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From gsing...@apache.org
Subject svn commit: r883365 [31/47] - in /lucene/mahout/trunk: ./ examples/ matrix/ matrix/src/ matrix/src/main/ matrix/src/main/java/ matrix/src/main/java/org/ matrix/src/main/java/org/apache/ matrix/src/main/java/org/apache/mahout/ matrix/src/main/java/org/a...
Date Mon, 23 Nov 2009 15:14:38 GMT
Added: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/bench/doc-files/usage_dgemm.txt
URL: http://svn.apache.org/viewvc/lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/bench/doc-files/usage_dgemm.txt?rev=883365&view=auto
==============================================================================
--- lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/bench/doc-files/usage_dgemm.txt (added)
+++ lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/bench/doc-files/usage_dgemm.txt Mon Nov 23 15:14:26 2009
@@ -0,0 +1,11 @@
+Arguments to be supplied:
+	<operation> <type> <cpus> <minSecs> <density> <transposeA> <transposeB> {sizes}
+where
+	operation = the operation to benchmark; in this case: dgemm
+	type = matrix type to be used; e.g. dense, sparse or rowCompressed
+	cpus = #cpus available; e.g. 1 or 2 or ...
+	minSecs = #seconds each operation shall at least run; e.g. 2.0 is a good number giving realistic timings
+	density = the density of the matrices to be benchmarked; e.g. 0.999 is very dense, 0.001 is very sparse
+	transposeA = false or true
+	transposeB = false or true
+	sizes = a list of problem sizes; e.g. 100 200 benchmarks squared 100x100 and 200x200 matrices

Propchange: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/bench/doc-files/usage_dgemm.txt
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Added: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/bench/package.html
URL: http://svn.apache.org/viewvc/lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/bench/package.html?rev=883365&view=auto
==============================================================================
--- lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/bench/package.html (added)
+++ lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/bench/package.html Mon Nov 23 15:14:26 2009
@@ -0,0 +1,5 @@
+<HTML>
+<BODY>
+Matrix benchmarks.
+</BODY>
+</HTML>

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Added: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/PerformanceLogFrame.html
URL: http://svn.apache.org/viewvc/lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/PerformanceLogFrame.html?rev=883365&view=auto
==============================================================================
--- lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/PerformanceLogFrame.html (added)
+++ lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/PerformanceLogFrame.html Mon Nov 23 15:14:26 2009
@@ -0,0 +1,15 @@
+<html>
+<head>
+  <title>Untitled Document</title>
+  <meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
+</head>
+
+<frameset rows="1800,0" cols="*">
+  <frame src="performanceLog.html">
+</frameset>
+<noframes>
+  <body bgcolor="#FFFFFF">
+
+  </body>
+</noframes>
+</html>
\ No newline at end of file

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Added: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/allColt1.0.1ibm1.3LxPIII.txt
URL: http://svn.apache.org/viewvc/lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/allColt1.0.1ibm1.3LxPIII.txt?rev=883365&view=auto
==============================================================================
--- lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/allColt1.0.1ibm1.3LxPIII.txt (added)
+++ lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/allColt1.0.1ibm1.3LxPIII.txt Mon Nov 23 15:14:26 2009
@@ -0,0 +1,301 @@
+Colt Matrix benchmark running on
+
+java.vm.vendor  IBM Corporation    
+java.vm.version 1.3.0              
+java.vm.name    Classic VM         
+os.name         Linux              
+os.version      2.2.12-20smp       
+os.arch         x86                
+java.version    1.3.0              
+java.vendor     IBM Corporation    
+java.vendor.url http://www.ibm.com/
+
+Colt Version is Version 1.0.1.51 (Mon May 15 20:23:08 CEST 2000)
+Please report problems to wolfgang.hoschek@cern.ch or http://nicewww.cern.ch/~hoschek/colt/index.htm
+
+
+
+Executing command = [dgemm, dense, 2, 2, 0.999, false, true, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-matrix mult dgemm(false, true, 1, A, B, 0, C)
+type=dense
+        | size
+        | 5      10     25      50      100     250     500     1000   
+-----------------------------------------------------------------------
+d 0.999 | 36.528 82.766 112.261 157.978 198.849 212.999 204.165 175.932
+Run took a total of Time=37.602 secs. End of run.
+
+
+Executing command = [dgemm, dense, 1, 2, 0.999, false, true, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-matrix mult dgemm(false, true, 1, A, B, 0, C)
+type=dense
+        | size
+        | 5      10     25      50      100     250     500     1000  
+----------------------------------------------------------------------
+d 0.999 | 36.213 74.518 102.758 105.095 119.012 116.333 102.124 87.409
+Run took a total of Time=49.605 secs. End of run.
+
+
+Executing command = [dgemm, rowCompressed, 1, 2, 0.01, false, false, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-matrix mult dgemm(false, false, 1, A, B, 0, C)
+type=rowCompressed
+       | size
+       | 5      10      25      50         100       250        500        1000      
+-------------------------------------------------------------------------------------
+d 0.01 | 32.525 138.719 703.833 1.845E+003 3.27E+003 2.848E+003 2.772E+003 2.951E+003
+Run took a total of Time=32.202 secs. End of run.
+
+
+Executing command = [dgemm, sparse, 1, 2, 0.01, false, false, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-matrix mult dgemm(false, false, 1, A, B, 0, C)
+type=sparse
+       | size
+       | 5      10      25      50         100       250        500        1000      
+-------------------------------------------------------------------------------------
+d 0.01 | 31.128 135.907 637.538 1.767E+003 3.15E+003 2.751E+003 2.817E+003 2.883E+003
+Run took a total of Time=32.277 secs. End of run.
+
+
+Executing command = [dgemv, dense, 2, 2, 0.01, false, 5, 10, 25, 50, 100, 250, 500, 1000, 2000] ...
+
+@x.x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-vector mult dgemv(false, 1, A, B, 0, C)
+type=dense
+       | size
+       | 5      10     25      50      100     250     500    1000   2000  
+---------------------------------------------------------------------------
+d 0.01 | 20.828 52.811 103.611 104.354 109.498 101.451 67.187 66.341 54.554
+Run took a total of Time=39.415 secs. End of run.
+
+
+Executing command = [dgemv, dense, 1, 2, 0.01, false, 5, 10, 25, 50, 100, 250, 500, 1000, 2000] ...
