If you're reading our blog, BLAS and FFT libraries likely form an important basis for your work. For instance, BLAS and FFT libraries are used in some of ArrayFire's higher-level functions for linear algebra, signal processing, and image processing. Today, OpenCL is getting a big boost in BLAS and FFT library availability. AMD has announced a bold and generous move to contribute back to the OpenCL community by open sourcing its APPML BLAS and FFT OpenCL libraries. At AccelerEyes, we have used AMD's OpenCL libraries in the past within our higher-level ArrayFire library. These libraries are the best BLAS and FFT OpenCL libraries available anywhere. We are thrilled to now join AMD and the open source community in maintaining and improving these ...
This is the first in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the getting_started/ directory. Hello World Of course we start with the classic "Hello World" example, which walks you through the basics of using the ArrayFire library. Running this example will print out system and device information, as well as perform some basic matrix operations. This is a good place to get familiar with the basic data container for ArrayFire - the array.
ArrayFire v1.9 (build XXXXXXX) by AccelerEyes (64-bit Linux)
CUDA toolkit 5.0, driver 304.54
GPU0 Quadro 6000, 6144 MB, Compute 2.0 (single,double)
Display Device: GPU0 Quadro 6000
Memory Usage: 5549 MB free (6144 MB total)...
Convolve In this example we show you how to perform a basic image convolution, as well as how ...
In case you missed it, we recently held a webinar on the Jacket GPU Computing Engine for MATLAB® and its applications to Optics and Photonics on Aug 1. From beam propagation methods to lens design, optics engineers are enjoying the benefit of GPU computing with Jacket to accelerate MATLAB® codes. This was part of a free series of webinars that help you learn about ArrayFire (for C/C++/Fortran/Python) and Jacket (for use with MATLAB®). Anyone can attend these webinars, for they are absolutely free and open for anyone to attend and interact with AccelerEyes engineers. Learn more at http://www.accelereyes.com/webinars. Jacket allows you to envision really fast applications for GPU computing, and the team at AccelerEyes recently helped Northrop Grumman Corporation achieve ...
Researchers at the University of Quebec have developed high-performance gene predictors using Jacket to accelerated their MATLAB® code. This work has been published in BMC Research Notes and is freely available here. Computerized approaches to studying the human genome are challenged by the exploding amount of data, which doubles roughly every 6 months. In order to deal with this burgeoning datasets, demands for faster processing power continue to arise. This work focuses on predicting genes using frequency analysis with FFTs and with an equivalent technique known as Goertzel’s algorithm. In these applications, the emphasis of this paper is to propose tools to geneticists and molecular biologists for the prediction or identification of new genes using existing complementary strategies. The criteria for these ...