[content_band inner_container="true" no_margin="true" border="bottom" padding_top="40px" padding_bottom="40px" ]
<p style="text-align: left;">[column type="two-fifths" fade="true" fade_animation="in-from-left"][custom_headline type="left" class="mtn" level="h2"]An acceleration library for CUDA and OpenCL.[/custom_headline][gap size="12%"][button  class="x-btn-custom-type" type="flat" shape="rounded" href="#" size="jumbo" block="true"]Download ArrayFire[/button]</p>
[gap size="10%"]
<p style="text-align: left;">[button  type="flat" block="true" shape="rounded" href="#" size="jumbo" class="x-btn-custom-type"]Schedule a consultation[/button][/column][column type="three-fifths" last="true"]</p>
<p style="text-align: center;"><iframe name="wistia_embed" src="//fast.wistia.net/embed/iframe/rj2bar3xxp" height="360" width="640" allowfullscreen="" frameborder="0" scrolling="no"></iframe>[/column][gap size="5%"]</p>
[/content_band]

[content_band inner_container="true" no_margin="true" border="none" padding_top="40px" padding_bottom="40px" bg_color="transparent" ]<span style="line-height: 1.5em;">[column type="two-fifths" last="false" ] [custom_headline type="left" level="h3"]It was a pleasure to burn.[/custom_headline]ArrayFire is a fast, hardware-neutral software library for GPU computing with an easy-to-use API. Its array-based function set makes GPU programming simple. A few lines of code in ArrayFire can replace dozens of lines of raw GPU code, saving you valuable time and lowering development costs.[</span><span style="line-height: 1.5em;">[/column]</span>

[column class="center-text" type="one-half" last='true" fade="true" fade_animation="in-from-right" ][image src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/Single-glame-guy-torso-e1391782556876.png"]<span style="line-height: 1.5em;">[/column]</span>

<span style="line-height: 1.5em;">[/content_band][content_band inner_container="true" no_margin="true" border="none" padding_top="40px" padding_bottom="40px" id="CUDAandOpenCL"]</span>

[column type="one-half"][gap size="15%"]

<a href="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/CUDA-logo.jpg"><img class="aligncenter" alt="CUDA logo" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/CUDA-logo.jpg" width="231" height="140" /></a>

<img class="aligncenter" alt="OpenCL" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/OpenCLLogo_678x452.png" width="326" height="217" />

[/column][column type="one-half" last="true"]

[custom_headline type="left" level="h3"] ArrayFire for CUDA and OpenCL[/custom_headline]

ArrayFire supports CUDA-capable NVIDIA GPUs and most OpenCL devices, including AMD GPUs/APUs and Intel coprocessors. It also supports other OpenCL devices from ARM and Qualcomm.<span style="line-height: 1.5em;">[/column]</span>

[/content_band][content_band id="documentation" inner_container="true" no_margin="true" border="none" padding_top="40px" padding_bottom="40px" id="documentation" ][column type="one-half"][custom_headline type="left" level="h3"]Documentation[/custom_headline]

<strong>ArrayFire for C, C++, and Fortran</strong>
<ul>
	<li id="overview"><a href="http://www.accelereyes.com/arrayfire/c" target="_blank">CUDA &amp; OpenCL C/C++</a></li>
	<li id="overview"><a href="http://www.accelereyes.com/arrayfire/fortran" target="_blank">CUDA &amp; OpenCL Fortran</a></li>
</ul>
<strong>User Forums</strong>
Every post gets a response from ArrayFire engineers.
<ul>
	<li id="overview"><a href="http://forums.accelereyes.com/" target="_blank">User Forums</a></li>
</ul>
<strong>Introductory Demo Videos</strong>
<ul>
	<li id="overview"><a href="http://www.accelereyes.com/arrayfire_tour?idx=1" target="_blank">ArrayFire Overview</a></li>
	<li id="overview"><a href="http://www.accelereyes.com/arrayfire_tour?idx=2" target="_blank">ArrayFire Demo</a></li>
</ul>
[/column][column type="one-half" last='true" fade="true" fade_animation="in-from-right" ][image src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/gears1-e1391783830561.png"][/column][/content_band]

[content_band inner_container="true" no_margin="true" border="none" padding_top="40px" padding_bottom="40px" id="whitepapers"][column type="one-half"][/column][column type="one-half" last="true"]

[custom_headline type="left" level="h3" ]Whitepapers[/custom_headline]
<p style="text-align: center;"><img class="aligncenter  wp-image-4830" alt="Whitepaper 1" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/02/Whitepaper-1.png" width="466" height="374" /></p>
&nbsp;

