Intel's powerful new Xeon Phi co-processor
In the high-performance computing field, an increasing number of users have turned to GPU computing, wherein a host computer copies data to the graphics card, which then returns a result.
This procedure is especially helpful for applications that repeatedly run the same operation against a large volume of data. A GPU can play to its strength, performing a large number of computations, each of which processes one data element. GPUs can process some types of calculations (such as mining bitcoins) orders of magnitude more efficiently than CPUs.
This performance advantage comes at a price. The programming model, and thus the programming procedure, differs fundamentally from that of CPUs. As a consequence, existing programs cannot run directly on GPUs. Although the OpenCL parallel programming framework tries to hide and abstract as many of these differences as possible, developers still need to be aware of the differences between coding for CPUs and GPUs.
[...]
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