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From Apache Wiki <wikidi...@apache.org>
Subject [Hadoop Wiki] Trivial Update of "Hama" by udanax
Date Wed, 05 Mar 2008 13:50:08 GMT
Dear Wiki user,

You have subscribed to a wiki page or wiki category on "Hadoop Wiki" for change notification.

The following page has been changed by udanax:
http://wiki.apache.org/hadoop/Hama

------------------------------------------------------------------------------
  [http://wiki.apache.org/hadoop-data/attachments/Hama/attachments/hama-medium.png]
+ 
+  * I'm looking for champion/mentor who can leads the proposal process.
  
  == Introduction ==
  '''Hama''' is a parallel matrix computational package based on Hadoop Map/Reduce. ''(Hama
is in korean, which means 'Hippo').'' It will be useful for a massively large-scale ''Numerical
Analysis'' and ''Data Mining'', which need the intensive computation power of matrix inversion,
e.g. linear regression, PCA, SVM and etc. It will be also useful for many scientific applications,
e.g. physics computations, linear algebra, computational fluid dynamics, statistics, graphic
rendering and many more.
@@ -9, +11 @@

  === Initial Contributors ===
   * [:udanax:Edward Yoon] (R&D center, NHN corp.) 
   * Chanwit Kaewkasi (Ph.D candidate, University of Manchester)
+ === Initial Source ===
+  * http://code.google.com/p/hama/source/checkout
  === Dependencies ===
   * Hadoop (HDFS, Map/Reduce) License: Apache License, 2.0 
   * Hbase (Sparse Matrix Table) License: Apache License, 2.0 
@@ -42, +46 @@

    def c = a * b
  }}}
  ----
+ == The parallel time complexity of Hama ==
+ === Addition/Substraction ===
+  * The matrix add/sub requires table full scan twice.
+  * The total time spent by these operation is given by O(n^2^/mappers).
+ 
+ === Multiplication ===
+  * The Multiplication requires (n + 1) table full scan irrespective of the number of mapper.
+  * Each map processor requires O(n^2^) for the communication and O(n^3^/mappers) the computation.

+ ----
  == References ==
   * ScaLAPACK, a library of high-performance linear algebra routines for distributed-memory
message-passing MIMD computers 
   * Scheduling algorithms for parallel Gaussian elimination withcommunication costs, Amoura,
A.K.; Bampis, E.; Konig, J.-C. 

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