+
+@x.x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-vector mult dgemv(false, 1, A, B, 0, C)
+type=dense
+       | size
+       | 5      10    25      50      100     250    500    1000   2000  
+-------------------------------------------------------------------------
+d 0.01 | 30.574 65.79 114.877 101.596 111.633 47.494 31.702 31.778 31.793
+Run took a total of Time=40.283 secs. End of run.
+
+
+Executing command = [dgemv, rowCompressed, 1, 2, 0.01, false, 5, 10, 25, 50, 100, 250, 500, 1000, 2000] ...
+
+@x.x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-vector mult dgemv(false, 1, A, B, 0, C)
+type=rowCompressed
+       | size
+       | 5      10      25      50         100       250        500        1000       2000      
+------------------------------------------------------------------------------------------------
+d 0.01 | 31.796 145.363 527.966 1.143E+003 2.33E+003 3.737E+003 4.776E+003 5.992E+003 3.448E+003
+Run took a total of Time=41.138 secs. End of run.
+
+
+Executing command = [dgemv, sparse, 1, 2, 0.01, false, 5, 10, 25, 50, 100, 250, 500, 1000, 2000] ...
+
+@x.x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-vector mult dgemv(false, 1, A, B, 0, C)
+type=sparse
+       | size
+       | 5      10     25      50      100     250     500     1000    2000   
+------------------------------------------------------------------------------
+d 0.01 | 23.507 84.216 341.435 598.555 717.508 733.817 768.899 756.811 573.721
+Run took a total of Time=38.784 secs. End of run.
+
+
+Executing command = [assign, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.assign(B) [Mops/sec]
+type=dense
+        | size
+        | 5      10      25      50    100    250    500    1000 
+-----------------------------------------------------------------
+d 0.999 | 79.408 164.319 309.899 35.58 44.551 20.849 21.326 21.78
+Run took a total of Time=36.992 secs. End of run.
+
+
+Executing command = [assignGetSet, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.assign(B) via get and set [Mops/sec]
+type=dense
+        | size
+        | 5     10    25    50    100   250  500   1000 
+--------------------------------------------------------
+d 0.999 | 8.567 7.898 8.028 7.567 7.823 5.57 5.289 5.275
+Run took a total of Time=32.556 secs. End of run.
+
+
+Executing command = [assignGetSetQuick, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.assign(B) via getQuick and setQuick [Mops/sec]
+type=dense
+        | size
+        | 5      10     25    50    100    250   500   1000 
+------------------------------------------------------------
+d 0.999 | 11.381 11.172 11.51 10.19 10.943 7.344 7.516 7.495
+Run took a total of Time=32.857 secs. End of run.
+
+
+Executing command = [elementwiseMult, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.assign(F.mult(0.5)) via Blas [Mflops/sec]
+type=dense
+        | size
+        | 5      10     25     50     100    250    500    1000 
+----------------------------------------------------------------
+d 0.999 | 34.886 64.726 94.636 43.634 52.731 25.993 22.014 22.75
+Run took a total of Time=37.829 secs. End of run.
+
+
+Executing command = [elementwiseMultB, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.assign(B,F.mult) via Blas [Mflops/sec]
+type=dense
+        | size
+        | 5      10     25     50     100    250    500    1000  
+-----------------------------------------------------------------
+d 0.999 | 44.274 55.451 60.475 35.507 26.752 14.646 14.925 14.599
+Run took a total of Time=34.514 secs. End of run.
+
+
+Executing command = [assignLog, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A[i,j] = log(A[i,j]) via Blas.assign(fun) [Mflops/sec]
+type=dense
+        | size
+        | 5     10    25    50    100   250   500   1000 
+---------------------------------------------------------
+d 0.999 | 4.697 4.703 4.777 4.694 4.804 4.333 4.171 4.204
+Run took a total of Time=33.089 secs. End of run.
+
+
+Executing command = [assignLog, dense, 2, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A[i,j] = log(A[i,j]) via Blas.assign(fun) [Mflops/sec]
+type=dense
+        | size
+        | 5     10    25    50    100   250   500   1000 
+---------------------------------------------------------
+d 0.999 | 4.351 4.727 4.881 4.775 4.894 7.295 6.991 7.246
+Run took a total of Time=32.783 secs. End of run.
+
+
+Executing command = [assignPlusMult, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A[i,j] = A[i,j] + s*B[i,j] via Blas.assign(fun) [Mflops/sec]
+type=dense
+        | size
+        | 5      10     25     50     100    250    500    1000  
+-----------------------------------------------------------------
+d 0.999 | 52.745 79.516 94.163 74.865 44.036 31.974 29.661 31.826
+Run took a total of Time=34.536 secs. End of run.
+
+
+Executing command = [SOR5, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.zAssign8Neighbors(5 point function) [Mflops/sec]
+type=dense
+        | size
+        | 5       10     25     50     100   250   500    1000 
+---------------------------------------------------------------
+d 0.999 | 117.043 74.677 59.492 55.529 53.61 38.48 39.857 37.43
+Run took a total of Time=32.801 secs. End of run.
+
+
+Executing command = [SOR8, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.zAssign8Neighbors(9 point function) [Mflops/sec]
+type=dense
+        | size
+        | 5       10     25     50     100    250    500    1000  
+------------------------------------------------------------------
+d 0.999 | 128.176 82.446 64.465 55.579 55.606 42.713 43.439 42.849
+Run took a total of Time=32.433 secs. End of run.
+
+
+Executing command = [LUDecompose, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of LU.decompose(A) [Mflops/sec]
+type=dense
+        | size
+        | 5     10     25     50     100    250    500    1000  
+----------------------------------------------------------------
+d 0.999 | 3.482 10.428 27.323 51.485 67.178 88.586 35.401 30.844
+Run took a total of Time=47.515 secs. End of run.
+
+
+Executing command = [LUSolve, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of LU.solve(A) [Mflops/sec]
+type=dense
+        | size
+        | 5      10     25    50     100    250    500    1000  
+----------------------------------------------------------------
+d 0.999 | 12.488 30.537 66.43 78.551 73.841 56.787 26.843 25.915
+Run took a total of Time=134.412 secs. End of run.
+
+
+Executing command = [pow, dense, 1, 2, 0.999, -1, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of matrix to the power of an exponent pow(A,-1)
+type=dense
+        | size
+        | 5     10     25     50     100    250    500    1000  
+----------------------------------------------------------------
+d 0.999 | 5.407 15.041 41.556 60.984 70.523 63.082 30.962 28.746
+Run took a total of Time=127.618 secs. End of run.