[/column]
<p style="text-align: left;">[column type="one-half"]<img class="aligncenter  wp-image-4831" alt="Whitepaper 2" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/02/Whitepaper-2.png" width="442" height="304" />[/column][column type="one-half" last="true"][/content_band]</p>
[content_band inner_container="true"  no_margin="true" border="none" padding_top="40px" padding_bottom="40px" id="benchmarks" ][column type="one-half"][custom_headline type="left" level="h3" ]Benchmarks[/custom_headline]

All benchmarks were performed on a NVIDIA Tesla C2050 with an Intel i7-950 processor. The benchmarks compare ArrayFire with several popular CPU-based acceleration libraries, including <a href="http://software.intel.com/en-us/articles/intel-mkl/" target="_new">Intel Math Kernel Library</a>, <a href="http://software.intel.com/en-us/articles/intel-ipp/" target="_new">Intel Integrated Performance Primitives</a>, <a href="http://eigen.tuxfamily.org/index.php?title=Main_Page" target="_new">Eigen</a>, and <a href="http://arma.sourceforge.net/" target="_new">Armadillo</a>.[/column]

[column type="one-half" last="true" fade="true" fade_animation="in-from-right" ][image src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/Benchmark-guy-torso-e1391784056150.png"][/column]

[column type="one-fifth"][/column][column type="three-fifths"][tab_nav type="four-up" ] [tab_nav_item title="Base" active="true"] [tab_nav_item title="Linear Algebra"] [tab_nav_item title="Convolution"][tab_nav_item title="Utility"][/tab_nav] [tabs] [tab active="true"]<img class="aligncenter" alt="Matrix Multiply" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/matrix_multiply-1024x730.png" width="569" height="405" />[line]<img class="aligncenter" alt="Matrix Transpose" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/matrix_transpose-1024x730.png" width="553" height="394" />[line]<img class="aligncenter" alt="FFT" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/fft-1024x730.png" width="553" height="394" />[/tab] [tab]<img class="aligncenter" alt="LU" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/lu-1024x730.png" width="553" height="394" />[line]<img class="aligncenter" alt="SVD" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/svd-1024x730.png" width="553" height="394" />[line]<img class="aligncenter" alt="Solve" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/solve-1024x730.png" width="553" height="394" />[/tab][tab]<img class="aligncenter" alt="Convs" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/convs-1024x730.png" width="553" height="394" />[line]<img class="aligncenter" alt="Conv" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/conv-1024x730.png" width="553" height="394" />[/tab][tab]<img class="aligncenter" alt="sum" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/sum-1024x730.png" width="553" height="394" />[line]<img class="aligncenter" alt="sort" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/sort-1024x730.png" width="553" height="394" />[line]<img class="aligncenter" alt="scan" src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/scan-1024x730.png" width="553" height="394" />[/tab] [/tabs][/column][column type="one-fifth" last="true"][/column][/content_band]

