Programming languages on the rise: CUDA extensions
As libraries for programming video cards to do massively parallel jobs, CUDA extensions are not technically a language; they're just extensions to C. Still, some enterprise programmers are beginning to unlock the massively parallel architectures normally devoted to rendering realistic blood splattering in alternative game worlds. Moreover, recoding loops for massive parallelism means rethinking many of the idioms from basic C or C++ programming, making CUDA extensions all the more valuable.
Opportunities to tape CUDA extensions include machine vision, massive simulations, and huge statistical computations. Many problems of data analysis are naturally massively parallel, making GPU processors worth a look. One of Nvidia's recent conferences devoted to CUDA applications included separate tracks devoted to computational fluid dynamics, computer vision, databases and data mining, finance, and molecular dynamics. That list alone is long enough to explain why big enterprise coders are curious.
"It's clear that the GPU reached escape velocity," Dan Vivoli, senior vice president at Nvidia, said at the Nvidia conference after scientists had presented papers on how the GPU's parallelism can work in these domains. "The processor is now reaching all different disciplines of science and industry."
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