Creating parallel applications with the Julia programming language
Getting Parallel
© Lead Image © Kavram, 123RF.com
Parallel processing is indispensable today – particularly in the field of natural sciences and engineering. Normal desktop users, however, can also benefit from higher performance through parallel execution with at least four calculation cores.
Programming tools such as MPI and OpenMP offer parallel processing features. It is easy to use these parallel language extensions, but using them efficiently is difficult because many algorithms cannot be rewritten for them. Languages such as Python and R also include parallel extensions, but these extensions were added on after the original language development and tend to be extremely slow when it comes to numerical calculations.
Many developers are looking for a language that is specifically designed with the intention of supporting parallel processing, and they want this parallel language to be easy to handle, with built-in features that facilitate parallelization and offer performance close to the blazing speed of C. A new language called Julia was developed to fill this niche (see the box titled "Julia Performance").
Hello Julia!
The current Julia version is available from GitHub [1]:
[...]
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