When using Titanium parallel Java dialect

Titan is an explicitly parallel Java dialect developed at UC Berkeley to support high performance scientific computing on large-scale multiprocessors, including massively parallel supercomputers and distributed memory clusters with one or more processors per node [ berkley ].

The link is probably one of the few sources that says something about the project. A search on this site (stackoverflow) can hardly say anything about this seemingly new parallel Java dialect.

My question is:

  • Do you use this language to develop parallel applications?
  • What is your experience of developing a parallel application using Titanium versus - say Hadoop MapReduce
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  • As countless Linux distributions, the Titanium "framework" was built with the same role: tailored to the creator's need , while there weren't many choices at the time.
  • The project is almost 20 years old (started around '96 -98), so given how much technology has evolved over the last couple of years, it is clear that while the concepts they used are still to be found, the implementation is not as modern.
  • So, back to the first point. This is not a criticism or judgment about the creation of UC Berkeley, because he had made a great effort, but any programmer that cares about their projects, will try to maintain and use the latest technology available to software, such as Apache Hadoop MapReduce

    , the Apache , which is known for which creates frameworks to meet the needs of the programmer . A good day!


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No, I do not use this language. And I tell you that it is better to use what is on the boom right now. try GridGain



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I can wander for buttons with the best of you ...

Not; that Titan has never escaped from the research lab, and it is not widely used, if at all developed for a parallel application. You seem to think Titan is new, but work on it began in the second half of the 1990s; since then an eon has passed in terms of languages ​​and systems of parallel programming. If anyone on the planet compares Titans to Hadoop / MapReduce correctly, I'll eat one of my hats

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