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Parallel and Bi data?

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Parallel and Bi data?

Postby Monica12 » Fri May 13, 2016 10:56 am

When you look at BigData analysis you have in mind platforms like Hadoop, Mahout, or Apache Spark and Storm and the latest but not least Yahoo! SAMOA.

All those distributed computation, machine-learning (ML) systems and platforms run on distributed pcs and cloud infrastructure like Amazon EC2 or Windows Azure or something else.

You have always the option to run them on you super expensive cluster anyway, BUT, what about a Parallela cluster?
Can a Ephipany cluster get rid of BigData and to act as a distributed system?

Parallela is a low consumption amazing parallel computation machine. 5W instead of 400W. A Parallela cluster can do much more of what we think crunching numbers for computation aims.Most of these big distributed systems by ASF are both CPU-bound and I/O-bound. But they are Memory bound as well!
They can scale through the distributed cloud architecture as well.

So, if the take the Parallela board, and build our first Parallela cluster to crunch numbers and do #BigData analysis we should keep in mind that running Hadoop on Parallela, maybe it's not the right solution.

We should consider a new class of optimized software for these small server clusters since they have a remarkable computational power with restricted memory constraints.So how to start with Parallela on #BigData? Which is the right software architecture?
Monica12
 
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Re: Parallel and Bi data?

Postby Monica12 » Sat May 14, 2016 7:41 am

Parallel programs are not CPU I/O and memory bound simultaneously, unless you have been able to modify it perfectly to your hardware. Also, remember that your BigData experience is limited to 512 KB (minus 16 times the code size) on a 16-core Epiphany.
I hope this information will work.
Source: https://intellipaat.com/tutorial/hadoop-tutorial/
Monica12
 
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