The server cluster is designed to break up and simultaneously execute thousands of threads and tasks across servers with Intel CPU cores and Nvidia GPU cores.

Graphics cards are considered more advanced in tackling scientific and math applications than CPUs, which are designed to handle generic computing tasks. Many PCs, for example, come with separate CPUs and GPUs in which specific multimedia tasks -- like video editing -- are unloaded directly to graphics chips, leaving CPUs free to execute tasks like word processing and virus scanning.

The main job of CPUs in the cluster will be to analyze code and then off-load complex processing tasks to the GPU, said John Lee, vice president of the advanced technology solutions group at Appro. The cluster includes 304 cores with quad-core Xeon chips and 18,240 Tesla S1070 GPU cores that provide 78 teraflops of performance.

The cluster includes multiple 1U servers integrated in a 42U standard rack configuration. It is targeted mainly at institutions that need high-performance computing capabilities, like academic institutions and oil and gas exploration companies, Lee said.

In current computing environments, GPUs are highly parallel processors designed to run repetitive tasks like graphics processing, said Dean McCarron, principal analyst at Mercury Research. CPUs, on the other hand, are general-purpose in nature and can take the load off GPUs by running basic applications.