
Parallel Programming in the Age of Big Data - qhoxie
http://gigaom.com/2008/11/09/mapreduce-leads-the-way-for-parallel-programming/
======
jaydub
Professor Hellerstein essentially stresses the distributed nature of
MapReduce:

" it works on 'shared-nothing' clusters of computers in a data center", "the
MapReduce framework is a parallel dataflow system that works by partitioning
data across machines"

To what extent does MapReduce leverage parallelism (on a single machine)?

Would " _Distributed_ Programming in the Age of Big Data" be a more
appropriate title?

~~~
neilc
Leveraging parallelism on a single multi-core machine has a definite
resemblance to leveraging parallelism in a cluster of machines: memory access
costs are likely to be non-uniform, for example (the cores on chip X are
"closer" to memory region A than the cores on chip Y, etc.)

This paper examines Map/Reduce performance on multi-core/SMP machines:

[http://csl.stanford.edu/~christos/publications/2007.cmp_mapr...](http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf)

