We are thrilled to announce the official release ArrayFire v2.0, our biggest and best product ever!
ArrayFire v2.0 adds full commercial support for OpenCL devices including all AMD APUs and AMD FireProTM graphics, CUDA GPUs from NVIDIA, and other OpenCL devices from Imagination, Freescale, ARM, Intel, and Apple.
ArrayFire is a CUDA and OpenCL library 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.
Announcing ArrayFire for OpenCL
- Support for all of ArrayFire’s function library (with a few exceptions)
- Same API as ArrayFire for CUDA enabling seamless interoperability
- Just-In-Time (JIT) compilation of kernels for top performance
- Specific tuning for Intel Xeon Phi coprocessors
- Accelerated algorithms for image processing, signal processing, visualization, and more
Updates to ArrayFire for CUDA
- New signal and image processing functions
- Faster transpose and matrix multiplication
- Enhanced debugging support for GDB and Visual Studio
- Better examples and documentation
Maybe the upgrade you wanted isn’t mentioned here. Don’t despair! Check out our release notes, as well as our new and improved documentation, for a complete list of the many ArrayFire v2.0 enhancements available.
To learn more about which licensing option would be the best for your needs, visit our ArrayFire licensing page.
Just getting started with GPU computing? Need an extra hand on a project? Tap into our deep parallel computing expertise and vast code base by setting up a free technical consultation today.
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Stay tuned for more exciting news from the ArrayFire gang coming soon…
Comments 1
Do you have any legacy tools to add Arrayfire basics to OpenCL equipped MATLAB setups? I know Jacket has been acquired by Mathworks, but their support for OpenCL within MATLAB appears to be string-bikini-like in its coverage.