+
+
+Command file name used: /afs/cern.ch/user/h/hoschek/coltb4/cmd2
+To reproduce and compare results, here it's contents:
+// matrix-matrix mult with 1 and with 2 CPUs
+dgemm dense 2 2.0 0.999 false true 5 10 25 50 100 250 500 1000
+dgemm dense 1 2.0 0.999 false true 5 10 25 50 100 250 500 1000
+dgemm rowCompressed 1 2.0 0.01 false false 5 10 25 50 100 250 500 1000
+dgemm sparse 1 2.0 0.01 false false 5 10 25 50 100 250 500 1000
+
+// matrix-vector mult
+dgemv dense 2 2.0 0.01 false 5 10 25 50 100 250 500 1000 2000
+dgemv dense 1 2.0 0.01 false 5 10 25 50 100 250 500 1000 2000
+dgemv rowCompressed 1 2.0 0.01 false 5 10 25 50 100 250 500 1000 2000
+dgemv sparse 1 2.0 0.01 false 5 10 25 50 100 250 500 1000 2000
+
+assign dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+assignGetSet dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+assignGetSetQuick dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+
+// with 1 and with 2 CPUs
+elementwiseMult dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+elementwiseMultB dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+assignLog dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+assignLog dense 2 2.0 0.999 5 10 25 50 100 250 500 1000
+assignPlusMult dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+assignPlusMult dense 2 2.0 0.999 5 10 25 50 100 250 500 1000
+
+SOR5 dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+SOR8 dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+
+LUDecompose dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+LUSolve dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+pow dense 1 2.0 0.999 -1 5 10 25 50 100 250 500 1000 

Propchange: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/allColt1.0.1ibm1.3LxPIII.txt
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Added: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/dgemmColt1.0.1ibm1.3LxPIII_1.txt
URL: http://svn.apache.org/viewvc/lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/dgemmColt1.0.1ibm1.3LxPIII_1.txt?rev=883365&view=auto
==============================================================================
--- lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/dgemmColt1.0.1ibm1.3LxPIII_1.txt (added)
+++ lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/dgemmColt1.0.1ibm1.3LxPIII_1.txt Mon Nov 23 15:14:26 2009
@@ -0,0 +1,30 @@
+Colt Matrix benchmark running on
+
+java.vm.vendor  IBM Corporation    
+java.vm.version 1.3.0              
+java.vm.name    Classic VM         
+os.name         Linux              
+os.version      2.2.12-20smp       
+os.arch         x86                
+java.version    1.3.0              
+java.vendor     IBM Corporation    
+java.vendor.url http://www.ibm.com/
+
+Colt Version is Version 1.0.1.50 (Mon May 15 00:02:49 CEST 2000)
+Please report problems to wolfgang.hoschek@cern.ch or http://nicewww.cern.ch/~hoschek/colt/index.htm
+
+
+
+Executing command = [dgemm, dense, 1, 2, 0.99, false, true, 5, 5, 50, 100, 300, 500, 1000] ...
+
+@x.x.x.x.x.x.x.*
+Performance of Blas matrix-matrix mult dgemm(false, true, 1, A, B, 0, C)
+type=dense
+       | size
+       | 5     5     50     100     300     500     1000  
+----------------------------------------------------------
+d 0.99 | 35.25 34.84 98.543 115.636 116.029 110.424 91.663
+Run took a total of Time=44.024 secs. End of run.
+
+Program execution took a total of 0.73700005 minutes.
+Good bye.

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Added: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/dgemmColt1.0.1ibm1.3LxPIII_2.txt
URL: http://svn.apache.org/viewvc/lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/dgemmColt1.0.1ibm1.3LxPIII_2.txt?rev=883365&view=auto
==============================================================================
--- lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/dgemmColt1.0.1ibm1.3LxPIII_2.txt (added)
+++ lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/dgemmColt1.0.1ibm1.3LxPIII_2.txt Mon Nov 23 15:14:26 2009
@@ -0,0 +1,30 @@
+Colt Matrix benchmark running on
+
+java.vm.vendor  IBM Corporation    
+java.vm.version 1.3.0              
+java.vm.name    Classic VM         
+os.name         Linux              
+os.version      2.2.12-20smp       
+os.arch         x86                
+java.version    1.3.0              
+java.vendor     IBM Corporation    
+java.vendor.url http://www.ibm.com/
+
+Colt Version is Version 1.0.1.50 (Mon May 15 00:02:49 CEST 2000)
+Please report problems to wolfgang.hoschek@cern.ch or http://nicewww.cern.ch/~hoschek/colt/index.htm
+
+
+
+Executing command = [dgemm, dense, 2, 2, 0.99, false, true, 5, 5, 50, 100, 300, 500, 1000] ...
+
+@x.x.x.x.x.x.x.*
+Performance of Blas matrix-matrix mult dgemm(false, true, 1, A, B, 0, C)
+type=dense
+       | size
+       | 5      5      50      100     300     500     1000   
+--------------------------------------------------------------
+d 0.99 | 37.829 37.278 165.654 202.532 214.819 203.583 175.685
+Run took a total of Time=33.605 secs. End of run.
+
+Program execution took a total of 0.56366664 minutes.
+Good bye.

