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[custom_headline type="left" level="h3"]If you want faster code, you've come to the right place.[/custom_headline][gap size="15%"][button  type="flat" shape="rounded" href="http://sandbox.arrayfire.com/download" size="x-large" block="true"]Download ArrayFire[/button]

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[button  type="flat" block="true" shape="rounded" href="http://www.meetme.so/arrayfire" size="x-large" ]Schedule a consultation[/button]

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[content_band inner_container="true" no_margin="true" border="top" padding_top="40px" padding_bottom="40px"][column type="one-half"][custom_headline type="left" level="h3"]Faster code[/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.

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[column type="one-third" class="center-text"][custom_headline type="left" level="h3"]Faster code[/custom_headline][line]Video content analysis is the basis for categorizing videos and enabling search by content. Google used ArrayFire to extract motion features in video, improving performance by substantially accelerating the solution of the L1-regularized least-squares optimization problem.[/column][column type="one-third" class="center-text"][custom_headline type="left" level="h3"]Open Source[/custom_headline][line]Andrew Shin, Market Risk Manager of Koch Supply &amp; Trading, achieves significant performance increases on option pricing algorithms using ArrayFire software, "My buddy and I are, at best, novice programmers and we couldn't imagine having to figure out how to code all this in CUDA."[/column][column type="one-third" last="true" class="center-text"][custom_headline type="left" level="h3"]Hardware Neutral[/custom_headline][line]The main thrust of this research was improving Mars rover image compression via GPUs and genetic algorithms. With ArrayFire software and GPUs, the researchers were able to achieve 5X speedups on the larger data sizes.[/column]
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