1024 Chickens Europe has turned to Skynet for farming advice. European Union agriculture commissioner Phil Hogan announced last Friday a multi-billion dollar plan to integrate digital sensors in every aspect of crop production to “produce more with less.” For instance, data from satellites could determine how best to apply fertilizers and pesticides, thus improving efficiency and crop yields. This undertaking would have been unthinkable a decade ago. But a new advance - parallel computing - has plowed through the computational challenges to make it a reality. University of Michigan professor Quentin Stout defines parallel computing as the use of multiple computer processors in combination to solve a single problem. The idea is simple on face: break up the task into small chunks which can be solved individually and then combined to produce a larger solution. Parallelism means addressing each chunk simultaneously rather than one at a time - which is much faster than a sequential approach. Parallel computing has the ability to reshape every aspect of our lives,
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A traditional computer simply executes a sequence of commands in a single order - like oxen plowing a field. But, according to Blaise Barney of the Lawrence Livermore National Laboratory, parallel computing works by partitioning a problem into multiple tasks which can be done at the same time. For example, your graphics processor may render each section of the screen separately or a signal processing program might apply multiple filters to audio data simultaneously. Think of it as if the thousand twenty four chickens each only had to plow a few feet of the field. IBM software engineer Paul McKenney notes that programming parallel software is exceedingly hard, much more difficult than straightforward sequential programs. But performance benefits outweigh the programming difficulty
A traditional computer simply executes a sequence of commands in a single order - like oxen plowing a field. But, according to Blaise Barney of the Lawrence Livermore National Laboratory, parallel computing works by partitioning a problem into multiple tasks which can be done at the same time. For example, your graphics processor may render each section of the screen separately or a signal processing program might apply multiple filters to audio data simultaneously. Think of it as if the thousand twenty four chickens each only had to plow a few feet of the field. IBM software engineer Paul McKenney notes that programming parallel software is exceedingly hard, much more difficult than straightforward sequential programs. But performance benefits outweigh the programming difficulty