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+<HTML><title>Function Objects</title>
+
+<BODY>
+<h2><a name="Transformation"></a>Example 1: Transformation </h2>
+
+<p>The following examples will often use prefabricated function objects from the
+  library <a href="../../../jet/math/Functions.html">org.apache.mahout.jet.math.Functions</a>
+  But you need not yet know all about that library, only that it exists. Let's
+  stay focused and browse through the examples. </p>
+<hr>
+<h2>Frequently Used Scaling <img src="../../doc-files/new.gif" width="32" height="22" align="bottom"></h2>
+
+<p>
+
+<p>
+<table width="100%" border="1" cellspacing="0">
+  <tr bgcolor="#339933">
+    <td width="19%">Operation</td>
+    <td width="35%">Method</td>
+    <td width="46%">Comment</td>
+  </tr>
+  <tr>
+    <td width="19%">elementwise scaling</td>
+    <td width="35%">assign(f) where f is one of {F.mult(a),F.div(a)}</td>
+    <td width="46%"><tt>x[i] = x[i] {*,/} a<br>
+      x[i,j] = x[i,j] {*,/} a</tt></td>
+  </tr>
+  <tr>
+    <td width="19%">elementwise scaling</td>
+    <td width="35%">assign(y,f) where f is one of {F.plus,F.minus, F.mult,F.div,
+      F.plusMult(a),F.minusMult(a)}
+    </td>
+    <td width="46%"><tt>x[i] = x[i] {+,-,*,/} y[i]<br>
+      x[i] = x[i] {+,-} y[i] {*,/} a<br>
+      <br>
+    </tt><tt>x[i,j] = x[i,j] {+,-,*,/} y[i,j]<br>
+      x[i,j] = x[i,j] {+,-} y[i,j] {*,/} a</tt></td>
+  </tr>
+</table>
+<p>Usually, assign operations are heavily optimized for function objects implementing
+  frequently used numerical scaling like plus,minus,mult,div,plusMult,minusMult,
+  etc. Here are idioms that make numerical codes efficient:</p>
+<pre>
+org.apache.mahout.jet.math.org.apache.mahout.jet.math cern.jet.math.Functions.functions; // naming shortcut (alias) saves some keystrokes:
+
+double a = 2;
+// x and y are 1,2 or 3-d matrices
+
+x.assign(F.mult(a));           // x[i] = x[i] * a
+x.assign(F.div(a));            // x[i] = x[i] / a
+
+x.assign(F.plus(a));           // x[i] = x[i] + a
+x.assign(F.minus(a));          // x[i] = x[i] - a
+
+
+x.assign(y, F.mult);           // x[i] = x[i] * y[i]
+x.assign(y, F.div);            // x[i] = x[i] / y[i]
+
+x.assign(y, F.plus);           // x[i] = x[i] + y[i]
+x.assign(y, F.minus);          // x[i] = x[i] - y[i]
+
+
+x.assign(y, F.plusMult(a));    // x[i] = x[i] + y[i]*a
+
+x.assign(y, F.plusMult(a));    // x[i,j] = x[i,j] + y[i,j]*a
+x.assign(y, F.minusMult(1/a)); // x[i,j] = x[i,j] - y[i,j]/a
+</pre>
+<p>Try the examples also on 2-d or 3-d matrices. They work without changes regardless
+  of dimensionality. </p>
+<hr>
+<h2>Transformation over one matrix </h2>
+
+<p>To prepare with, let's construct a 1-d matrix:
+<pre>double[] v1 = {0, 1, 2, 3}; <br>DoubleMatrix1D x = new DenseDoubleMatrix1D(v1); </pre>
+<p>Using a <tt>mult</tt> function object, we multiply the matrix with a scalar
+  <tt>c</tt> 
+<pre>// x[i] = x[i] * c<br>double c = 2;<br>x.assign(cern.jet.math.Functions.mult(c));
+System.out.println(x);
+--&gt; 0 2 4 6</pre>
+<p>It would be equivalent but more clumsy to write 
+<pre>x.assign( 
+   new DoubleFunction() {
+      public final double apply(double a) { return a*c); } 
+   }
+); 
+</pre>
+<p>Similarly, the <tt>sin</tt> function object is used to transform the matrix
+  to hold in each cell the sine of the former corresponding cell value: 
+<pre>// set each cell to its sine<br>System.out.println(x.assign(cern.jet.math.Functions.sin));
+
+// set each cell to random state uniform in (0,1)<br>x.assign(cern.jet.math.Functions.random()));<br>--&gt; 0.002489 0.793068 0.620307 0.35774 
+<br>// set each cell to random state uniform in (0,1)<br>System.out.println(x.assign(cern.jet.math.Functions.random()));<br>--&gt; 0.002489 0.793068 0.620307 0.35774 
+<br>// set each cell to random state uniform in (-0.5, 0.5)<br>int seed = 12345;<br>System.out.println(x.assign(new cern.jet.random.Uniform(-0.5, 0.5, seed)));<br>--&gt; 0.31733 0.499061 0.010354 -0.368467 
+
+// set each cell to random state from Poisson distribution with mean=2<br>System.out.println(x.assign(new cern.jet.random.Poisson(2, cern.jet.random.Poisson.makeDefaultGenerator()))); 
+--&gt; 9 6 2 2
+</pre>
+<hr>
+<h2>Transformation over two matrices</h2>
+
+<p>
+
+<p>To prepare with, let's construct two 1-d matrices: 
+<pre>double[] v1 = {0, 1, 2, 3}; <br>double[] v2 = {0, 2, 4, 6};
+DoubleMatrix1D x = new DenseDoubleMatrix1D(v1);
+DoubleMatrix1D y = new DenseDoubleMatrix1D(v2);
+</pre>
+<p><b><tt>x = x<sup>y</sup> &lt;==&gt; x[i] = x[i]<sup>y[i]</sup></tt></b> <b><tt>
+  for all i</tt></b>
+
+<p>A prefabricated <tt>pow</tt> function object is used to compute the power transformation:</p>
+<pre>// x[i] = Math.pow(x[i], y[i])
+System.out.println(x.assign(y, org.apache.mahout.jet.math.Functions.pow));
+--&amp;gt; 1 1 16 729
+</pre>
+<p>A prefabricated <tt>mult</tt> function does something similar:</p>
+<pre>// x[i] = x[i] * y[i]<br>System.out.println(x.assign(y, cern.jet.math.Functions.mult));
+--> 0 2 8 18
+</pre>
+<p>The naming shortcut (alias) saves some keystrokes:</p>
+<pre>org.apache.mahout.jet.math.org.apache.mahout.jet.math cern.jet.math.Functions.functions;</pre>
+<p>Chaining function objects yields more complex functions:</p>
+<pre>// x[i] = x[i] * y[i] * 3<br>System.out.println(x.assign(y, F.chain(F.mult,F.mult(3))));
+--> 0 6 24 54
+</pre>
+<p></p>
+
+<p>More complex transformation functions need to be written by hand:</p>
+<pre>m1.assign(m2,
+   new DoubleDoubleFunction() {
+      public double apply(double a, double b) { return Math.PI*Math.log(a-5)*Math.pow(a,b); }
+   }
+);
+</pre>
+<p> If we want to generate a third matrix holding the result of the power transformation,
+  and leave both source matrices unaffected, we make a copy first and then apply
+  the transformation on the copy: 
+<pre>// z[i] = Math.pow(x[i],y[i])<br>DoubleMatrix2D z = x.copy().assign(y, F.pow);
+System.out.println(z);
+</pre>
+</BODY>
+</HTML>
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+<HTML><title>Function Objects</title>
+
+<BODY>
+<h2><a name="Aggregation"></a>Example 2: Aggregation</h2>
+
+<p><i>Aggregation</i> is a generalized form of summation or integration. Aggregation
+  functions visit all cells of a matrix and derive a single number as &quot;summary
+  information&quot;. Number of elements, mean, maximum, minimum, variance, root-mean-square
+  are classic aggregation functions, but there are many more. Floods of data are
+  too difficult to understand - a single number sells better to management. More
+  seriously, the scientific process takes observations and tries to find patterns,
+  the essentials of knowledge, which are more compact and easier to internalize
+  than the observations itself. The most compact representation of knowledge is,
+  of course, a single number.
