With the rise of GPU computing over the last few years, it is not surprising that <a href="http://blogs.nvidia.com/2011/11/gpu-supercomputers-show-exponential-growth-in-top500-list/">more than 30</a> super computers are now powered by GPUs. Add to that Amazon ECC now offers <a href="http://aws.amazon.com/ec2/">GPU instances</a> that have two GPUs per node, it is now easy to set up a cluster of your own and run simulations using a large number of GPUs. The availability of these clusters and the proven performance of GPUs over the last couple of years has led many researchers, and businesses to move from testing GPUs to scaling their problems to multiple GPUs.

As a consequence of this,  we have a lot of our customers want to know how to scale our products across multiple GPUs. So in this post we would like to show you a brief description of what is happening when you call gpu enabled functions. We will then discuss how this knowledge can be used to scale you problems in the best possible manner.

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