ArrayFire v2.0 Official Release

ScottAnnouncements, ArrayFire, CUDA, OpenCL 1 Comment

We are thrilled to announce the official release ArrayFire v2.0, our biggest and best product ever! ArrayFire v2.0 adds full commercial support for OpenCL devices including all AMD APUs and AMD FireProTM graphics, CUDA GPUs from NVIDIA, and other OpenCL devices from Imagination, Freescale, ARM, Intel, and Apple. ArrayFire is a CUDA and OpenCL library designed for maximum speed without the hassle of writing time-consuming CUDA and OpenCL device code.  With ArrayFire’s library functions, developers can maximize productivity and performance. Each of ArrayFire’s functions has been hand-tuned by CUDA and OpenCL experts. Announcing ArrayFire for OpenCL Support for all of ArrayFire’s function library (with a few exceptions) Same API as ArrayFire for CUDA enabling seamless interoperability Just-In-Time (JIT) compilation of …

ARM Showcases ArrayFire OpenCL Support for Mali GPU at Supercomputing ’13

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ARM showcased ArrayFire support for the Mali GPU at the Supercomputing ’13 conference recently held in Denver.  This exciting development caught the attention of many attendees as they viewed the ArrayFire demos running in the ARM and AccelerEyes exhibits.   Energy budgets are always constrained, and form an expensive component of any HPC system. ARM Mali GPUs provide the best performance and throughput for a given energy envelope. Partnering with ARM, AccelerEyes further reduces the cost of HPC by minimizing development time and costs. AccelerEyes offers the most productive software solutions for accelerating code using GPUs, coprocessors, and OpenCL devices.  AccelerEyes delivers ArrayFire to accelerate C, C++, and Fortran codes on CUDA and OpenCL devices.  ArrayFire customers come from a wide range …

Partners Magnify the SC13 Experience

John MelonakosArrayFire, Events 1 Comment

Yesterday, we posted photos from our exhibit. Today was the last day of SC13, and we want to tip our hat to the wonderful partners that magnified our SC13 experience. Creative Consultants, Mellanox, and Allinea Creative Consultants ran an ArrayFire demo across several nodes using Mellanox interconnect. The demo was a multi-node, multi-GPU lattice boltzmann simulation. Allinea also showcased their debugging and profiling tools on the same ArrayFire based code. AMD ArrayFire OpenCL demos were showcased in the AMD exhibit. It was great to see momentum from AMD at SC13 carried over from the previous week’s APU13 conference. Microway In the photo below, you can see ArrayFire running on Microway’s WhisperStation. Microway had prime real estate at the conference and surely every …

APU 2013 – Day 1 Recap

John MelonakosEvents, OpenCL Leave a Comment

AMD’s APU 2013 kicked off today with keynotes and a welcome reception. The developer summit is themed as the epicenter of heterogeneous computing. AMD has a world class CPU and a world class GPU and is pushing the industry forward by combining both of those devices into the same chip, the APU. AMD’s APUs are programmable via OpenCL, the industry standard for heterogeneous development. AMD is also leading the way with standards for Heterogeneous System Architecture (HSA). APU13 will have many technical sessions, keynotes, and demos around OpenCL and HSA. We are at the APU conference demoing ArrayFire acceleration on two of AMD’s newest hardware offerings: A machine with the latest AMD Radeon R9 209X discrete GPU A machine with the …

ArrayFire v2.0 Release Candidate Now Available for Download

Aaron TaylorAnnouncements, ArrayFire, CUDA, OpenCL Leave a Comment

ArrayFire v2.0 is now available for download. The second iteration of our free, fast, and simple GPU library now supports both CUDA and OpenCL devices. Major Updates ArrayFire now works on OpenCL enabled devices New and improved documentation Optimized for new GPUs–NVIDIA Kepler (K20) and AMD Tahiti (7970) New in ArrayFire OpenCL Same APIs as ArrayFire CUDA version Supports both Linux and Windows Just In Time Compilation (JIT) of kernels Parallel for: gfor Accelerated algorithms in the following domains Image Processing Signal Processing Data Analysis and Statistics Visualization And more New in ArrayFire CUDA New Signal and Image processing functions Faster transpose and matrix multiplication Better debugging support for GDB and Visual Studio Bug fixes to make overall experience better For a more complete list of  the …