+
+<p>We will use the prefabricated <tt>plus</tt> and <tt>square</tt> function objects
+  to compute the sum of squares of a 1-d matrix, but first, let's get prepared. 
+<pre>double[] v1 = {0, 1, 2, 3}; <br>DoubleMatrix1D matrix = new DenseDoubleMatrix1D(v1);
+
+// the naming shortcut (alias) saves some keystrokes:
+cern.jet.math.Functions F = cern.jet.math.Functions.functions;
+</pre>
+<hr>
+<h2>Aggregation over one matrix </h2>
+<pre>
+// Sum( x[i]*x[i] ) 
+System.out.println(matrix.aggregate(F.plus,F.square));
+// --> 14
+
+// Sum( x[i]*x[i]*x[i] ) 
+System.out.println(matrix.aggregate(F.plus,F.pow(3)));
+// --> 36
+
+// Sum( x[i] ) 
+System.out.println(matrix.aggregate(F.plus,F.identity));
+// or the specialized version
+System.out.println(matrix.zSum());
+// --> 6
+</pre>
+<pre>// Min( x[i] ) 
+System.out.println(matrix.aggregate(F.min,F.identity));
+// --> 0
+
+// Max( Sqrt(x[i]) / 2 ) 
+System.out.println(matrix.aggregate(F.max,F.chain(F.div(2),F.sqrt)));
+// --> 0.8660254037844386
+
+// Number of all cells with 0 <= value <= 2
+System.out.println(matrix.aggregate(F.plus,F.between(0,2)));
+// --> 3
+
+// Number of all cells with 0.8 <= Log2(value) <= 1.2
+System.out.println(matrix.aggregate(F.plus,F.chain(F.between(0.8,1.2),F.log2)));
+// --> 1
+
+// Product( x[i] )
+System.out.println(matrix.aggregate(F.mult,F.identity));
+// --> 0
+
+// Product( x[i] ) of all x[i] > limit
+final double limit = 1;
+DoubleFunction f = new DoubleFunction() {
+	public final double apply(double a) { return a>limit ? a : 1; }
+};
+System.out.println(matrix.aggregate(F.mult,f));
+// --> 6
+
+// RMS (Root-Mean-Square) is a measure of the average "size" of the elements of a data sequence.
+double rms = Math.sqrt(matrix.aggregate(F.plus,F.square) / matrix.size());</pre>
+<hr>
+<h2>Aggregation over two matrices </h2>
+<pre>
+DoubleMatrix1D x = matrix.copy();
+DoubleMatrix1D y = matrix.copy();
+// x is 0 1 2 3 
+// y is 0 1 2 3 
+
+// Sum( x[i]*y[i] )
+System.out.println(x.aggregate(y, F.plus, F.mult));
+// --> 14
+
+// Sum( (x[i]+y[i])^2 )
+System.out.println(x.aggregate(y, F.plus, F.chain(F.square,F.plus)));
+// --> 56
+
+
+// Sum(Math.PI * Math.log(y[i] / x[i]))<br>x.aggregate(y, F.plus, F.chain(F.mult(Math.PI),F.chain(F.log,F.swapArgs(F.div))));
+
+// equivalent, but perhaps less error prone and more readable: 
+x.aggregate(y, F.plus,
+   new DoubleDoubleFunction() {
+      public double apply(double a, double b) { return Math.PI*Math.log(b/a); }
+   }
+);
+</pre>
+<p>Try the examples on 2-d or 3-d matrices. They work without changes (except,
+  of course, that construction of the source matrix need to be modified). </p>
+</BODY>
+</HTML>
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+<HTML><title>Function Objects</title>
+
+<BODY>
+<h2><a name="Selection"></a>Example 3: Selection views based on conditions </h2>
+
+<p>Using condition functions (predicates), we can filter away uninteresting data
+  and keep only interesting data. In physics codes, this process is often called
+  <i>cutting on a predicate</i>.
+<hr>
+<h2>Conditions on 1-d matrices (vectors) </h2>
+<pre>
+// the naming shortcut (alias) saves some keystrokes:
+cern.jet.math.Functions F = cern.jet.math.Functions.functions;
+
+double[] v1 = {0, 1, 2, 3};
+DoubleMatrix1D matrix = new DenseDoubleMatrix1D(v1);
+// 0 1 2 3 
+
+// view all cells for which holds: lower <= value <= upper
+final double lower = 0.2;
+final double upper = 2.5
+matrix.viewSelection(F.isBetween(lower, upper)); 
+// --> 1 2
+
+// equivalent, but less concise:
+matrix.viewSelection( 
+	new DoubleProcedure() {
+		public final boolean apply(double a) { return lower &lt;= a &amp;&amp; a &lt;= upper; }
+	}
+);
+// --> 1 2
+</pre>
+<pre></pre>
+<pre>// view all cells with even value
+matrix.viewSelection( 
+	new DoubleProcedure() {
+		public final boolean apply(double a) { return a % 2 == 0; }
+	}
+);
+// --> 0 2
+
+// sum of all cells for which holds: lower <= value <= upper
+double sum = matrix.viewSelection(F.isBetween(lower, upper)).zSum(); 
+// --> 3
+<br>// equivalent: 
+double sum = matrix.viewSelection(F.isBetween(lower, upper)).aggregate(F.plus,F.identity); 
+</pre>
+<hr>
+<h2>Conditions on 2-d matrices </h2>
+<pre>
+// view all rows which have a value < threshold in the first column (representing "age")
+final double threshold = 16;
+matrix.viewSelection( 
+	new DoubleMatrix1DProcedure() {
+		public final boolean apply(DoubleMatrix1D m) { return m.get(0) < threshold; }
+	}
+);
+
+// view all rows with RMS < threshold.<br>// the RMS (Root-Mean-Square) is a measure of the average "size" of the elements of a data sequence.
