gtensor
GTensor is a multi-dimensional array C++14 header-only library for hybrid GPU development. (by wdmapp)
CuTeLib
CUDA Template Library provides simple, typesafe, performant constructs for C++ CUDA projects (by anders-wind)
gtensor | CuTeLib | |
---|---|---|
1 | 1 | |
33 | 0 | |
- | - | |
8.9 | 7.8 | |
5 months ago | over 2 years ago | |
C++ | C++ | |
BSD 3-clause "New" or "Revised" License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
gtensor
Posts with mentions or reviews of gtensor.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-19.
-
Guidelines for using raw pointers in modern C++ and GPUs
If you want something like thrust:: device_vector, that also supports intel GPUs via SYCL (AMD has rocThrust but no intel equivalent AFAIK), checkout our project https://github.com/wdmapp/gtensor/. It also has multi-d arrays and lazy evaluation of complex array expressions. and experimental cross vendor gpu BLAS and FFT with a nicer interface.
CuTeLib
Posts with mentions or reviews of CuTeLib.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-19.
-
Guidelines for using raw pointers in modern C++ and GPUs
I am building a similarly library, including copy and streams and so on. Check it out https://github.com/anders-wind/CuTeLib
What are some alternatives?
When comparing gtensor and CuTeLib you can also consider the following projects:
mixbench - A GPU benchmark tool for evaluating GPUs and CPUs on mixed operational intensity kernels (CUDA, OpenCL, HIP, SYCL, OpenMP)
oneMKL - oneAPI Math Kernel Library (oneMKL) Interfaces
stlbm
taco - The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs
occa - Portable and vendor neutral framework for parallel programming on heterogeneous platforms.
mtensor - a c++/cuda template library for tensor lazy evaluation
blitz - Blitz++ Multi-Dimensional Array Library for C++
CHAI - Copy-hiding array abstraction to automatically migrate data between memory spaces
CppCoreGuidelines - The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++