ArrayFire v3.5.1 Release

miguel@arrayfire.com Announcements, ArrayFire 1 Comment

We are excited to announce ArrayFire v3.5.1! This release focuses on fixing bugs and improving performance. Here are the improvements we think are most important: Performance improvements We've improved element-wise operation performance for the CPU backend. The af::regions() function has been modified to leverage texture memory, improving its performance. Our JIT engine has been further optimized to boost performance. Bug fixes We've squashed a long standing bug in the CUDA backend responsible for breaking whenever the second, third, or fourth dimensions were large enough to exceed limits imposed by the CUDA runtime. The previous implementation of af::mean() suffered from overflows when the summation of the values lied outside the range of the backing data type. New kernels for each of ...

ArrayFire v3.5 Official Release

Umar Announcements, ArrayFire, CUDA, Open Source, OpenCL 1 Comment

Today we are pleased to announce the release of ArrayFire v3.5, our open source library of parallel computing functions supporting CUDA, OpenCL, and CPU devices. This new version of ArrayFire improves features and performance for applications in machine learning, computer vision, signal processing, statistics, finance, and more. This release focuses on thread-safety, support for simple sparse-dense arithmetic operations, canny edge detector function, and a genetic algorithm example. A complete list of ArrayFire v3.5 updates and new features are found in the product Release Notes. Thread Safety ArrayFire now supports threading programming models. This is not intended to improve the performance since most of the parallelism is happening on the device, but it does allow you to use multiple devices in ...

Contributors: A big benefit to open source software

Brian Kloppenborg ArrayFire, Open Source 4 Comments

Making the decision to open source your software is not an easy process. Indeed, here at ArrayFire our choice to release ArrayFire under the open source, commercially friendly, BSD 3-Clause License came only after many hours of consideration and philosophical discussion (e.g. see our CEO's blog on these topics). Thus far this decision has proven to be strictly beneficial to our company. The impact of third-party contributions Although ArrayFire is primarily developed by our engineers, there are several contributions from other developers. Therefore we feel particularly compelled to elucidate how these contributions have improved the ArrayFire ecosystem. Packaging for Linux and OSX One of the best parts of open source distribution is that your code can be packaged and distributed for ...

ArrayFire v3.0 is here!

Aaron Announcements, ArrayFire, CUDA, Open Source, OpenCL 5 Comments

Today we are pleased to announce the release of ArrayFire v3.0. This new version features major changes to ArrayFire’s visualization library, a new CPU backend, and dense linear algebra for OpenCL devices. It also includes improvements across the board for ArrayFire’s OpenCL backend. A complete list ArrayFire v3.0 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 ...

Conway's Game of Life using ArrayFire

Shehzan ArrayFire, CUDA, Image Processing, Open Source, OpenGL 4 Comments

Conway's Game of Life is a popular zero player cellular automaton devised by the John Horton Conway in 1970. The game makes for a fun evolution as the player sets the initial condition and then observes the evolution of the game. Each cell has 2 states: live or dead. There are 4 simple rules that determine this: Any live cell with fewer than two live neighbours dies, as if caused by under-population. Any live cell with two or three live neighbours lives on to the next generation. Any live cell with more than three live neighbours dies, as if by overcrowding. Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction. From a programmer's ...

ArrayFire is Now Open Source

Scott Announcements, ArrayFire, Open Source 7 Comments

Yes, you read that right! ArrayFire is open source—it’s all there and it’s all free. This is big, and you and the rest of the parallel computing community are going to love it! You can download our pre-compiled binary installers which are optimized for a wide variety of systems or you can get a copy of the ArrayFire source code from our GitHub page. ArrayFire is being released under the BSD 3-Clause License, which will enable unencumbered deployment and portability of ArrayFire for commercial use. So go check it out! We welcome your feedback and look forward to your future contributions to ArrayFire. The move to open source isn't our only news—we've also made ArrayFire better than ever. Check out our recent ...

Accelerating Java using ArrayFire, CUDA and OpenCL

Pavan ArrayFire, JAVA 3 Comments

We have previously mentioned the ability to use ArrayFire through Java. In this post, we are going to show how you can get the best performance inside Java using ArrayFire for CUDA and OpenCL. Code Here is a sample code to perform Monte Caro Estimation of Pi.

The same code can be written using ArrayFire in the following manner.

Array.randu(dims, Array.FloatType) creates a uniform random Array. Array.FloatType is passed in to create a uniform random array of 32 bit floating point numbers. Other types can include Array.FloatComplexType, Array.DoubleType and so on. Array.mul, Array.add and Array.lt perform element wise operations on the two operands to produce an output. Array.sumAll adds up all the elements in the array to produce ...

Open Source Initiatives from ArrayFire

Pavan Announcements, ArrayFire, CUDA, Fortran, JAVA, Open Source, OpenCL, OpenGL, R Leave a Comment

At ArrayFire we like to use a lot of Free/Open Source software. We use various Linux distributions, Jenkins, Gitlab, gcc, emacs, vim and numerous other FOSS tools on a daily basis. We also love the idea of developing software collaboratively and openly. Last year we started working with AMD on CL Math Libraries. Internally we've had numerous discussions about contributing to the GPGPU community. However, it's neither simple nor straightforward to take a closed software Open Source. Earlier this year, we decided to take the first step and Open Source all of the ArrayFire library's  tertiary projects. This includes all of our ArrayFire library's language wrappers, examples, and source code used for our blog posts. All of our projects are hosted at our ...

clMath: An Open Source BLAS and FFT Library for OpenCL

Scott Announcements, OpenCL Leave a Comment

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 ...

ArrayFire Examples and Benchmarks Whitepaper

ArrayFire ArrayFire Leave a Comment

What do you get when you offer the world's most comprehensive GPU library available for free? Excited users who go the extra mile and give back to the community. Andrzej Chrzȩszczyk from Jan Kochanowski University recently wrote an awesome whitepaper, entitled "Matrix Computations on the GPU with ArrayFire for Python and C/C++." The whitepaper contains many GPU computing tutorial examples as well as performance timings for each example. Andrzej notes, "The purpose of this document is to make the first steps in using modern graphics cards to general purpose computations simpler."  This document is especially beneficial for programmers looking to accelerate Python or C/C++ codes. We thank Andrzej this fine contribution to the ArrayFire community.  His documentation on ArrayFire will be beneficial to all ...