+final double threshold = 0.5;
+matrix.viewSelection( 
+	new DoubleMatrix1DProcedure() {
+		public final boolean apply(DoubleMatrix1D m) { return Math.sqrt(m.aggregate(F.plus,F.square) / m.size()) < threshold; }
+	}
+);
+</pre>
+<hr>
+<h2>Conditions on 3-d matrices</h2>
+<pre>
+// view all slices which have an aggregate sum > 1000
+matrix.viewSelection( 
+	new DoubleMatrix2DProcedure() {
+		public final boolean apply(DoubleMatrix2D m) { return m.zSum() > 1000; }
+	}
+);
+</pre>
+</BODY>
+</HTML>
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+<HTML><title>Function Objects</title>
+
+<BODY>
+<h1>Example 4: Sorting by user specified order </h1>
+
+<p>Assume, we would like to sort the rows of a 2d matrix by the the last column (representing "age"). This can be done
+  with
+<pre>
+// sort by last column
+sorted = matrix.viewSorted(matrix.columns()-1);
+</pre>
+<p>Or assume, we would like to sort the columns of a 2d matrix by the the last row.
+  Unfortunately, there is no convenience method to directly sort by row. So we need to view columns as rows and rows as
+  columns, then sort, then adjust our view again.
+<pre>
+// sort by last row
+int lastRow = matrix.rows()-1;
+sorted = matrix.viewDice().viewSorted(lastRow).viewDice();
+</pre>
+<p>Next, we would like to sort the rows of a 2d matrix by the aggregate sum
+  of values in a row. A <i>comparator</i> object is used to do the job: 
+<pre>// sort by sum of values in a row
+DoubleMatrix1DComparator comp = new DoubleMatrix1DComparator() {
+	public int compare(DoubleMatrix1D a, DoubleMatrix1D b) {
+		double as = a.zSum(); double bs = b.zSum();
+		return as < bs ? -1 : as == bs ? 0 : 1;
+	}
+};
+sorted = cern.colt.matrix.doublealgo.Sorting.quickSort(matrix,comp);
+</pre>
+<p>Further, we would like to sort the rows of a 2d matrix by the aggregate sum of
+  logarithms in a row (which is a way to achieve sorting by <i>geometric mean</i>
+  when viewing a row as a series of samples). A slightly more complex comparator
+  object is needed: 
+<pre>// sort by sum of logarithms in a row
+DoubleMatrix1DComparator comp = new DoubleMatrix1DComparator() {
+	public int compare(DoubleMatrix1D a, DoubleMatrix1D b) {
+		double as = a.aggregate(cern.jet.math.Functions.plus,cern.jet.math.Functions.log); <br>		double bs = b.aggregate(cern.jet.math.Functions.plus,cern.jet.math.Functions.log);
+		return as < bs ? -1 : as == bs ? 0 : 1;
+	}
+};
+sorted = cern.colt.matrix.doublealgo.Sorting.quickSort(matrix,comp);
+</pre>
+This is certainly not most efficient since row sums are recomputed many times
+(<tt>2*rows*log(rows)</tt> times, on average), but will suffice as an example.
+An efficient app will precompute the sums and use <tt>cern.colt.GenericSorting</tt>
+to sort the matrix. In general, if comparisons are expensive, precomputation boots
+performance by a factor <tt>2*log(rows)</tt>.
+<p><i><img src="../../doc-files/new.gif" width="32" height="22" align="bottom"></i>Recently,
+  two methods that do exactly that were added to <a href="../doublealgo/Sorting.html">cern.colt.matrix.doublealgo.Sorting</a>.
+  One of them works by filling a row into a so-called "bin", which is a multi-set
+  with statistics operations defined upon. Aggregate measures over the row are
+  then computed via a <a href="../../../jet/histo/BinFunction1D.html">BinFunction1D</a>.
+  Some prefabricated functions are contained in <a href="../../../jet/histo/BinFunctions1D.html">BinFunctions1D</a>
+  Here is how to solve the problem efficiently: 
+<pre>
+// sort by sum of logarithms in a row
+sorted = cern.colt.matrix.doublealgo.Sorting.quickSort(matrix,hep.aida.bin.BinFunctions1D.sumLog);
+
+// sort by median in a row
+sorted = cern.colt.matrix.doublealgo.Sorting.quickSort(matrix,hep.aida.bin.BinFunctions1D.median);
+
+// sort by maximum in a row
+sorted = cern.colt.matrix.doublealgo.Sorting.quickSort(matrix,hep.aida.bin.BinFunctions1D.max);
+</pre>
+</BODY>
+</HTML>
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+<HTML><title>Function Objects</title>
+
+<BODY>
+<h1><a name="Overview"></a>Function Objects</h1>
+Here are some examples demonstrating how function objects can be used to
+<ol>
+  <li><a href="function1.html">transform</a> a matrix A into another matrix B
+    which is a function of the original matrix A (and optionally yet another matrix
+    C)
+  </li>
+  <li><a href="function2.html">aggregate</a> cell values or a function of them</li>
+  <li><a href="function3.html">generate selection</a> views for cells satisfying
+    a given condition
+  </li>
+  <li><a href="function4.html">sort</a> matrix rows or columns into a user specified
+    order
+  </li>
+  <li>You will most likely use them to do many more powerful things</li>
+</ol>
+</BODY>
+</HTML>
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+Colt Matrix benchmark running on
+
+java.vm.vendor  IBM Corporation    
+java.vm.version 1.3.0              
+java.vm.name    Classic VM         
+os.name         Linux              
+os.version      2.2.12-20smp       
+os.arch         x86                
+java.version    1.3.0              
+java.vendor     IBM Corporation    
+java.vendor.url http://www.ibm.com/
+
+Colt Version is Version 1.0.1.51 (Mon May 15 20:23:08 CEST 2000)
+Please report problems to wolfgang.hoschek@cern.ch or http://nicewww.cern.ch/~hoschek/colt/index.htm
+
+
+
+Executing command = [dgemm, dense, 2, 2, 0.999, false, true, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-matrix mult dgemm(false, true, 1, A, B, 0, C)
+type=dense
+        | size
+        | 5      10     25      50      100     250     500     1000   
+-----------------------------------------------------------------------
+d 0.999 | 36.528 82.766 112.261 157.978 198.849 212.999 204.165 175.932
+Run took a total of Time=37.602 secs. End of run.
+
+
+Executing command = [dgemm, dense, 1, 2, 0.999, false, true, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-matrix mult dgemm(false, true, 1, A, B, 0, C)
+type=dense
+        | size
+        | 5      10     25      50      100     250     500     1000  
+----------------------------------------------------------------------
+d 0.999 | 36.213 74.518 102.758 105.095 119.012 116.333 102.124 87.409
+Run took a total of Time=49.605 secs. End of run.
