In case you missed it, we recently held a webinar on how to accelerate common medical imaging applications using an easy, powerful programming library with Jacket for MATLAB®.

This webinar was part of an ongoing series of webinars that will help you learn more about the many applications of Jacket and ArrayFire, while interacting with AccelerEyes GPU computing experts.  Gallagher Pryor, CTO of AccelerEyes, used the Bayesian Image Segmentation algorithm as a simple use-case to show how easy it is to convert CPU code to GPU code with Jacket (only 4 lines of CPU code needed to be changed!).

For those of you who missed it, we uploaded the webinar on Youtube. We hope to see you at the next one!

We recently reported that Jacket could be used over Windows Remote Desktop connections as long as you had an NVIDIA Tesla device in TCC mode. With the latest NVIDIA driver updates, Tesla and Quadro devices can be put into TCC mode, making it possible to use Jacket over Remote Desktop with both Tesla and Quadro devices.

We have tested this out with the NVIDIA Quadro 4000 as well as Quadro 6000 GPUs. The system had a Tesla C2050 connected to the display, and the Quadro in TCC mode. Here’s the ginfo output:

>> ginfo
Jacket v2.0 (build 80c7ba4) by AccelerEyes (64-bit Windows)
License Type: Designated Computer ([JACKET_ROOT]\jacket\engine\jlicense.dat)
Addons: MGL4, JMC, SDK, DLA, SLA
CUDA toolkit 4.0, driver 285.62
GPU1 Quadro 4000, 2048 MB, Compute 2.0 (single,double)
Memory Usage: 1977 MB free (2048 MB total)

Jacket over Remote Desktop is documented extensively on the AccelerEyes Wiki. Please check that page for more information.

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 3:00 p.m. EST

The OpenCL standard continues to mature and is now (or soon will be) supported by a variety of GPUs and manycore processors. At AccelerEyes, we remain at the forefront of OpenCL development. ArrayFire OpenCL is a fast software library for GPU computing with a simple API.  In this informative webinar, our team of GPU experts will discuss OpenCL and CUDA trade-offs and comparisons.  In addition, you’ll get to see ArrayFire OpenCL in action with real code.

GPU Programming for Financial Computing:  March 15, 2012 at 3:00 p.m. EST

Quantitative analysts are discovering the benefits of leveraging GPUs in tackling complex financial computing models. Using Jacket’s computational horsepower, analysts can employ a variety of functions to achieve speedups in trade signal generation, complex derivative pricing, evaluating risk scenarios, and more. In this webinar, we’ll discuss the latest developments in GPU programming for financial computing.

Each webinar will be conducted by AccelerEyes’ team of GPU computing experts and will include live demos of Jacket and ArrayFire.   We hope you will join us as we discuss exciting developments in GPU computing software!

One of the biggest areas where GPUs are providing benefit is with scientific computing. With libraries like Sage and SciPy providing a huge collection of functions and algorithms for free, Python has become one of the favorite tools for developers around the world. Even though these libraries have C/C++ back-ends, performance on large problems quickly becomes an issue and can kill productivity.

On the heals of our free release of Arrayfire C/C++, we’re excited to release ArrayFire Python. All of this is FREE for most users (see below for clarification)!

The structure of ArrayFire/Python is loosely based on NumPy in that it uses a single array object that can contain multiple data types. You can convert NumPy arrays to ArrayFire arrays and vice versa. If you already have your application using NumPy arrays, this is a quick way to jump in and tweak critical sections.

import numpy as np
import arrayfire as af
a = np.random.rand(5,5)
b = af.array(a)
c = b.host() # c is the same as a

Alternatively, you can generate data on the device:

r = af.randu(5, 5)
o = af.ones(5,5)
z = af.zeros(5,5)

Once you have the data you need, you can utilize hundreds of functions to convert your code entirely onto the GPU. You’ll find much of the API follows directly from NumPy itself.

Screenshot taken while running raindrop example

Jumping right in, here is an example showing the Monte-Carlo calculation of pi

from arrayfire import *
def pi(samples=20000000):
    x = randu(samples, 1)
    y = randu(samples, 1)
    return 4 * sum(mul(x, x) + mul(y, y) < 1) / samples

You can visit our website to download the latest version of ArrayFire. You can find the Python wrapper in arrayfire/python directory. Installation instructions are in README, and a few examples are included that show off both compute and visualizations.

Our Forums are the best place to get the latest info and help.

AccelerEyes provides this software for free in the hope that some of you might be interested in hiring us to port your code to the GPU.  If that is interesting, let us know!

