Fastor
ArrayFire
Fastor | ArrayFire | |
---|---|---|
5 | 6 | |
706 | 4,413 | |
- | 0.7% | |
4.3 | 7.1 | |
22 days ago | about 1 month ago | |
C++ | C++ | |
MIT License | 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.
Fastor
-
Standard way of doing maths with arrays?
I'm going to throw in a recommendation for Fastor. It is generally faster than other libraries, is very lightweight, and has a pretty modern syntax.
- LibRapid -- High Performance Arrays for C++
-
From Julia to C++ Struggle
There are C++ libraries that deal with linear algebra and tensors that are able to produce fully vectorized code without requiring you to mess around with SIMD intrinsics. See, for instance, fastor, blaze, eigen and the huge Trillinos set of packages. C++ is very widely used when it comes to scientific HPC applications. All you need to do is google search or better yet, join r/cpp and r/cpp_questions and start asking away for the things you need. The C++ community is very welcoming and full of experts that will be able to help you.
-
Use of BLAS vs direct SIMD for linear algebra library operations?
Picking what size you are targeting is really important, though. Could the matrices you are working with realistically be bigger than say 32x32? BLAS is good for big matrices. It's not as great for small matrices. Eigen or Fastor will do better for these smaller problems. And for various common operations on sizes 2, 3, and 4, hand coded graphics-oriented libraries might outperform those.
-
Scientific computing in Cpp
Tensorflow, Machine learning: https://www.tensorflow.org/ Fastor, A tensor library: https://github.com/romeric/Fastor GNU Scientific Library(GSL): https://www.gnu.org/software/gsl/ Boost. FEniCS, A finite element library: https://fenicsproject.org/ Intel MKL, a BLAS+LAPACK+other goodies library: https://software.intel.com/content/www/us/en/develop/tools/math-kernel-library.html SuiteSparse, A sparse linear algebra library: http://faculty.cse.tamu.edu/davis/suitesparse.html Sundials, Nonlinear solvers: https://computing.llnl.gov/projects/sundials
ArrayFire
-
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.
-
seeking C++ library for neural net inference, with cross platform GPU support
What about Arrayfire. https://github.com/arrayfire/arrayfire
-
[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
-
[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
-
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?
xtensor - C++ tensors with broadcasting and lazy computing
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
DirectXMath - DirectXMath is an all inline SIMD C++ linear algebra library for use in games and graphics apps
Boost.Compute - A C++ GPU Computing Library for OpenCL
dynarray - A header-only library, VLA for C++ (≥C++14). Extended version of std::experimental::dynarray
VexCL - VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
ITensors.jl - A Julia library for efficient tensor computations and tensor network calculations
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
sundials - Official development repository for SUNDIALS - a SUite of Nonlinear and DIfferential/ALgebraic equation Solvers. Pull requests are welcome for bug fixes and minor changes.
CUB - THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
SPTK - A suite of speech signal processing tools
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System