mdspan
stdBLAS
mdspan | stdBLAS | |
---|---|---|
6 | 4 | |
375 | 103 | |
2.4% | 7.8% | |
8.5 | 3.3 | |
10 days ago | about 1 month ago | |
C++ | C++ | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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mdspan
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July 2022 ISO C++ committee virtual meeting report
Why not use https://github.com/kokkos/mdspan though ?
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C++ for scientific programming?
It can be the base of whatever *you* write via bindings generators like pybind11. In that sense, the answer to your question is "however you like". For actual simulation code, you'll see a lot more legacy Fortran and C. That said, with things like mdspan maybe being standardized (proposal), efforts towards a standard linear algebra library, and the existence of ubiquitous HPC frameworks already having been written in C++, I would say it's only a matter of time before C++ accounts for an even bigger share of all HPC code.
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[D] Deep Learning Framework for C++.
I'm aware of only two relevant projects myself, I don't know much, came to reddit kind of by chance. One of the multi-dimensional array libraries proposed for potential standardisation, and a gnu machine learning library that was discontinued which could be worked off of. There's probably a lot more out there, but don't get distracted from making something awesome :)
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Array template implementation
As u/IyeOnline already made the important points about VLAs and std::vector, I would just add that you may find std::mdspan to be a helpful data structure. You can allocate 1d memory and give it a 2d shape of k with nice 2d indexing, eg auto& elem = mymdspan(row, col);.
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C++23: Near The Finish Line
Kokkos mdspan
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Is there an OOP-wrapper library for cublas?
The good thing here is that it heavily relies on mdpsan that is a multidimensional view that handle shape and strides. And kokkos provide a C++14 compatible implementation with a complete CUDA support.
stdBLAS
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Which is the best way to work with matrices and linear algebra using c++?
You can have a look at https://github.com/kokkos/stdBLAS (it's an implementation of the proposed linear algebra extensions for future C++).
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C++23: Near The Finish Line
It has the same BSD license as Kokkos: https://github.com/kokkos/stdBLAS/blob/main/LICENSE
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Linear Algebra Library Reccomendations?
The BLAS and LAPACK have C interfaces, and they generally don't allocate memory unless you ask them. (Some BLAS implementations allocate internal storage for rearranging matrix data.) There are a few C++ BLAS wrappers; here is one of them.
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Is there an OOP-wrapper library for cublas?
The second one is stdBLAS by kokkos. It doesn't even use cuBLAS but it's a reference implementation of P1673. This paper describe what could be a blas API in the std. I would recommend trying to do something equivalent on your side.
What are some alternatives?
kokkos - Kokkos C++ Performance Portability Programming Ecosystem: The Programming Model - Parallel Execution and Memory Abstraction
circle - The compiler is available for download. Get it!
plf_hive - plf::hive is a fork of plf::colony to match the current C++ standards proposal.
kokkos-kernels - Kokkos C++ Performance Portability Programming Ecosystem: Math Kernels - Provides BLAS, Sparse BLAS and Graph Kernels
LEWG - Project planning for the C++ Library Evolution Working Group
plf_colony - An unordered C++ data container providing fast iteration/insertion/erasure while maintaining pointer/iterator validity to non-erased elements regardless of insertions/erasures. Provides higher-performance than std:: library containers for high-modification scenarios with unordered data.
deepdetect - Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
papers - ISO/IEC JTC1 SC22 WG21 paper scheduling and management
marian - Fast Neural Machine Translation in C++
stl-header-heft - Measures how parsing overweight the major STLs have become