xsimd VS DOKSparse

Compare xsimd vs DOKSparse and see what are their differences.

xsimd

C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, AVX512, NEON, SVE)) (by xtensor-stack)
simd-intrinsics Vectorization Simd CPP Avx Neon Sse Avx512 simd-instructions mathematical-functions c-plus-plus-11 Sve
Source Code
xsimd.readthedocs.io
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DOKSparse

sparse DOK tensors on GPU, pytorch (by DeMoriarty)
Cuda Pytorch Sparse
Source Code
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xsimd DOKSparse
3 2
2,043 2
1.4% -
8.7 4.2
1 day ago 10 months ago
C++ Cuda
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.
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xsimd

Posts with mentions or reviews of xsimd. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-03.

DOKSparse

Posts with mentions or reviews of DOKSparse. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-03.
  • GDlog: A GPU-Accelerated Deductive Engine
    16 projects | news.ycombinator.com | 3 Dec 2023
  • tensor.to_sparse() Memory Allocation
    1 project | /r/pytorch | 22 Apr 2023
    If using sparse tensors is a must, you can look into DOK sparse format, which is supported for 2d matrices in scipy. it kinda allows you to access any element of the sparse tensor in constant time, which makes it possible to create your tensor directly in sparse format, skipping the need to create a dense numpy array first. In case you need a GPU version of this, I have a library that implements sparse dok tensor in pytorch and cuda. currently it's GPU only.

What are some alternatives?

When comparing xsimd and DOKSparse you can also consider the following projects:

highway - Performance-portable, length-agnostic SIMD with runtime dispatch

cub - [ARCHIVED] Cooperative primitives for CUDA C++. See https://github.com/NVIDIA/cccl

Vc - SIMD Vector Classes for C++

MegBA - MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment

libsimdpp - Portable header-only C++ low level SIMD library

CUDA-Guide - CUDA Guide

nsimd - Agenium Scale vectorization library for CPUs and GPUs

cuhnsw - CUDA implementation of Hierarchical Navigable Small World Graph algorithm

FastDifferentialCoding - Fast differential coding functions (using SIMD instructions)

TorchPQ - Approximate nearest neighbor search with product quantization on GPU in pytorch and cuda

optuna - A hyperparameter optimization framework

instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more

xsimd vs highway DOKSparse vs cub xsimd vs Vc DOKSparse vs MegBA xsimd vs libsimdpp DOKSparse vs CUDA-Guide xsimd vs nsimd DOKSparse vs cuhnsw xsimd vs FastDifferentialCoding DOKSparse vs TorchPQ xsimd vs optuna DOKSparse vs instant-ngp