ArrayFire for Defense and Intelligence Applications – Joint Webinar Recap

Aaron TaylorEvents 1 Comment

In case you missed it, hundreds of attendees recently joined us in a special joint webinar with NVIDIA. The webinar was led by Kyle Spafford, a Senior Developer at AccelerEyes. Kyle detailed how GPU computing can be implemented in the defense and intelligence fields. Kyle specifically addressed enabling unique solutions for applications related to video analysis, recognition, and tracking using the ArrayFire software library for C, C++, and Fortran. At the conclusion of the presentation Kyle fields questions from those in attendance, including “How does ArrayFire Fortran Lib compare to CUDA Fortran?” (see 59:36 mark), “Can you target a specific GPU if you have multiple on the machine?” (56:14), and “How can I combine several kernels to one fat kernel by using …

clMath: An Open Source BLAS and FFT Library for OpenCL

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If you’re reading our blog, BLAS and FFT libraries likely form an important basis for your work. For instance, BLAS and FFT libraries are used in some of ArrayFire’s higher-level functions for linear algebra, signal processing, and image processing. Today, OpenCL is getting a significant boost in BLAS and FFT library availability. AMD has announced a bold and generous move to contribute to the OpenCL community by open-sourcing its APPML BLAS and FFT OpenCL libraries. At AccelerEyes, we have previously used AMD’s OpenCL libraries within our higher-level ArrayFire library. These libraries are the best BLAS and FFT OpenCL libraries available anywhere. We are thrilled to join AMD and the open-source community in maintaining and improving these libraries for the benefit of all. …

ArrayFire Examples (Part 7 of 8) – PDE

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This is the seventh in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the pde/ directory. In these examples, my machine has the following configuration: ArrayFire v1.9.1 (build XXXXXXX) by AccelerEyes (64-bit Linux) License: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX CUDA toolkit 5.0, driver 319.17 GPU0 Tesla K20c, 5120 MB, Compute 3.5 (current) GPU1 Tesla C2075, 6144 MB, Compute 2.0 GPU2 Tesla C1060, 4096 MB, Compute 1.3 Display Device: GPU0 Tesla K20c Memory Usage: 5044 MB free (5120 MB total)   The followings are the examples of formulating Partial Differential Equations, generally used to create a relevant computer model with several variables. In these examples, …

ArrayFire Examples (Part 6 of 8) – Multiple GPUs

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This is the sixth in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the multi_gpu/ directory. In these examples, my machine has the following configuration: ArrayFire v1.9.1 (build XXXXXXX) by AccelerEyes (64-bit Linux) License: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX CUDA toolkit 5.0, driver 319.17 GPU0 Tesla K20c, 5120 MB, Compute 3.5 (current) GPU1 Tesla C2075, 6144 MB, Compute 2.0 GPU2 Tesla C1060, 4096 MB, Compute 1.3 Memory Usage: 4935 MB free (5120 MB total) *The following order represents the speed of GPUs in my machine from fastest to slowest: K20c, C2070, C1060. ArrayFire is capable of multi-GPU management. This capability becomes useful for benchmarking …

ArrayFire Examples (Part 5 of 8) – Machine Learning

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This is the fifth in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the machine_learning/ directory. In these examples, my machine has the following configuration: ArrayFire v1.9 (build XXXXXXX) by AccelerEyes (64-bit Mac OSX) License: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX CUDA toolkit 5.0, driver 304.54 GPU0 GeForce GT 560M, 1024 MB, Compute 3.0 (single,double) Display Device: GPU0 GeForce GT 650M Memory Usage: 245 MB free (1024 MB total)…    1. K-Means Clustering – kmeans.cpp Figure 1 This is an example of K-Means Clustering Algorithm. K-Means Clustering Algorithm is a data mining technique that partitions the given data into groups by their similarities. All you need to …