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Playing or processing?

机译:玩还是处理?

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Until recently there were only two ways to speed up a volunteer-computing project: persuade more people to take part, or wait for users to upgrade their pcs to faster models. But in October 2006 the creators of Folding@home, a popular volunteer-computing project run by Stanford University that analyses protein folding, gave its users another option. It released a version of its software designed to run not on a computer's central processing unit (cpu), but on the graphics chips (gpus) found in modern pcs. The results were impressive. Precise comparisons are difficult, says Vijay Pande, Folding@home's director, but "a ten-fold speed increase is easy, a 50-fold one is possible, and you might even reach 100 if your application was particularly suitable."
机译:直到最近,只有两种方法可以加快志愿者计算项目的速度:说服更多的人参加,或者等待用户将其个人电脑升级到更快的模型。但是在2006年10月,Folding @ home的创建者为斯坦福大学的用户提供了另一个选择,Folding @ home是由斯坦福大学(Stanford University)开展的一项受欢迎的志愿者计算项目,该项目分析蛋白质折叠。它发布了其软件的版本,该软件设计为不在计算机的中央处理器(cpu)上运行,而是在现代PC上的图形芯片(gpus)上运行。结果令人印象深刻。 Folding @ home的主管Vijay Pande说,很难进行精确的比较,但是“将速度提高十倍是容易的,可以将速度提高50倍,如果您的应用程序特别合适,甚至可以达到100倍。”

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