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The following page has been changed by edwardyoon:
http://wiki.apache.org/hadoop/Hamburg

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   * Computation on data that reside in local storage; it is similar to map 
operation in M/R.
   * Each node communicates its necessary data into one another.
   * All processors synchronize which waits for all of the communications 
actions to complete.
- The main difference between Hamburg and M/R is that Hamburg does not make 
intermediate data aggregate into reducer. Instead, each computation node 
communicates only necessary data into one another. 
- It will be efficient if total communicated data is smaller then intermediate 
data to be aggregated into reducers.
+ 
+ Let's see more detail in the diagram of computing method of Hamburg based on 
BSP model.
+ 
+ 
[http://lh4.ggpht.com/_DBxyBGtfa3g/SmQUYTHWooI/AAAAAAAABmk/cFVlLCdLVHE/s800/figure1.PNG]
+ 
+ Each worker will process the data fragments stored locally. And then, We can 
do bulk synchronization using collected communication data. The 'Computation' 
and 'Bulk synchronization' can be performed iteratively, Data for 
synchronization can be compressed to reduce network usage. The main difference 
between Hamburg and M/R is that Hamburg does not make intermediate data 
aggregate into reducer. Instead, each computation node communicates only 
necessary data into one another.  It will be efficient if total communicated 
data is smaller then intermediate data to be aggregated into reducers. Plainly, 
It aims to improve the performance of traverse operations in Graph computing. 
+ 
+ For example, to explores all the neighboring nodes from the root node using 
Map/Reduce (FYI, 
[http://blog.udanax.org/2009/02/breadth-first-search-mapreduce.html 
Breadth-First Search (BFS) & MapReduce]), We need a lot of iterations to get 
next vertex per-hop time.
+ 
+ Let's assume the graph looks like presented below:
+ 
+ 
[http://lh5.ggpht.com/_DBxyBGtfa3g/SmQTwhOSGwI/AAAAAAAABmY/ERiJ2BUFxI0/s800/figure2.PNG]
  
  === Initial contributors ===
   * Edward J. (edwardyoon AT apache.org)

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