ArrayFire
A general-purpose tensor library for parallel computing across CPUs, GPUs, and hardware accelerators.
What is ArrayFire?
ArrayFire is a general-purpose tensor library that simplifies parallel computing across CPUs, GPUs, and other hardware accelerators. It provides a high-level abstraction via `af::array` objects, automatically optimizing operations for performance while maintaining cross-platform compatibility. The library addresses the complexity of writing efficient, portable code for diverse parallel architectures.
Developers and researchers in scientific computing, machine learning, computer vision, and signal processing who need high-performance tensor operations across multiple hardware platforms. It's particularly valuable for those seeking to write portable code without deep expertise in low-level GPU or accelerator programming.
ArrayFire offers a unified, easy-to-use API that abstracts hardware details, enabling developers to write once and run efficiently everywhere—from mobile phones to supercomputers. Its rigorous performance tuning, extensive function library, and cross-platform support make it a robust choice for accelerating technical computing workloads.
Overview
ArrayFire: a general purpose GPU library.
Use Cases
Best For
- Accelerating machine learning and deep learning workloads on GPUs
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