<span style="line-height: 1.5em;">[content_band inner_container="true" no_margin="true" border="none" padding_top="40px" padding_bottom="40px" id="arrayfirefree"]</span>
<div id="arrayfirefree"></div>
[column type="one-half"][/column][column type="one-fourth"][custom_headline type="center" level="h3"]ArrayFire Free[/custom_headline][/column][column type="one-fourth" last="true"][/column][column type="one-fourth"  fade="true" fade_animation="in-from-right" ][image src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/right-leaning-e1391788779101.png"][/column][column type="one-fourth"]
<table>
<tbody>
<tr>
<td>[custom_headline type="center" level="h5"]Functions[/custom_headline]</td>
</tr>
<tr>
<td>[icon type="check-circle"] Basic Operations</td>
</tr>
<tr>
<td>[icon type="check-circle"] Arithmetic</td>
</tr>
<tr>
<td>[icon type="check-circle"] Signal Processing</td>
</tr>
<tr>
<td>[icon type="check-circle"] Image Processing</td>
</tr>
<tr>
<td>[icon type="check-circle"] Statistics</td>
</tr>
<tr>
<td>[icon type="check-circle"] Visualization</td>
</tr>
</tbody>
</table>
[/column][column type="one-fourth"]
<table>
<tbody>
<tr>
<td>[custom_headline type="center" level="h5"]Hardware[/custom_headline]</td>
</tr>
<tr>
<td>[icon type="check-circle"] CUDA</td>
</tr>
<tr>
<td>[icon type="check-circle"] OpenCL</td>
</tr>
<tr>
<td>[custom_headline type="center" level="h5"]Languages[/custom_headline]</td>
</tr>
<tr>
<td>[icon type="check-circle"] C/C++</td>
</tr>
<tr>
<td>[icon type="check-circle"] Fortran</td>
</tr>
<tr>
<td>[custom_headline type="center" level="h5"]Advanced[/custom_headline]</td>
</tr>
<tr>
<td> [icon type="check-circle"] GFOR Loop</td>
</tr>
</tbody>
</table>
[/column][column type="one-fourth" last="true"]
<table>
<tbody>
<tr>
<td>[custom_headline type="center" level="h5"]Platforms[/custom_headline]</td>
</tr>
<tr>
<td>[icon type="check-circle"] Windows</td>
</tr>
<tr>
<td>[icon type="check-circle"] Linux</td>
</tr>
<tr>
<td>[icon type="check-circle"] Mac</td>
</tr>
<tr>
<td>[custom_headline type="center" level="h5"]Data Types[/custom_headline]</td>
</tr>
<tr>
<td>[icon type="check-circle"] All types</td>
</tr>
<tr>
<td>[custom_headline type="center" level="h5"]Support[/custom_headline]</td>
</tr>
<tr>
<td> [icon type="check-circle"] Forums</td>
</tr>
</tbody>
</table>
[/column][/content_band][content_band inner_container="true" no_margin="true" border="none" padding_top="40px" padding_bottom="40px" id="arrayfirepro"]
<div id="arrayfirepro"></div>
[pricing_table  columns="3"][pricing_table_column  featured="false" title="ArrayFire Developer" currency="$" price="499 / year" interval="$149 / year (Academic)"] [icon_list] [icon_list_item type="star"] Dedicated license [/icon_list_item] [icon_list_item  type="times-circle"] Starts at 2 GPUs or devices [/icon_list_item] [icon_list_item type="times-circle"] Linear algebra [/icon_list_item] [icon_list_item type="times-circle"] Sparse matrices [/icon_list_item][icon_list_item type="times-circle"] Phone support [/icon_list_item][icon_list_item type="times-circle"] Premium support &amp; consulting [/icon_list_item][/icon_list] [button href="#"] Purchase [/button] [/pricing_table_column] [pricing_table_column featured="true" featured_sub="Most Popular" title="ArrayFire Pro" currency="$" price="1,499 / year" interval="$525 / year (Academic)"] [icon_list] [icon_list_item type="star"] Dedicated license [/icon_list_item] [icon_list_item type="star"] Starts at 2 GPUs or devices [/icon_list_item] [icon_list_item type="star"] Linear algebra [/icon_list_item][icon_list_item type="star"] Sparse matrices [/icon_list_item][icon_list_item type="star"] Phone support [/icon_list_item][icon_list_item type="times-circle"] Premium support &amp; counsulting [/icon_list_item] [/icon_list] [button href="#"]Purchase [/button] [/pricing_table_column] [pricing_table_column featured="false" title="ArrayFire Enterprise" currency="$" price="2,999 / year" interval="$1,049 / year (Academic)"] [icon_list] [icon_list_item type="star"] Dedicated license [/icon_list_item] [icon_list_item type="star"] Starts at 2 GPUs or devices [/icon_list_item] [icon_list_item type="star"] Linear algebra [/icon_list_item][icon_list_item type="star"]Sparse matrices [/icon_list_item][icon_list_item type="star"] Phone support[/icon_list_item][icon_list_item type="star"]Premium support &amp; consulting [/icon_list_item][/icon_list] [button href="#"] Purchase [/button] [/pricing_table_column] [/pricing_table] [/content_band]

[content_band inner_container="true" no_margin="true" border=" none" padding_top="40px" padding_bottom="40px" id="consulting"]
<div id="consulting"></div>
[column type="one-half"  fade="true" fade_animation="in-from-right" ][image src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/Bat-signal.png"][/column][column type="one-half" last="true"]

[custom_headline type="left" level="h3"  ] GPU Consulting Services [/custom_headline]

ArrayFire provides GPU coding services. Hire us to accelerate your code.

We've already helped thousands of organizations speedup their code. We've accelerated all sorts of algorithms, data sets, and challenging problems in the past and we're sure that we can tackle your code too. [/column]

[/content_band][content_band inner_container="true" no_margin="true" border="none" padding_top="40px" padding_bottom="40px" id="starter"]
<div id="starter"></div>
[column type="one-half" last="false"]

[custom_headline type="left" level="h3"] Starter Package ($500)¹ [/custom_headline]

Hire us for 1-day. We set you up with one engineer dedicated for a full day, as well as support from senior and test engineers throughout the day. Here's how it works:
<ul>
	<li>Initial phone call (click "Talk to Us" below to get started)</li>
	<li>You send us your code or algorithm</li>
	<li>We analyze your code for potential GPU benefit</li>
	<li>We accelerate as much of the code as is possible within the 1-day effort, applying our extensive GPU software libraries</li>
	<li>We produce a final report, along with code</li>
</ul>
The final report outlines the best approach to follow for full GPU-acceleration of your code.
<h6>¹ Limit one (1) per customer.[/column][column type="one-half" last="true" fade="true" fade_animation="in-from-right" ][image src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/comingatyou.png"][/column][/content_band]</h6>
[content_band inner_container="true" no_margin="true" border="none" padding_top="40px" padding_bottom="40px" id="customcoding"]
<div id="customcoding"></div>
[column type="one-half"  fade="true" fade_animation="in-from-right" ][image src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/Hunched-over.png"][/column][column type="one-half" last="true"]

[custom_headline type="left" level="h3" ] Coding Work (Custom) [/custom_headline]

Hire us immediately to accelerate your code.

