ArrayFire: A Portable Open-Source Accelerated Computing Library by Pavan Yalamanchilli.
From the post:
The ArrayFire library is a high-performance software library with a focus on portability and productivity. It supports highly tuned, GPU-accelerated algorithms using an easy-to-use API. ArrayFire wraps GPU memory into a simple “array” object, enabling developers to process vectors, matrices, and volumes on the GPU using high-level routines, without having to get involved with device kernel code.
ArrayFire Capabilities
ArrayFire is an Fortran. ArrayFire has a range of functionality, including
- standard math functions;
- image processing functions;
- accelerated core algorithms: reduction, scan, sort etc.;
- statistics;
- set operations;
- BLAS linear algebra routines;
- FFT algorithms;
- and more.
ArrayFire has three back ends to enable portability across many platforms: CUDA, OpenCL and CPU. It even works on embedded platforms like NVIDIA’s Jetson TK1.
In a past post about ArrayFire we demonstrated the ArrayFire capabilities and how you can increase your productivity by using ArrayFire. In this post I will tell you how you can use ArrayFire to exploit various kind of parallelism on NVIDIA GPUs.
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Just in case you get a box full of GPUs during the holidays and/or need better performance from ones you already have!
Enjoy!