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 v2.0 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 …
ARM Showcases ArrayFire OpenCL Support for Mali GPU at Supercomputing ’13
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 …
clMath: An Open Source BLAS and FFT Library for OpenCL
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. …
Beamforming with ArrayFire
Alessandro Savoia and researchers at Università degli Studi Roma Tre have achieved an order of magnitude improvement in the performance of a beamforming application using ArrayFire for GPU acceleration with CUDA-capable NVIDIA GPUs. This application involves conventional beamforming. Steps include the application of a time delay to each signal vector, summation across all vectors, and processing on the result. Processing includes demodulation, envelope extraction, and logarithmic compression. ArrayFire’s functions for shifting, interpolation, and filtering made this application possible for acceleration on GPUs and reduced the time to develop significantly. Alessandro’s benchmarks show that a CPU-only version was only running at 1 frame/sec, while the ArrayFire-accelerated version was running at 10-20 frames/sec, depending on the dataset. Alessandro and his team are looking forward to …
ArrayFire + Scorpii Demo by CreativeC
CreativeC makes awesome compute + visualization systems. We got to see the demo in live action at the GPU Technology Conference last month. Tim Thomas was kind enough to let us film the demo showing how ArrayFire can be used to drive a multi-node, 9 GPU system in a physics application. Checkout the video below. If you are interested in high-throughput compute coupled with high-pixel visualizations, we recommend you talk with the folks at CreativeC. They are always pushing the envelope on what can be done with GPU computing and GPU visualizations. Also, if you have cool demos showing ArrayFire in action, let us know. We’d love to film your work and make it available on this blog! Related articles …
How much speedup can you get with CUDA or OpenCL?
Everyday developers ask us to predict how much speedup they can get with CUDA or OpenCL. Rather than gaze mysteriously into a crystal ball, we ask the developers questions to explore pertinent acceleration factors. Note, we’ll use the term accelerator to include GPUs, Xeon Phi coprocessor, APUs, FPGAs, and any other CUDA or OpenCL device. The principles we discuss below are equally applicable to all of these accelerators. The following are some of the important factors that must be considered when estimating the potential for accelerated speedups: Hardware: The more advanced the accelerator hardware, the more the speedup you get (e.g. the NVIDIA Kepler K20 outperforms the previous NVIDIA Fermi C2090 generation). Data Sizes: In general, accelerators will outperform CPUs to …
Jacket v2.1 Now Available
Optimization Library, Sparse Functionality, Graphics Library Improvements, CUDA 4.1 Enhancements, and much more… AccelerEyes announces the release of Jacket v2.1, adding GPU computing capabilities for use with MATLAB®. Jacket v2.1 delivers even more speed through a host of new improvements, maximizing GPU device performance and utilization.. Notable new features include an Optimization Library and additional functions to our Graphics Library. With Jacket v2.1, we have also extended support for sparse matrix subscripting and made improvements to host-to-device and device-to-host data transfer speeds for complex data. In addition, we have included various GFOR enhancements. Jacket v2.1 now includes NVIDIA CUDA 4.1 enhancements to provide improved functionality and performance (requires latest drivers). Jacket is the premier GPU software plugin for MATLAB®, better than alternative …
AccelerEyes Webinar Series
AccelerEyes invites you to participate in series of webinars designed to help you learn more about Jacket for MATLAB® and ArrayFire for C/C++/Fortran/Python, a comprehensive library of GPU-accelerated functions. GPU Programming for Medical Image Segmentation: January 18, 2012 at 3:00 p.m. EST There’s a huge volume of data generated using acquisition modalities like computer tomography (CT), magnetic resonance imaging (MRI), positron emission tomography or nuclear medicine. A common need is to manipulate and transmit this data using compression techniques in as little time as possible. During this webinar we will show Jacket’s superior speed and handling volumes from subscripting to convolutions. Come and learn how to accelerate common medical imaging applications using an easy, powerful programming library with Jacket for MATLAB®. OpenCL and CUDA Trade-Offs and Comparison: February 15, 2012 at …
Jacket v2.0 Now Available
New Multi-GPU functionality , added support for OpenCL devices, and much more… AccelerEyes announces the release of Jacket version 2.0, adding GPU computing capabilities for use with MATLAB®. Version 2.0 delivers even more speed through a host of new improvements, maximizing GPU device performance and utilization. Notable new features include a multi-GPU interface and support for OpenCL devices. With Jacket v2.0, your M-code is now portable across all major GPU devices, including AMD/ATI, Intel, and NVIDIA chips. Jacket is the premier GPU software plugin for MATLAB®, better than alternative solutions. It is relied upon by thousands of organizations for rapid prototyping and problem solving across a range of government, manufacturing, energy, media, biomedical, financial, and scientific research applications. Multi-GPU Details: …