ArrayFire v3.1 Official Release

ScottArrayFire 1 Comment

Today we are pleased to announce the release of ArrayFire v3.1. This new version features new functional support with a focus on computer vision and machine learning functions added to the library, along with new support for Array and Data Handling functions. This release also includes support for CUDA 7.5. A complete list of ArrayFire v3.1 updates and new features can be found in the product Release Notes.

With over 8 years of continuous development, the open source ArrayFire library is the top CUDA and OpenCL software library. ArrayFire supports CUDA-capable GPUs, OpenCL devices, and other accelerators. With its easy-to-use API, this hardware-neutral software library is designed for maximum speed without the hassle of writing time-consuming CUDA and OpenCL device code. With ArrayFire’s library functions, developers can maximize productivity and performance. Each of ArrayFire’s functions has been hand-tuned by CUDA and OpenCL experts.

New functions added in the following categories:

  • Computer Vision
    • SIFT feature descriptor
    • Harris corner detector
    • SUSAN corner detector
  • Machine Learning
    • Image wrap and unwrap (used in Convolutional Networks)
    • Real to Complex FFTs (used in Convolutional Networks)
    • Nearest Neighbor Search
  • Other Functions
    • Singular Value Decomposition
    • Select and Replace
    • Inplace FFTs

Availability

Visit ArrayFire’s website to download ArrayFire v3.1 Installers or go to our GitHub account and build the source code. The ArrayFire software library operates under the BSD 3-Clause License which enables unencumbered deployment and portability of ArrayFire for all uses, including commercially.

Dedicated support and coding services

ArrayFire offers dedicated support packages for ArrayFire users.

ArrayFire serves many clients through consulting and coding services, algorithm development, porting code, and training courses for developers.

Comments 1

  1. This is great.

    Could you publish some performance tests to see how performance has improved?
    Better do it on Broadwell / Skylake GPU’s.

    Thank You.

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