## ArrayFire v3.5 Official Release

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 …

## ArrayFire – CUDA Interoperability

Although ArrayFire is quite extensive, there remain many cases in which you may want to write custom kernels in CUDA or OpenCL. For example, you may wish to add ArrayFire to an existing code base to increase your productivity, or you may need to supplement ArrayFire’s functionality with your own custom implementation of specific algorithms. Today’s “Learning ArrayFire from scratch“, blog post discusses how you can interface ArrayFire and CUDA.

## Benchmarking parallel vector libraries

There are many open source libraries that implement parallel versions of the algorithms in the C++ standard template libraries. Inevitably we get asked questions about how ArrayFire compares to the other libraries out in the open. In this post we are going to compare the performance of ArrayFire to that of BoostCompute, HSA-Bolt, Intel TBB and Thrust. The benchmarks include the following commonly used vector algorithms across 3 different architectures. Reductions Scan Transform The following setup has been used for the benchmarking purposes. The code to reproduce the benchmarks is linked at the bottom of the post. The hardware used for the benchmarks is listed below: NVIDIA Tesla K20 AMD FirePro S10000 Intel Xeon E5-2560v2 Background ArrayFire ArrayFire provides high …

## ArrayFire v3.0 is here!

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 …

## Machine Learning with ArrayFire: Linear Classifiers

Linear classifiers perform classification based on the linear combinition of the component features. Some examples of Linear Classifiers include: Naive Bayes Classifier, Linear Discriminant Analysis, Logistic Regression and Perceptrons. ArrayFire’s easy to use API enables users to write such classifiers from scratch fairly easily. In this post, we show how you can map mathematical equations to ArrayFire code and implement them from scratch. Naive Bayes Classifier Perceptron Naive Bayes Classifier Naive bayes classifier is a probabilistic classifier that assumes all the features in a feature vector are independent of each other. This assumption simplifies the bayes rule to a simple multiplication of probabilities as show below. First we start with the simple Baye’s rule.  p(C_k | x) = \frac{p(C_k)}{p(x)} …

## Performance of ArrayFire JIT Code Generation

The ArrayFire library offers JIT (Just In Time) compiling for standard arithmetic operations. This includes trigonometric functions, comparisons, and element-wise operations. At run-time, ArrayFire aggregates these function calls using an Abstract Syntax Tree (AST) data structure such that whenever a JIT-supported function is ”met,” it is added into the AST for a given variable instance. The AST of the variable is computed if one of the following conditions is met: When the above occurs, and the variable needs to be evaluated, the functions and variables in the AST data structure are used to create a single kernel (”function-call”). This is done by creating a customized kernel on-the-fly that is made up of all the functions in the AST – the …

## ArrayFire is Now Open Source

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 …