arrayfire/arrayfire

Health v2 · maintenance only

Adoption not part of the score

4902 stars · 558 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

ArrayFire is a general-purpose tensor/numerical computing library for C, C++, and Python that accelerates array operations on GPUs (CUDA, OpenCL, oneAPI) and CPUs. It provides hundreds of functions for linear algebra, image processing, computer vision, signal processing, statistics, and machine learning behind a simple af::array abstraction.

Use cases

• accelerate numerical array computations on GPU
• run linear algebra on CUDA or OpenCL devices
• GPU-accelerated image processing in C++
• port scientific computing code across CPU and GPU backends
• build high-performance machine learning primitives
• write cross-platform GPGPU code without writing kernels

When to choose

• you need GPU-accelerated tensor math in C/C++ without writing CUDA kernels
• you want one API that targets CUDA, OpenCL, oneAPI, and CPU backends
• you need fast linear algebra, FFT, image, or signal processing routines
• you are doing HPC or scientific computing on NVIDIA, AMD, or Intel GPUs

When to avoid

• you need a Python-first deep learning framework with autograd and neural network layers
• you want a modern actively evolving ecosystem - development activity has slowed
• your workload is small and CPU-only, where GPU overhead outweighs gains
• you need distributed multi-node computing rather than single-device acceleration

Facets

library · maturity stable

machine-learning image-processing computer-vision gpu-computing math data-science gpu-computing machine-learning image-processing computer-vision performance cpp c windows cross-platform tensor-library gpgpu opencl oneapi linear-algebra signal-processing hpc scientific-computing algorithms cuda linux macos gpu

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