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 ...
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 ...
The Thrombotherm project by Catalysts is developing a method to analyze blood platelets by means of cell microscopy in real time and to classify them according to their activation state. ArrayFire enabled faster overall research project times and real-time analysis on video data. This project represents an enormous extension of thrombocyte diagnotics, especially through significantly accelerated analysis times. Faster analyses enabled university research collaborators from the University of Applied Sciences OÖ and the Ludwig Boltzmann Institute to shorten research project times. The project has three main parts: Detect cell morphology in real-time Thombotherm makes it possible to mathematically determine and categorize the cell boundaries by means of transmitted light microscopy. The software distinguishes between "fried-egg”-shaped cells and "spider”-shaped cells. This is used ...
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 ...
Today we are pleased to announce the release of ArrayFire v3.4, 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 5 major components of the library that are common to many areas of mathematical, scientific, and financial computing: sparse matrix operations, random number generation, image processing, just-in-time (JIT) compilation, and visualizations. Sparse Matrix and BLAS (see blog post) Support for CSR and COO storage types Sparse-Dense Matrix Multiplication and Matrix-Vector Multiplication Conversion to and from dense matrix to CSR and COO storage types Support for Random Number Generator Engines (see blog post) Philox Threefry Mersenne Twister Image Processing (see blog post) ...
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 ...
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 ...
There are a number of additions and updates to image based features in the new v3.4 release of ArrayFire. Among the updates are: New interpolation methods for several existing functions approx1, approx2 transform resize Functions for image moments This blog post will display some typical use cases for these new features. ArrayFire v3.4 implements several new interpolation methods for 1-d and 2-d domains. The new interpolation methods for 1-d functions are: AF_INTERP_LINEAR_COSINE AF_INTERP_CUBIC and for 2-d functions are: AF_INTERP_BILINEAR_COSINE AF_INTERP_BICUBIC The behavior of the interpolation methods can be seen in the following pictures. A common use for interpolation is image filtering. Given a coarse image, we can resample it to be smoother.
af::array img = af::randu(7,7); //create a random image
//define sample points for interpolation
af::array Xs = af::seq(0, 6, 0.1f);
af::array Ys = af::seq(0, 6, 0.1f);
Xs = af::tile(Xs, 1, Ys.dims(0));
Ys = af::tile(Ys.T(), Xs.dims(0));
//interpolate based on specific method
af::array img_bilinear = af::approx2(img, Xs, Ys, AF_INTERP_BILINEAR);
af::array img_bilinearcos = af::approx2(img, Xs, Ys, AF_INTERP_BILINEAR_COSINE);
af::array img_bicubic = af::approx2(img, Xs, Ys, AF_INTERP_BICUBIC_SPLINE);
Pseudorandom number generators (PRNGs) are an integral part of many applications in statistics, modeling, and simulations. In ArrayFire v3.4, we introduce random number generation enhancements that improve speed, accuracy, storage, and unity among the ArrayFire backends. Previously in ArrayFire v3.3, each ArrayFire backend used a different PRNG. In ArrayFire v3.4, each ArrayFire backend is able to select from among 3 different random number generators. ArrayFire v3.3 (platform specific) ArrayFire v3.4 (all generators on all platforms) CUDA XORWOW CUDA, OpenCL, CPU Philox (CBRNG), Threefry (CBRNG), Mersenne Twister OpenCL Threefry (CBRNG) CPU Mersenne Twister As seen above, the XORWOW generator (which was only available for CUDA devices previously) has been replaced by the Philox generator which is available along with Threefry and Mersenne ...
In ArrayFire v3.4, we have added support for sparse matrices, which greatly reduce the memory footprint on GPUs and accelerated devices for many applications. A sparse data structure is one where all the non-zero elements are not stored. Sparse matrices are useful when the number of zero-values elements are much greater than the number of non-zero elements (i.e. the sparsity of the matrix is high). A sparse data structure is generally stored as 3 arrays: A data or values array containing all the non-zero elements A vector for row indices (based on storage format) A vector for column indices (based on storage format) There are many ways to store sparse matrices, the most prominent of which are: Compressed Sparse Row (CSR) Compressed Storage Column (CSC) ...