We often get questions such as the one we just received via email: 1) Any idea if you will be supporting AMD/ATI cards in future ? 2) Have you considered OpenCL as a potential pathway for the future ? I can see an advantage there for you (if it takes off) in that you’re not tied to a single vendor any more and potentially you’d be able to take advantage of other accelerators that may support it. It’s very early days yet but certainly from our point of view the current paradigm of code to a single vendors card doesn’t seem sustainable.. OpenCL is a community effort to create a standard for parallel computing, with early emphasis on GPGPU computing, …
ArrayFire v3.9.0 Release
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 …
The Torch By ArrayFire: Q4’2022 GPU Updates
News for the accelerated computing community – November 2, 2022 Signup for Newsletter Emails Dear ArrayFire Community, Last quarter was highlighted by the significant announcement that our team has joined Intel Corporation to deliver on a shared vision of open-source accelerated computing with oneAPI. The ArrayFire open-source project will continue to follow The ArrayFire Mission. It will be governed by its maintainers sponsored by various companies, including Google, Twitter, VoltronData, and now Intel. ArrayFire’s support for CUDA, OpenCL, and x86 will continue unchanged. We are also excited to announce that our consulting and training services team is expanding its offering in partnership with OpenTeams, a leading provider of technology and talent to support companies backed by innovative open-source communities like ours. This quarterly newsletter brings together …
Simulating Soliton Excitations in Open Systems
Researchers from the University Bordeaux credit ArrayFire in a paper published in a Master’s Thesis by André Almeida. The thesis is titled “Soliton Excitations in Open Systems using GPGPU Supercomputing.” It investigates the stability of nonlinear excitations in open optical systems modeled by the Complex Ginzburg Landau Equation when influenced by effects such as dissipation and gain, using numerical simulations. Summary In the early years of the 19th century the naval engineer James Scott Russell made the first observation of a very uniform accumulation of water in a boat canal that was capable to propagate for many kilometers without any losses in amplitude and with constant width. This was a very strange phenomenon at the time because no known description of hydrodynamics …
Exciting Updates at ArrayFire
Today, we are pleased to announce that our open-source team has joined Intel to focus on building an open future for accelerated computing with oneAPI. At Intel, we will build towards a vision that flourishes at scale, serves domain professionals worldwide, and participates in the exciting oneAPI ecosystem of open-source technical computing. Read more about this on the Intel blog: ArrayFire Team joins Intel for oneAPI. The ArrayFire open-source project will continue to follow The ArrayFire Mission. It will be governed by its maintainers sponsored by a variety of companies, including Google, Twitter, VoltronData, and now Intel. ArrayFire’s support for CUDA, OpenCL, and x86 will continue unchanged. We are also excited to announce that our consulting and training services team …
The Torch By ArrayFire: Q3’2022 GPU Updates
News for the accelerated computing community – June 27, 2022 Signup for Newsletter Emails Dear Friends, Welcome to the first newsletter for our ArrayFire community! This newsletter brings together people using and developing ArrayFire and other accelerated computing tools. You are part of this vibrant group that “gathers” together around open source work, including: You are distinguished professionals in your domains, and we hope to build more opportunities for you to interact with the ArrayFire team and one another. We will start with this lightweight quarterly newsletter. At a glance, you’ll be able to see recent developments as well as upcoming opportunities. Enjoy! -John Melonakos, CEO & Co-Founder Product Releases ArrayFire v3.8.2 was released on May 19, 2022. Read more …
Classification of Topological Discrepancies in 3D Printing with ArrayFire
Researchers from the Palo Alto Research Center in California credit ArrayFire in a paper published in the Journal of Computer-Aided Design. The paper is titled “A Classification of Topological Discrepancies in Additive Manufacturing” and showcases a novel approach for classification of local shape deviations in topological terms than can be used to improve 3D printing processes. The OpenCL version of ArrayFire on an NVIDIA GTX 1080 GPU was used for FFT-based convolutions and superlevel set operations. A design’s manufacturability via an additive manufacturing (AM) process is largely determined by the AM machine’s ability to print the shape within ‘acceptable limits’. The notion of geometric dimensioning and tolerancing has been used successfully to define and check these limits for conventionally manufactured …
ArrayFire Updates to Kickoff 2022
We are excited to report a great kickoff to 2022 with this quick list of notable ArrayFire developments underway. ArrayFire v3.8.1 Release We recently announced ArrayFire v3.8.1, available on Github (source) and on our download page (binaries). Flashlight Project for Machine Learning The open source Flashlight project from Facebook is growing rapidly. In a single repository, Flashlight provides apps for research across multiple domains: Automatic speech recognition Image classification Object detection Language modeling Flashlight builds atop ArrayFire as the tensor library for GPU and CPU training. Parallel Universe Magazine ArrayFire was recently featured in Intel’s Parallel Universe Magazine. Check out the article entitled, “ArrayFire Interoperability with oneAPI, Libraries, and OpenCL Code.” This article explains how, with minor code changes, whole OpenCL libraries …
ArrayFire v3.8.1 Release
We are excited to share the v3.8.1 bugfix release of ArrayFire! In this post, we share an overview of the changes to ArrayFire in its 3.8.1 bugfix release. The binaries and source code can be downloaded from these locations: Official installers GitHub repository Official APT repository The bugfix release consists mainly of overall improvements to the ArrayFire 3.8 codebase as well as bugfixes. Improvements As always, a number of improvements have been made to all backends. We continue to clean up the codebase and update the library to support newer frameworks. In addition to general maintenance and bookkeeping, the following improvements have been added: moddims now uses JIT approach for certain special cases JIT Performance Optimization Improved readability of log …
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The Fastest Library for GPUsalso, hire us to accelerate your codeGet StartedDownload BinariesGithub ProjectTalk to Us About Your ProjectLearn abour Our ServicesEasy-to-use API, like these examples”Used by 10,000s of developers, ArrayFire is easy-to-use and blazingly fast.”-Developed over 14 years, with an obsession for excellenceHundreds of FunctionsArrayFire supports hundreds of accelerated tensor computing functions, in the following areas: Array handling Computer vision Image processing Linear algebra Machine learning Standard math Signal Processing Statistics Vector algorithms Data structures in ArrayFire are smartly managed to avoid costly memory transfers and to take advantage of each performance feature provided by the underlying hardware.ArrayFire CommunityThe community of ArrayFire developers invites you to build with us if you’re interested and able to write top performing tensor functions. …