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The following page has been changed by udanax:
http://wiki.apache.org/hama/Architecture

'''NOTE:'''
 * Sparse matrix operations cannot be optimized.
+ * Sparse matrix operations cannot be optimized
* Sparse structures which are growable can exceed the initial bandwidth allocation, while
those which are not growable are fixed, and overallocation will cause an error
 * Matrices which are column major typically perform better with columnoriented operations,
and likewise for row major matrices. Matrix/vector multiplication is rowmajor, while transpose
multiplication is columnmajor.
+ * Matrices which are column major typically perform better with columnoriented operations,
and likewise for row major matrices. Matrix/vector multiplication is rowmajor, while transpose
multiplication is columnmajor
== Why sparse matrices? ==
@@ 24, +24 @@
== Storage of sparse matrices ==
 I choosed HBase(sparse matrix storage) to reduce storage and complexity.
+ We choosed HBase which columnoriented sparse table storage to reduce storage and complexity.
* Hama use columnoriented storage of matrices (HBase) , and so compressed column format
is a natural choice of sparse storage
* Hama forces the elements of each column to be stored in increasing order of their row
index
@@ 37, +37 @@
See also: [http://labs.google.com/papers/bigtableosdi06.pdf Bigtable], A Distributed Storage
System for Structured Data
 == Sparse Functions ==

 === Manipulate sparse matrices ===
 <bgcolor="#ececec">'''Function''' <bgcolor="#ececec">'''Explanation''' 
 nnz <99%>returns '''number of non zero''' elements in SM
 nonzeros <99%>Returns a vector of the '''nonzero''' values of the sparse matrix
S

 === Linear Algebra ===

 <bgcolor="#ececec">'''Function''' <bgcolor="#ececec">'''Explanation''' 
 spdetermin <99%>Compute the '''determinant''' of sparse matrix A
 spcholesky <99%>Compute the Cholesky factor, '''R''', of the symmetric positive
definite
 splcholesky <99%>Compute the Cholesky factor, '''L''', of the symmetric positive
definite
 splu <99%>Compute the '''LU''' decomposition of the sparse matrix A

 === Iterative techniques ===