* ArrayFire is free for use on a single GPU.  To run ArrayFire on larger hardware systems, contact sales@accelereyes.com.

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:

• Control over all GPUs in your program through simple, fast GPU selection functions.  Jacket automatically handles communication between the GPU devices, without the need to launch bulky parallel computing workers
• GINFO, GSELECT, GSYNC all extended to handle multiple devices

OpenCL Details:

• Supports single precision, floating point, real, and complex types
• Supports array math, FFTs, element-wise operations, and more
• Selection of any OpenCL compliant device listed in GINFO
• Currently available as a FREE beta feature, download now

Other Notable Improvements:

• New Base Jacket Functions, such as MEDIAN and PROD
• Additional Image Processing Library functions
• Additional Statistics Library functions
• Additional Signal Processing Library functions
• Support for binary to decimal conversion with BI2DE, DE2BI
• New Demos (included in every download):
  • Defense Optical Flow Tracking example, Music Visualizer example, and new Jacket CPU v. GPU demo
• Financial example of Black-Scholes with GCOMPILE is 35X faster than CPU
  

Visit our company website and see the v2.0 release notes for the full list of enhancements.

Priciing and Availability

Jacket v2.0 is available for download on the AccelerEyes website.  Pricing for a Jacket base license with support for a single GPU is $999.00 USD for commercial and $350.00 USD for academic customers.  AccelerEyes provides 12 months of software maintenance and updates with each software license.  Volume packages and development bundles are also now available at special price points.

Try our Professional Services

AccelerEyes provides professional GPU consulting services.  Our team of engineers guarantees great results from GPU computing.  Equipped with Jacket and years of experience, our experts deliver results in fewer hours than any other consulting firms.  Set up a free GPU consultation today.

Lenovo and AccelerEyes have a joint solution for optimizing M code on Lenovo workstations.  The combined HPC solution combines high Intel Xeon CPU performance for daily productivity with unprecedented NVIDIA graphics (GPU) performance for parallel computing with Jacket. Jacket’s comprehensive benchmark suite, when run on Lenovo ThinkStation systems, shows tremendous amounts of speedups for a wide variety of computationally-intensive applications.

A free, fast, and simple GPU library for CUDA and OpenCL devices.

AccelerEyes announces the launch of ArrayFire, a freely-available GPU software library supporting CUDA and OpenCL devices.

ArrayFire supports C, C++, Fortran, and Python languages on AMD, Intel, and NVIDIA hardware.  Learn more by visiting the ArrayFire product page.

“ArrayFire is our best software yet and anyone considering GPU computing can benefit,” says James Malcolm, VP Engineering at AccelerEyes.  “It is fast, simple, GPU-vendor neutral, full of functions, and free for most users.”

Thousands of paying customers currently enjoy AccelerEyes’ GPU software products.  With ArrayFire, everyone developing software for GPUs has an opportunity to enjoy these benefits without the upfront expense of a developer license.

Reasons to use ArrayFire:

• Fast.  It beats other CPU and GPU acceleration software.  Benchmark it yourself!
• Friendly.  You can learn it in minutes.  It is super easy to use.
• Useful.  It will benefit your code.  It contains the largest set of GPU software functions in the world.
• HW-neutral.  Run on your favorite hardware.  ArrayFire code runs on any CUDA or OpenCL device.
• Proven.  AccelerEyes’ software is relied upon by thousands of active users.  You can tap great support on the ArrayFire forums.
• GFOR.  You get the powerful and only GPU FOR-loop in the world.
• Multi-GPU Scalable.  You can scale from one to multiple GPUs in minutes with one simple function call.
• Graphics.  Beautiful OpenGL visualizations, adding eye-candy to your acceleration.

“We are excited to make ArrayFire free to most customers,” says John Melonakos, CEO of AccelerEyes.  “We see too many organizations frustrated by the difficulty of programming GPUs today.  ArrayFire removes that frustration, enabling GPU tire-kickers to realize the true benefits of the powerful GPU hardware.”

It is well-supported commercial software at open-source prices.  Visit our website to download the new software today!

Pricing and Availability 

ArrayFire is free for most users.  To learn more about ArrayFire licensing, visit the ArrayFire licensing page.

Try our Professional Services

AccelerEyes provides professional GPU consulting services.  Our team of engineers guarantees great results from GPU computing.  Equipped with ArrayFire and years of experience, our experts deliver results in fewer hours than any other consulting firms.  Set up a free GPU consultation today by emailing us at sales@accelereyes.com.

AccelerEyes invites you to participate in series of webinars designed to help you learn more about Jacket for MATLAB® and LibJacket for C/C++/Fortran/Python, a comprehensive library of GPU-accelerated functions.

