oneMKL
kokkos-kernels
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| oneMKL | kokkos-kernels | |
|---|---|---|
| 2 | 1 | |
| 564 | 276 | |
| 3.5% | 3.3% | |
| 8.5 | 9.1 | |
| 12 days ago | about 23 hours ago | |
| C++ | C++ | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
oneMKL
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Stable Diffusion on AMD RDNA™ 3 Architecture
I think there's already been work done to just use intel MKL on any device: https://github.com/oneapi-src/oneMKL
- Developing in heterogeneous environment with the best HPC libraries
kokkos-kernels
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Is there an OOP-wrapper library for cublas?
It’s a work in progress, but Kokkos and the associated Kokkos Kernels are probably the closest thing to what you’re asking for.
What are some alternatives?
oneDNN - oneAPI Deep Neural Network Library (oneDNN)
mdspan - Reference implementation of mdspan targeting C++23
peakperf - Achieve peak performance on x86 CPUs and NVIDIA GPUs
rocBLAS - Next generation BLAS implementation for ROCm platform
nekRS - our next generation fast and scalable CFD code
kronmult993 - CPU and GPU implementations of kronmult.
ArrayFire - ArrayFire: a general purpose GPU library.
cu - package cu provides an idiomatic interface to the CUDA Driver API.
LSQR-CUDA - This is a LSQR-CUDA implementation written by Lawrence Ayers under the supervision of Stefan Guthe of the GRIS institute at the Technische Universität Darmstadt. The LSQR library was authored Chris Paige and Michael Saunders.
stdBLAS - Reference Implementation for stdBLAS
monolish - monolish: MONOlithic LInear equation Solvers for Highly-parallel architecture
kokkos - Kokkos C++ Performance Portability Programming Ecosystem: The Programming Model - Parallel Execution and Memory Abstraction