We are pleased to announce a new release of the ArrayFire library, v3.9.0. This release makes it easier than ever to target new devices without sacrificing performance. This post describes four of these new features, including: oneAPI Backend This release is the first time since v3.0 that introduces a new backend. The new backend is built with the oneAPI specification on top of the SYCL language. oneAPI is an open specification providing a full framework for high-performance computing applications without vendor lock-in. While this has been possible with OpenCL, the oneAPI specification includes libraries like BLAS and FFT significantly reducing the burden on the developers to maintain math functions and increasing the performance of these common operations. Here is a …
Synthetic Aperture Radar on the Jetson TX1
Researchers at Peter the Great St. Petersburg Polytechnic University have implemented a synthetic aperture radar processing on the Jetson TX1 Platform using ArrayFire as described in this paper. The paper introduces SAR as “a remote sensing technique producing high-resolution radar images of the Earth’s surface. SAR technology allows obtaining wide swath radar images of objects at a considerable distance regardless of the weather and lighting conditions. It can be used by unmanned aerial vehicles and space satellites. Thus, SAR technology allows solving various problems, such as: detecting small objects (vehicles, airplanes, ships), assessing the state of railways, airfields, seaports, mapping an area, assisting in geological exploration, mapping vegetation, detecting oil spills and pollution as well as many other tasks.” The …
CUDA Computing on Google Colab with ArrayFire
For the first-time in our 14 year existence, we are now able to provide our community with the ability to run ArrayFire programs for free within minutes. Before today, users would have to download and install the library on their own systems, which can be a hassle if you just want to play around with some code and benchmarks. Today, we’re excited to announce that ArrayFire is available on Google Colab, the free GPU computing cloud service from Google. Colaboratory, or “Colab” for short, allows you to write and execute Python in your browser, with Zero configuration required, free access to GPUs, and easy sharing. You can jump right in and start playing with this new tool: Click Here to …
Finger Vein Identity Recognition in “Negligible Time” using ArrayFire
In this blog post, we summarize work by researchers in Slovakia using ArrayFire to develop OpenFinger, a finger vein identity recognition library. Finger prints and finger veins can be used as a biometric for identity recognition. The physical setup of their sensor system is the following collection of CMOS sensors scattering light to a near infrared LED that projects the image to a CCD camera for capture to a computer. The computing infrastructure used in this work consists of the following components. Several great open source libraries are used, including OpenCV, Caffe, Qt, and ArrayFire. ArrayFire is specifically used in pre-processing to accelerate Gabor filtering on the GPU. Gabor filter has proven itself as one of the most suitable techniques …
Bringing Together the GPU Computing Ecosystem for Python
To date, we have not done a lot for the Python ecosystem. A few months ago, we decided it was time to change that. Like NVIDIA said in this post, the current slate of GPU tools available to Python developers is scattered. With some attention to community building, perhaps we can build something better — together. NVIDIA spoke some about its plans to help cleanup the ecosystem. We’re onboard with that mentality and have two ways we propose to contribute: We’re working on a survey paper that assesses the state of the ecosystem. What technical computing things can you do with each package? What benchmarks result from the packages on real Python user code? What plans does each group have …
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 …
GTC 2015 ArrayFire Recordings
Missed visiting ArrayFire at GTC this year? We’ve got you covered! You can now check out the recordings of all our GTC 2015 talks and tutorials at your own convenience. Learn about accelerating your code from the best in the business. Talks Real-Time and High Resolution Feature Tracking and Object Recognition Peter Andreas Entschev This session will cover real-time feature tracking and object recognition in high resolution videos using GPUs and productive software libraries including ArrayFire. Feature tracking and object recognition are computer vision problems that have challenged researchers for decades. Over the last 15 years, numerous approaches were proposed to solve these problems, some of the most important being SIFT, SURF and ORB. Traditionally, these approaches are so computationally …