@@ -50,3 +50,11 @@ TODO: compare the implementation of multidimensional arrays
...
@@ -50,3 +50,11 @@ TODO: compare the implementation of multidimensional arrays
-[RAJA](https://github.com/LLNL/RAJA) - No memory management, views are initialized with a raw pointer, index permutations are initialized at runtime, only dynamic dimensions.
-[RAJA](https://github.com/LLNL/RAJA) - No memory management, views are initialized with a raw pointer, index permutations are initialized at runtime, only dynamic dimensions.
-[Kokkos](https://github.com/kokkos/kokkos) - Configurable layout and default selection based on the memory/execution space, but only AoS and SoA are considered, even for `N > 2`. For parallel work there is only one leading dimension - it does not map to 2D or 3D CUDA grids.
-[Kokkos](https://github.com/kokkos/kokkos) - Configurable layout and default selection based on the memory/execution space, but only AoS and SoA are considered, even for `N > 2`. For parallel work there is only one leading dimension - it does not map to 2D or 3D CUDA grids.
-[CUV](https://github.com/deeplearningais/CUV) - Assumption that "everything is an n-dimensional array" (like Matlab), CPU and GPU support, column-major or row-major, integration with Python and Numpy.
-[CUV](https://github.com/deeplearningais/CUV) - Assumption that "everything is an n-dimensional array" (like Matlab), CPU and GPU support, column-major or row-major, integration with Python and Numpy.
## Unstructured meshes
TNL has a unique data structure for the representation of unstructured meshes in high-performance
simulation software, including GPU accelerators. See the original paper [Configurable open-source
data structure for distributed conforming unstructured homogeneous meshes with GPU support](
https://doi.org/10.1145/3536164) for the design choices an benchmarks comparing it to the
