ginkgo VS MatX

Compare ginkgo vs MatX 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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MatX

An efficient C++17 GPU numerical computing library with Python-like syntax (by NVIDIA)
Cuda GPU Gpgpu HPC gpu-computing
Source Code
nvidia.github.io
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ginkgo MatX
2 7
375 1,117
1.9% 1.2%
9.8 9.1
2 days ago 6 days ago
C++ C++
BSD 3-clause "New" or "Revised" License BSD 3-clause "New" or "Revised" 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.

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

MatX

Posts with mentions or reviews of MatX. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-10-03.

What are some alternatives?

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

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

ParallelReductionsBenchmark - Thrust, CUB, TBB, AVX2, CUDA, OpenCL, OpenMP, SyCL - all it takes to sum a lot of numbers fast!

alpaka - Abstraction Library for Parallel Kernel Acceleration :llama:

GPU-accelerated-guppy-basecalling - GPU-accelerated guppy basecalling and demultiplexing on Linux

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

cuda_memtest - Fork of CUDA GPU memtest :eyeglasses:

stdgpu - stdgpu: Efficient STL-like Data Structures on the GPU

Trilinos - Primary repository for the Trilinos Project

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!

conan - Conan - The open-source C and C++ package manager

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

conan-center-index - Recipes for the ConanCenter repository

ginkgo vs HIP MatX vs ParallelReductionsBenchmark ginkgo vs alpaka MatX vs GPU-accelerated-guppy-basecalling ginkgo vs arbor MatX vs cuda_memtest ginkgo vs stdgpu MatX vs Trilinos ginkgo vs AdaptiveCpp MatX vs conan ginkgo vs Halide MatX vs conan-center-index