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ArrayFire Alternatives
Similar projects and alternatives to ArrayFire
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Thrust
[ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
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InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
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CUB
THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
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Pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
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Taskflow
A General-purpose Parallel and Heterogeneous Task Programming System
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Onboard AI
Learn any GitHub repo in 59 seconds. Onboard AI learns any GitHub repo in minutes and lets you chat with it to locate functionality, understand different parts, and generate new code. Use it for free at www.getonboard.dev.
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futhark
:boom::computer::boom: A data-parallel functional programming language
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moodycamel
A fast multi-producer, multi-consumer lock-free concurrent queue for C++11
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tfjs
A WebGL accelerated JavaScript library for training and deploying ML models.
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mmaction2
OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
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angle
A conformant OpenGL ES implementation for Windows, Mac, Linux, iOS and Android.
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deepdetect
Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better ArrayFire alternative or higher similarity.
ArrayFire reviews and mentions
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.
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Learn WebGPU
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.
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[D] Deep Learning Framework for C++.
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
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[D] Neural Networks using a generic GPU framework
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.
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www.saashub.com | 8 Dec 2023
Stats
Basic ArrayFire repo stats
6
4,280
0.0
about 1 month ago
arrayfire/arrayfire is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.
The primary programming language of ArrayFire is C++.
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