oneMKL
ArrayFire
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| oneMKL | ArrayFire | |
|---|---|---|
| 2 | 6 | |
| 565 | 4,404 | |
| 3.5% | 1.0% | |
| 8.5 | 7.8 | |
| 7 days ago | 21 days ago | |
| C++ | C++ | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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
ArrayFire
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Learn WebGPU
Loads of people have stated why easy GPU interfaces are difficult to create, but we solve many difficult things all the time.
Ultimately I think CPUs are just satisfactory for the vast vast majority of workloads. Servers rarely come with any GPUs to speak of. The ecosystem around GPUs is unattractive. CPUs have SIMD instructions that can help. There are so many reasons not to use GPUs. By the time anyone seriously considers using GPUs they're, in my imagination, typically seriously starved for performance, and looking to control as much of the execution details as possible. GPU programmers don't want an automagic solution.
So I think the demand for easy GPU interfaces is just very weak, and therefore no effort has taken off. The amount of work needed to make it as easy to use as CPUs is massive, and the only reason anyone would even attempt to take this on is to lock you in to expensive hardware (see CUDA).
For a practical suggestion, have you taken a look at https://arrayfire.com/ ? It can run on both CUDA and OpenCL, and it has C++, Rust and Python bindings.
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seeking C++ library for neural net inference, with cross platform GPU support
What about Arrayfire. https://github.com/arrayfire/arrayfire
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[D] Deep Learning Framework for C++.
Low-overhead — not our goal, but Flashlight is on par with or outperforming most other ML/DL frameworks with its ArrayFire reference tensor implementation, especially on nonstandard setups where framework overhead matters
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[D] Neural Networks using a generic GPU framework
Looking for frameworks with Julia + OpenCL I found array fire. It seems quite good, bonus points for rust bindings. I will keep looking for more, Julia completely fell off my radar.
- Windows 11 va bloquer les bidouilles qui facilitent l'emploi d'un navigateur alternatif à Edge
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Arrayfire progressive performance decline?
Your Problem may be the lazy evaluation, see this issue: https://github.com/arrayfire/arrayfire/issues/1709
What are some alternatives?
oneDNN - oneAPI Deep Neural Network Library (oneDNN)
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
kokkos-kernels - Kokkos C++ Performance Portability Programming Ecosystem: Math Kernels - Provides BLAS, Sparse BLAS and Graph Kernels
Boost.Compute - A C++ GPU Computing Library for OpenCL
peakperf - Achieve peak performance on x86 CPUs and NVIDIA GPUs
VexCL - VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
nekRS - our next generation fast and scalable CFD code
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
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.
CUB - THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
monolish - monolish: MONOlithic LInear equation Solvers for Highly-parallel architecture
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System