+
+
+Executing command = [dgemm, rowCompressed, 1, 2, 0.01, false, false, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-matrix mult dgemm(false, false, 1, A, B, 0, C)
+type=rowCompressed
+       | size
+       | 5      10      25      50         100       250        500        1000      
+-------------------------------------------------------------------------------------
+d 0.01 | 32.525 138.719 703.833 1.845E+003 3.27E+003 2.848E+003 2.772E+003 2.951E+003
+Run took a total of Time=32.202 secs. End of run.
+
+
+Executing command = [dgemm, sparse, 1, 2, 0.01, false, false, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-matrix mult dgemm(false, false, 1, A, B, 0, C)
+type=sparse
+       | size
+       | 5      10      25      50         100       250        500        1000      
+-------------------------------------------------------------------------------------
+d 0.01 | 31.128 135.907 637.538 1.767E+003 3.15E+003 2.751E+003 2.817E+003 2.883E+003
+Run took a total of Time=32.277 secs. End of run.
+
+
+Executing command = [dgemv, dense, 2, 2, 0.01, false, 5, 10, 25, 50, 100, 250, 500, 1000, 2000] ...
+
+@x.x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-vector mult dgemv(false, 1, A, B, 0, C)
+type=dense
+       | size
+       | 5      10     25      50      100     250     500    1000   2000  
+---------------------------------------------------------------------------
+d 0.01 | 20.828 52.811 103.611 104.354 109.498 101.451 67.187 66.341 54.554
+Run took a total of Time=39.415 secs. End of run.
+
+
+Executing command = [dgemv, dense, 1, 2, 0.01, false, 5, 10, 25, 50, 100, 250, 500, 1000, 2000] ...
+
+@x.x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-vector mult dgemv(false, 1, A, B, 0, C)
+type=dense
+       | size
+       | 5      10    25      50      100     250    500    1000   2000  
+-------------------------------------------------------------------------
+d 0.01 | 30.574 65.79 114.877 101.596 111.633 47.494 31.702 31.778 31.793
+Run took a total of Time=40.283 secs. End of run.
+
+
+Executing command = [dgemv, rowCompressed, 1, 2, 0.01, false, 5, 10, 25, 50, 100, 250, 500, 1000, 2000] ...
+
+@x.x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-vector mult dgemv(false, 1, A, B, 0, C)
+type=rowCompressed
+       | size
+       | 5      10      25      50         100       250        500        1000       2000      
+------------------------------------------------------------------------------------------------
+d 0.01 | 31.796 145.363 527.966 1.143E+003 2.33E+003 3.737E+003 4.776E+003 5.992E+003 3.448E+003
+Run took a total of Time=41.138 secs. End of run.
+
+
+Executing command = [dgemv, sparse, 1, 2, 0.01, false, 5, 10, 25, 50, 100, 250, 500, 1000, 2000] ...
+
+@x.x.x.x.x.x.x.x.x.*
+Performance of Blas matrix-vector mult dgemv(false, 1, A, B, 0, C)
+type=sparse
+       | size
+       | 5      10     25      50      100     250     500     1000    2000   
+------------------------------------------------------------------------------
+d 0.01 | 23.507 84.216 341.435 598.555 717.508 733.817 768.899 756.811 573.721
+Run took a total of Time=38.784 secs. End of run.
+
+
+Executing command = [assign, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.assign(B) [Mops/sec]
+type=dense
+        | size
+        | 5      10      25      50    100    250    500    1000 
+-----------------------------------------------------------------
+d 0.999 | 79.408 164.319 309.899 35.58 44.551 20.849 21.326 21.78
+Run took a total of Time=36.992 secs. End of run.
+
+
+Executing command = [assignGetSet, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.assign(B) via get and set [Mops/sec]
+type=dense
+        | size
+        | 5     10    25    50    100   250  500   1000 
+--------------------------------------------------------
+d 0.999 | 8.567 7.898 8.028 7.567 7.823 5.57 5.289 5.275
+Run took a total of Time=32.556 secs. End of run.
+
+
+Executing command = [assignGetSetQuick, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.assign(B) via getQuick and setQuick [Mops/sec]
+type=dense
+        | size
+        | 5      10     25    50    100    250   500   1000 
+------------------------------------------------------------
+d 0.999 | 11.381 11.172 11.51 10.19 10.943 7.344 7.516 7.495
+Run took a total of Time=32.857 secs. End of run.
+
+
+Executing command = [elementwiseMult, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.assign(F.mult(0.5)) via Blas [Mflops/sec]
+type=dense
+        | size
+        | 5      10     25     50     100    250    500    1000 
+----------------------------------------------------------------
+d 0.999 | 34.886 64.726 94.636 43.634 52.731 25.993 22.014 22.75
+Run took a total of Time=37.829 secs. End of run.
+
+
+Executing command = [elementwiseMult, dense, 2, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.
\ No newline at end of file

Added: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/out8
URL: http://svn.apache.org/viewvc/lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/out8?rev=883365&view=auto
==============================================================================
--- lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/out8 (added)
+++ lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/out8 Mon Nov 23 15:14:26 2009
@@ -0,0 +1,49 @@
+Colt Matrix benchmark running on
+
+java.vm.vendor  IBM Corporation    
+java.vm.version 1.3.0              
+java.vm.name    Classic VM         
+os.name         Linux              
+os.version      2.2.12-20smp       
+os.arch         x86                
+java.version    1.3.0              
+java.vendor     IBM Corporation    
+java.vendor.url http://www.ibm.com/
+
+Colt Version is Version 1.0.1.51 (Mon May 15 20:23:08 CEST 2000)
+Please report problems to wolfgang.hoschek@cern.ch or http://nicewww.cern.ch/~hoschek/colt/index.htm
+
+
+
+Executing command = [elementwiseMult, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.assign(F.mult(0.5)) via Blas [Mflops/sec]
+type=dense
+        | size
+        | 5      10     25     50    100    250  500    1000  
+--------------------------------------------------------------
+d 0.999 | 31.272 57.278 92.914 43.65 47.647 27.2 22.452 23.232
+Run took a total of Time=38.05 secs. End of run.
+
+
+Executing command = [elementwiseMultB, dense, 1, 2, 0.999, 5, 10, 25, 50, 100, 250, 500, 1000] ...
+
+@x.x.x.x.x.x.x.x.*
+Performance of A.assign(B,F.mult) via Blas [Mflops/sec]
+type=dense
+        | size
+        | 5      10     25     50     100    250    500    1000  
+-----------------------------------------------------------------
+d 0.999 | 44.274 55.451 60.475 35.507 26.752 14.646 14.925 14.599
+Run took a total of Time=34.514 secs. End of run.