Joint Webinar With NVIDIA: LibJacket CUDA Library

On October 20th we co-hosted a joint webinar with NVIDIA.  During this well-attended event, our GPU computing experts provided a general product overview and usage of the LibJacket CUDA library.  Several impressive demos of LibJacket in action were provided as well.  LibJacket supports hundreds of GPU computing functions and programmers in numerous industries have been able to speedup applications.  Be sure to check out the Q&A session included in the recorded webinar posted on NVIDIA’s Developer Zone. Thanks again to NVIDIA for co-hosting this informative webinar!

Graphics library: Tweaked FDTD (example in LibJacket package)

GPU Programming for Defense/Intelligence Apps: November 15, 2011 at 3:00 p.m. EST

Major defense and intelligence institutions are discovering just how effective GPU computing can be in enabling unique solutions using Jacket and LibJacket.  Come and learn how to accelerate common defense and intelligence algorithms using easy, powerful programming libraries, with Jacket for MATLAB® and LibJacket for C/C++/Fortran.

LibJacket CUDA Library for Maximus Applications:  December 15, 2011 at 3:00 p.m. EST

Learn how to integrate computations with visualizations in a CUDA-based application through simple visualization functions for plotting, image and volume rendering, and more.

Graphics library: simulating shallow water equations with reflective

The series will be conducted by AccelerEyes’ team of GPU computing experts and will include live demos of Jacket and LibJacket.   We hope you will join us as we discuss exciting developments in GPU computing software!

OpenCV is one of the most popular computer vision toolkits, and over the last year they’ve been integrating more GPU processing into the core.

One of the most common image processing tasks is convolution. Since LibJacket and OpenCV both support this, one of my coworkers rolled up his sleeves and benchmarked the latest versions from both libraries: OpenCV/CPU, OpenCV/GPU, LibJacket.

Jump over to his personal website for the full benchmark results and source code.  From the graphs, the GPU implementations from OpenCV and LibJacket both easily outperform the default CPU version in OpenCV, but notice that LibJacket pushes performance even further and dominates OpenCV’s GPU implementation, especially when using separable filters.

We’ve worked really hard the last few years to produce a reliable, high-performance commercial library and run-time.  If you’re interested in the best software for GPU computing, you might consider buying a license to save yourself time and to boost the end-to-end performance of your code.

To learn more about LibJacket, take the tour.

Researchers at SAIL (Stanford Artificial Intelligence Laborator), have done it again. They have successfully used Jacket to speed up the training part of Deep Learning algorithms. In their paper titled “On Optimization Methods for Deep Learning”, they experiment with some of the well known training algorithms and demostrate their scalability across parallel architectures (GPUs as well as multi-machine networks). The algorithms include SGDs (Stochastic Gradient Descent) L-BFGS (Limited BFGS used for solving non-linear problems), CG (Conjugate Gradient).

While SGDs are easy to implement, they require manual tuning. Add to that their sequential nature, they are hard to tune, scale and parallelize making them difficult to use with Deep Learning algorithms.  L-BFGS and CG algorithms can be harder to implement and are more computationally intensive. For speed benefits, they use approximated second order information for L-BFGS, and use the conjugacy information during the optimization step for CG. To overcome the scalability problems of L-BFGS and CG, which require gradient of the entire data set, they utilized mini-batch training which results in a faster algorithm for larger data sizes.

Following are few of the results they achieved using these algorithms for various problems. Each machine is equipped with 4 Intel CPU cores (at 2.67GHz) and a GeForce GTX 285 GPU.

Autoencoder training on the CPU

Autoencoder training on the GPU vs CPU

It can be seen that LBFGS and CG algorithms converge nearly twice as fast on the GPU compared to the CPU. SGD methods though neither improve nor degrade the performance. This behavior can be explained by the large number mini batch sizes preferred by LBFGS / CG compared to SGDs, making them more parallelized for the GPUs.

 For the next experiment, they used the algorithms for training supervised Convoluted neural networks. They then split the workload of calculating the gradient across multiple machines with GPUs.

Training Supervised CNNs on a cluster of computers (with GPUs)

As it can be seen, the rate of convergence for LBFGS scales well all the way up to 8 machines!

They also talks about other optimization techniques, usage of these algorithms for sparse auto encoding and locally connected networks, and uitility / accuracy of the the LBFGS algorithm for Deep learning.

We would like to thank Quoc V. Le, Jiquan Ngiam, Adam Coates, Abhik Lahiri, Bobby Prochnow, Andrew Y. Ng and everyone at their group for letting us use and share their work.