CHAI VS CuTeLib

Compare CHAI vs CuTeLib and see what are their differences.

CHAI

Copy-hiding array abstraction to automatically migrate data between memory spaces (by LLNL)

CuTeLib

CUDA Template Library provides simple, typesafe, performant constructs for C++ CUDA projects (by anders-wind)
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CHAI CuTeLib
1 1
104 0
1.9% -
7.3 7.8
7 days 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.

CHAI

Posts with mentions or reviews of CHAI. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-03-19.

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.

What are some alternatives?

When comparing CHAI and CuTeLib you can also consider the following projects:

RAJA - RAJA Performance Portability Layer (C++)

oneMKL - oneAPI Math Kernel Library (oneMKL) Interfaces

Umpire - An application-focused API for memory management on NUMA & GPU architectures

taco - The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs

ArrayFire - ArrayFire: a general purpose GPU library.

stlbm

mtensor - a c++/cuda template library for tensor lazy evaluation

blitz - Blitz++ Multi-Dimensional Array Library for C++

gtensor - GTensor is a multi-dimensional array C++14 header-only library for hybrid GPU development.

CppCoreGuidelines - The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++