We get questions all the time about how to make GPU hardware decisions. We’ve seen just about every scenario you can imagine, and so we always jump at the chance to help others through this decision process. Here’s a recent question from a customer. “I’ve just found your post on Analytic Bridge and have taken a look at your website … I’m replacing my two Tesla M1060 cards (computing capability too low) and I’m considering used Tesla M2070s or the new GTX 760 cards. Could you offer any insight? I believe the GTX 760 cards may well outperform the older 2070s and are much cheaper.” And here’s our response. “The GTX 760 will probably outperform the M2070 for single precision …
ArrayFire v2.1 Official Release
It’s that time again—we’re pleased to announce the release of our newest version of ArrayFire: ArrayFire v2.1. ArrayFire v2.1 is now bigger, faster, and stronger, thanks to some key function additions, API changes, feature improvements, and bug fixes. 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. Major Updates Support for CUDA 6.0 Support for Mac OS X New language support (available on github) ArrayFire for Java ArrayFire for R! ArrayFire for Fortran* ArrayFire Extras on Github All language wrappers …
ArrayFire-OpenGL Interop using CUDA
A lot of ArrayFire users have been interested in the usage of ArrayFire in partnership with OpenGL for graphics computation. In the long run, we do plan to expand further on the interoperablilty and make it easier through ArrayFire. For now, we have developed a small example to expand on the usage of the CUDA-OpenGL interop API to assist in the interop operations between ArrayFire and OpenGL. Some of the advantage of direct ArrayFire-OpenGL interop are: Faster data transfers: Since the OpenGL buffers as well as ArrayFire data reside on the GPU, we can use a direct device to device copy rather than using the CPU as an intermediate and the relatively slow PCIe interface. Offscreen rendering: It is commonly …
ArrayFire v2.0 Official Release
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 …
Photos from SC13
SC13 was awesome this week! Tomorrow is the last day of the exhibition. For those of you that did not make it to the show, here are some pictures from our exhibit: The AccelerEyes Booth ——————————————————————————————————– ArrayFire OpenCL Demo on ARM Mali ——————————————————————————————————– ArrayFire CUDA Demo on NVIDIA K40 ——————————————————————————————————– ArrayFire OpenCL Demo on Intel Xeon Phi Coprocessor ——————————————————————————————————– ArrayFire OpenCL Demo on AMD FirePro GPU ——————————————————————————————————– It was a great show and wonderful to see so many ArrayFire users in person. If you could not attend and would like to learn more about our CUDA or OpenCL products or services, let us know! Related articles ArrayFire v2.0 Release Candidate Now Available for Download Two Kinds of Exhibits to Watch …
ArrayFire v2.0 Release Candidate Now Available for Download
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 Examples (Part 7 of 8) – PDE
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
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
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 …
Solution to NVIDIA Toolkit Installation Error for Ubuntu 12.10 [Driver: Installation Failed]
Driver: Installation Failed You may find this error message while trying to set up the NVIDIA CUDA Toolkit in Ubuntu. I found it when I was installing the toolkit for ArrayFire [1] CUDA Toolkit Installation 1. Download the CUDA Toolkit in the link. 2. Extract the .run file in a location sudo sh cuda_5.0.35_linux_64_ubuntu11.10-1.run –extract <location> 3. Exit the X server (press Ctrl+Alt+F1) and stop the display manager by the following command. sudo stop lightdm 4. cd to the location and now there are run files named samples*, devdriver* and cudatoolkit*. 5. Install devdriver (*only if NVIDIA Driver is not installed) sudo sh devdriver_5.0_linux_64_304.54.run 6. Install cudatoolkit sudo sh cudatoolkit-5.0.35_linux_64_ubuntu11.10.run In the end, when it asks …