Why Choose Us?
<ul>
	<li>Deep experience, already servicing thousands of customers</li>
	<li>Quality work, with leading GPU software</li>
	<li>Efficient delivery, with ArrayFire's extensive code base we can deliver results fast</li>
	<li>Proven, we make customers happy through faster code</li>
</ul>
Our Strengths
<ul>
	<li>Excellence in GPU code</li>
	<li>Well-written, efficient C, C++, Fortran, Java, or .NET</li>
	<li>Application porting and integration</li>
	<li>Tuning software to peak hardware performance</li>
	<li>Scalability of algorithms to handle enterprise-level programs</li>
</ul>
<span style="line-height: 1.5em;">[/column][/content_band]</span>

[content_band inner_container="true" no_margin="true" border="none" padding_top="40px" padding_bottom="40px" id="customcoding"][column type="one-half"][custom_headline type="left" level="h3"  ]The ArrayFire Blog[/custom_headline][/column][column type="one-half" last="true" fade="true" fade_animation="in-from-right" ][image src="http://sandbox.arrayfire.com/wp-content/uploads/2014/01/Hunched-over.png"][/column][/content_band]

[content_band inner_container="true" no_margin="true" border="none" padding_top="40px" padding_bottom="40px" id="customcoding"][column type="one-half"][/column][column type="one-half" last="true"][custom_headline type="left" level="h3" ]Training[/custom_headline]

<span style="line-height: 1.5em;">Attendees will receive the latest industry knowledge and techniques for GPU computing in CUDA and OpenCL. Check out our [extra href="#example" title="Training Course Syllabus" info="popover" info_place="top" info_trigger="click" info_content="</span>

&nbsp;
<h5>DAY 1, INTRODUCTION</h5>
&nbsp;

<strong>Lectures:</strong>
Parallel Computing Overview
Programming Models
Architectures Supported
Basic Dataset Mapping Techniques
Acceleration Libraries, ArrayFire
Asynchronos Operation
Portability

&nbsp;

<strong>Practice:</strong>
A Simple Kernel
Equivalent ArrayFire Example
Monte Carlo Pi Estimation
Using Libraries
Timing
Porting Code for Multiple Devices
<h5>DAY 3, SCALING</h5>
&nbsp;

<strong>Lectures:</strong>
Multi-Device Use Cases
Heterogeneous Computing
Scaling Across Multiple Devices

&nbsp;

<strong>Practice:</strong>
Out of Core Problems: Matrix Multiply
Task Level Parallelism: Optimization
ArrayFire Multi-Device
<h5>DAY 2, OPTIMIZATION</h5>
<strong>Lectures:
</strong>Architecture
Memory Models
Advanced Mapping Techniques
Architecture Specific Limitations and Optimizations
ArrayFire: Lazy Evaluation and Code Vectorization

&nbsp;

<strong>Practice:
</strong>Matrix Transpose
Optimization Using Shared Memory
Median Filter
Optimization Using Constant Memory
Command Queues Example
ArrayFire Example: Nearest Neighbor Algorithm

&nbsp;
<h5>DAY 4, ALGORITHM PROBLEMS</h5>
&nbsp;

<strong>Lectures and Practice:
</strong>Reductions
Scan Algorithms
Sort
Convolution
Customer-Specific Problem"]syllabus[/extra]!

ArrayFire offers up to four days of specialized GPU training in CUDA and OpenCL programming. Register for a general course, or schedule a course dedicated for your organization. We can travel to you, or you can travel to our Atlanta office. We recommend that attendees have a working knowledge of C/C++ in order to gain the most from the training courses.[/column][column type="one-third" last="false"]

<strong>Fee includes:</strong>
<ul>
	<li>Instruction by an excellent and interesting expert. Many hands-on exercises.</li>
	<li>Use of a laptop with CUDA and OpenCL capable GPUs and CPUs</li>
	<li>Choice of Linux or Windows operating system</li>
	<li>Printed manual of lecture material</li>
	<li>Electronic copy of programming exercises</li>
	<li>Certificate of Completion[/column][column type="one-third" last="true"][/column]</li>
</ul>
[column type="one-half"][/column][column type="one-half" last="true"]

[custom_headline type="left" level="h3" looks_like="h3 ]Upcoming Courses[/custom_headline]

<strong>CUDA</strong>

<strong>OpenCL</strong>

<span style="line-height: 1.5em;">[/column][/content_band]</span>