that enables developers to write GPU kernels in terms of tile-level…
Build tile-based GPU kernels in NVIDIA cuTile Python for vector addition, matrix addition, and matrix multiplication, with a…
Coding guide to NVIDIA tile-based GPU programming, using cuTile and Triton kernels to build vector add, softmax, matmul, and…
There are many ways to optimize code for GPUs. In this post, you’ll learn how kernel fusion can improve memory bandwidth and…
