ginkgo VS stdgpu

Compare ginkgo vs stdgpu and see what are their differences.

ginkgo

Numerical linear algebra software package (by ginkgo-project)
Linear Algebra HPC gpu-computing Cuda krylov-methods preconditioning sparse-linear-systems spmv Hip Oneapi Dpcpp Openmp
Source Code
ginkgo-project.github.io
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stdgpu

stdgpu: Efficient STL-like Data Structures on the GPU (by stotko)
Concurrency GPU gpu-computing gpu-acceleration Gpgpu Data structures Stl stl-like stl-containers CPP Cpp17 Cpp20 modern-cpp Cuda Openmp Rocm Hip
Source Code
stotko.github.io
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ginkgo stdgpu
2 -
375 1,086
1.9% -
9.8 7.1
3 days ago about 1 month ago
C++ C++
BSD 3-clause "New" or "Revised" License Apache License 2.0
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.

ginkgo

Posts with mentions or reviews of ginkgo. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-26.
  • AMD HIP + Cuda in same program
    3 projects | /r/CUDA | 26 Aug 2022
  • Incorporating abidiff into CI?
    1 project | /r/cpp | 28 Nov 2021
    I had exactly the same thought after watching the video (though I wanted to do this for a while anyways) and did exactly that :) I found it interesting to see how different changes impact the ABI of our library, even though we don't promise ABI compatibility or anything. We add the head of the diff part to a PR comment, and store everything else as a build artifact. See the corresponding PR here: https://github.com/ginkgo-project/ginkgo/pull/922

stdgpu

Posts with mentions or reviews of stdgpu. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning stdgpu yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing ginkgo and stdgpu you can also consider the following projects:

HIP - HIP: C++ Heterogeneous-Compute Interface for Portability

ArrayFire - ArrayFire: a general purpose GPU library.

alpaka - Abstraction Library for Parallel Kernel Acceleration :llama:

libcudacxx - [ARCHIVED] The C++ Standard Library for your entire system. See https://github.com/NVIDIA/cccl

arbor - The Arbor multi-compartment neural network simulation library.

alpaka - The project alpaka has moved to https://github.com/alpaka-group/alpaka

MatX - An efficient C++17 GPU numerical computing library with Python-like syntax

moodycamel - A fast multi-producer, multi-consumer lock-free concurrent queue for C++11

AdaptiveCpp - Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!

Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl

Halide - a language for fast, portable data-parallel computation

Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System

ginkgo vs HIP stdgpu vs ArrayFire ginkgo vs alpaka stdgpu vs libcudacxx ginkgo vs arbor stdgpu vs alpaka ginkgo vs MatX stdgpu vs moodycamel ginkgo vs AdaptiveCpp stdgpu vs Thrust ginkgo vs Halide stdgpu vs Taskflow