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
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 …
Visit ArrayFire at GTC 2017
GTC is quickly approaching and we want to see you there! When: May 8-11 Where: San Jose, California ArrayFire Booth: 406 GTC is the world’s premier GPU developer conference. Connect with experts from NVIDIA and other leaders in high performance computing. At GTC you’ll discover what’s next in GPU breakthroughs and gain useful insights in hundreds of sessions and hands-on labs covering a diverse range of application domains. Attend an ArrayFire Talk We invite you to attend the following talk presented by one of our knowledgeable and experienced GPU developers. ARRAYFIRE GRAPH: DYNAMIC GRAPH LIBRARY FOR GPUS Presented by Kumar Aatish – Thursday May 11 at 9:00 am – Marriott Ballroom 3 (Session ID S7723) ArrayFire Graph is an out-of-core dynamic graph library that …
ArrayFire at SC16
SC16 is almost here! We’re getting excited to heading to Salt Lake City, Utah, to be a part of this excellent conference. It’s a great place for soaking up HPC knowledge, getting inspired, and connecting with the brightest minds in the industry. Here’s a quick run-down of where we’ll be. Visit our booth. We’re booth #717 in the exhibit hall during exhibit hours November 15 – 17. We’ll be showing off our latest demos and our engineers will be available for questions. Ask your questions, meet the team, or just bounce some ideas. Try our in-booth tutorials. Want to learn how to use ArrayFire to accelerate your code? Stop by and receive an in-booth tutorial from one of our ArrayFire experts. We’ll show …
Graphics Updates in ArrayFire v3.4
This post outlines the new graphics features available in ArrayFire v3.4: Vector Fields, Overlays We have added visualization support to render ArrayFire array objects as vector fields. An example of how to visualize vector fields is included in ArrayFire v3.4. A screenshot of this example’s output in multi-view mode is shown below, showcasing both static and dynamic vector field rendering. Previously, each graph (such as plot, hist, scatter, etc) was rendered in its own window (or view). Overlaying graphs was not supported. ArrayFire v3.4 now support graph overlays. Each draw call in ArrayFire is either rendered to a whole window (single view) or to a view, which is a portion of the screen obtained in multiview mode. The following image is an example of a …
Performance Improvements to JIT in ArrayFire v3.4
ArrayFire uses Just In Time compilation to combine many light weight functions into a single kernel launch. This along with our easy-to-use API allows users to not only quickly prototype their algorithms, but also get the best out of the underlying hardware. This feature has been a favorite among our users in the domains of finance and scientific simulation. That said, ArrayFire v3.3 and earlier had a few limitations. Namely: Multiple outputs with inter-dependent variables were generating multiple kernels. The number of operations per kernel was fairly limited by default. In the latest release of ArrayFire, we addressed these issues to get some pretty impressive numbers. In the rest of the post, we demonstrate the performance improvements using our BlackScholes …
Visit ArrayFire at GTC 2016
GTC is quickly approaching and we want to see you there! When: April 4-7 Where: San Jose, California ArrayFire Booth: 103 GTC is the world’s premier GPU developer conference. Connect with experts from NVIDIA and other leaders in high performance computing. At GTC you’ll discover what’s next in GPU breakthroughs and gain useful insights in hundreds of sessions and hands-on labs covering a diverse range of application domains. We invite you to attend the following talk presented by one of our knowledgeable and experienced GPU developers. Real-Time Visualization of CUDA® Data Using ArrayFire Forge Presented by Brian Kloppenborg – Wednesday April 6 at 3:30 pm (Room 211B) We will debut ArrayFire Forge, our new general-purpose data visualization library for GPUs. ArrayFire Forge is a …
ArrayFire at SC15
SC15 is almost here! We’re getting excited to head down to Austin, Texas, to be a part of this excellent conference. It’s a great place for soaking up HPC knowledge, getting inspired, and connecting with the brightest minds in the industry. Here’s a quick run-down of where we’ll be. Visit our booth. We’re booth #2229. We’ll be showing off our latest demos and our engineers will be available for questions. Ask your questions, meet the team, or just bounce some ideas. Try our in-booth tutorials. Want to learn how to use ArrayFire to accelerate your code? Stop by and receive an in-booth tutorial from one of our ArrayFire experts. We’ll show you how the open source ArrayFire software library makes programming GPUs and …
ArrayFire release cycle
Coming off of our latest release cycle, we think it is a good time to talk about our release policy more openly. In this post we are going to talk about our versioning scheme and the expected release cycle. The versioning scheme of the library will follow the Apache Portable Runtime guidelines. Each release version number will be of the following format arrayfire-x.y.z. The releases are going to be categorized broadly into the following categories based on the frequency and scope. Bug fix releases A bugfix release will bump the 3rd digit of the release version. These releases will neither add new functions nor break API and ABI compatiblity from the previous Feature release. Bug fix releases will be infrequent, …
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 …