flamethrower VS ArrayFire

Compare flamethrower vs ArrayFire and see what are their differences.


a DNS performance and functional testing utility supporting UDP, TCP, DoT and DoH (by @ns1labs) (by DNS-OARC)
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flamethrower ArrayFire
3 6
293 4,227
0.3% 0.4%
0.0 6.6
5 months ago 12 days ago
C++ C++
Apache License 2.0 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.


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

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


Posts with mentions or reviews of ArrayFire. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-27.
  • Learn WebGPU
    9 projects | news.ycombinator.com | 27 Apr 2023
    Loads of people have stated why easy GPU interfaces are difficult to create, but we solve many difficult things all the time.

    Ultimately I think CPUs are just satisfactory for the vast vast majority of workloads. Servers rarely come with any GPUs to speak of. The ecosystem around GPUs is unattractive. CPUs have SIMD instructions that can help. There are so many reasons not to use GPUs. By the time anyone seriously considers using GPUs they're, in my imagination, typically seriously starved for performance, and looking to control as much of the execution details as possible. GPU programmers don't want an automagic solution.

    So I think the demand for easy GPU interfaces is just very weak, and therefore no effort has taken off. The amount of work needed to make it as easy to use as CPUs is massive, and the only reason anyone would even attempt to take this on is to lock you in to expensive hardware (see CUDA).

    For a practical suggestion, have you taken a look at https://arrayfire.com/ ? It can run on both CUDA and OpenCL, and it has C++, Rust and Python bindings.

  • [D] Deep Learning Framework for C++.
    7 projects | /r/MachineLearning | 12 Jun 2022
    Low-overhead — not our goal, but Flashlight is on par with or outperforming most other ML/DL frameworks with its ArrayFire reference tensor implementation, especially on nonstandard setups where framework overhead matters
  • [D] Neural Networks using a generic GPU framework
    2 projects | /r/MachineLearning | 4 Jan 2022
    Looking for frameworks with Julia + OpenCL I found array fire. It seems quite good, bonus points for rust bindings. I will keep looking for more, Julia completely fell off my radar.

What are some alternatives?

When comparing flamethrower and ArrayFire you can also consider the following projects:

Thrust - The C++ parallel algorithms library.

Boost.Compute - A C++ GPU Computing Library for OpenCL

VexCL - VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP


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

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

moderngpu - Patterns and behaviors for GPU computing

HPX - The C++ Standard Library for Parallelism and Concurrency

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

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

RaftLib - The RaftLib C++ library, streaming/dataflow concurrency via C++ iostream-like operators

C++ Actor Framework - An Open Source Implementation of the Actor Model in C++