+
+Command file name used: /afs/cern.ch/user/h/hoschek/coltb4/cmd5
+To reproduce and compare results, here it's contents:
+// with 1 and with 2 CPUs
+elementwiseMult dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+
+elementwiseMultB dense 1 2.0 0.999 5 10 25 50 100 250 500 1000
+
+Program execution took a total of 1.2129 minutes.
+Good bye.

Added: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown122RC3.txt
URL: http://svn.apache.org/viewvc/lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown122RC3.txt?rev=883365&view=auto
==============================================================================
--- lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown122RC3.txt (added)
+++ lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown122RC3.txt Mon Nov 23 15:14:26 2009
@@ -0,0 +1,357 @@
+Colt Matrix benchmark running on
+
+java.vm.vendor  Sun Microsystems Inc.
+java.vm.version 1.2.2                
+java.vm.name    Classic VM           
+os.name         Linux                
+os.version      2.2.12-20            
+os.arch         i386                 
+java.version    1.2.2                
+java.vendor     Sun Microsystems Inc.
+java.vendor.url http://java.sun.com/ 
+
+
+@x....x....x....x....x....x....
+@x....x....x....x....x....x....*
+Performance of DoubleMatrix2D assign [Mops/sec]
+type=dense
+       | density
+       | 0.0010 0.01   0.1    0.999 
+------------------------------------
+s 30   | 72.148 72.856 77.553 65.259
+i 33   | 55.993 60.265 66.304 47.264
+z 66   | 41.874 41.737 41.502 41.344
+e 100  | 40.807 41.364 42.104 41.375
+  300  | 20.258 20.157 20.429 20.357
+  1000 | 19.399 19.324 19.342 19.342
+
+Performance of DoubleMatrix2D assign [Mops/sec]
+type=sparse
+       | density
+       | 0.0010       0.01    0.1     0.999  
+---------------------------------------------
+s 30   |  62.035       28.36   13.683   2.644
+i 33   |  95.487       51.332  17.624   1.807
+z 66   | 297.188      111.492  21.432   2.31 
+e 100  | 377.066      150.811 NaN     NaN    
+  300  |   1.323E+003 264.146 NaN     NaN    
+  1000 |   1.628E+003 275.615 NaN     NaN    
+
+Speedup of dense over sparse
+       | density
+       | 0.0010 0.01  0.1     0.999  
+-------------------------------------
+s 30   | 1.163  2.569   5.668  24.683
+i 33   | 0.586  1.174   3.762  26.15 
+z 66   | 0.141  0.374   1.936  17.898
+e 100  | 0.108  0.274 NaN     NaN    
+  300  | 0.015  0.076 NaN     NaN    
+  1000 | 0.012  0.07  NaN     NaN    
+Run took a total of Time=190.751 secs. End of run.
+
+@x....x....x....x....x....x....
+@x....x....x....x....x....x....*
+Performance of DoubleMatrix2D assignGetSetQuick [Mops/sec]
+type=dense
+       | density
+       | 0.0010 0.01  0.1   0.999
+---------------------------------
+s 30   | 4.927  4.744 4.931 4.958
+i 33   | 4.517  4.857 4.868 4.823
+z 66   | 5.016  4.938 4.918 4.891
+e 100  | 4.912  4.938 4.932 4.926
+  300  | 4.18   4.176 4.107 4.188
+  1000 | 4.037  4.039 4.042 4.037
+
+Performance of DoubleMatrix2D assignGetSetQuick [Mops/sec]
+type=sparse
+       | density
+       | 0.0010 0.01  0.1     0.999  
+-------------------------------------
+s 30   | 1      0.932   0.812   0.445
+i 33   | 1.002  0.932   0.764   0.349
+z 66   | 0.957  0.938   0.802   0.389
+e 100  | 0.964  0.941 NaN     NaN    
+  300  | 0.959  0.93  NaN     NaN    
+  1000 | 0.954  0.933 NaN     NaN    
+
+Speedup of dense over sparse
+       | density
+       | 0.0010 0.01  0.1     0.999  
+-------------------------------------
+s 30   | 4.927  5.092   6.071  11.138
+i 33   | 4.508  5.209   6.369  13.816
+z 66   | 5.241  5.266   6.134  12.561
+e 100  | 5.098  5.248 NaN     NaN    
+  300  | 4.359  4.49  NaN     NaN    
+  1000 | 4.231  4.328 NaN     NaN    
+Run took a total of Time=168.947 secs. End of run.
+
+@x....x....x....x....x....x....
+@x....x....x....x....x....x....*
+Performance of DoubleMatrix2D assignGetSet [Mops/sec]
+type=dense
+       | density
+       | 0.0010 0.01  0.1   0.999
+---------------------------------
+s 30   | 2.793  2.767 2.82  2.77 
+i 33   | 2.812  2.802 2.773 2.805
+z 66   | 2.824  2.821 2.825 2.858
+e 100  | 2.823  2.78  2.851 2.811
+  300  | 2.562  2.516 2.512 2.526
+  1000 | 2.493  2.444 2.441 2.463
+
+Performance of DoubleMatrix2D assignGetSet [Mops/sec]
+type=sparse
+       | density
+       | 0.0010 0.01  0.1     0.999  
+-------------------------------------
+s 30   | 0.861  0.811   0.716   0.395
+i 33   | 0.858  0.81    0.728   0.326
+z 66   | 0.829  0.815   0.7     0.362
+e 100  | 0.833  0.816 NaN     NaN    
+  300  | 0.83   0.81  NaN     NaN    
+  1000 | 0.828  0.812 NaN     NaN    
+
+Speedup of dense over sparse
+       | density
+       | 0.0010 0.01  0.1     0.999  
+-------------------------------------
+s 30   | 3.246  3.411   3.941   7.015
+i 33   | 3.277  3.46    3.811   8.594
+z 66   | 3.407  3.459   4.033   7.883
+e 100  | 3.391  3.406 NaN     NaN    
+  300  | 3.086  3.105 NaN     NaN    
+  1000 | 3.011  3.011 NaN     NaN    
+Run took a total of Time=164.045 secs. End of run.
+
+@x....x....x....x....x....x....
+@x....x....x....x....x....x....*
+Performance of DoubleMatrix.zMult(B,C) [Mflops/sec]
+type=dense
+       | density
+       | 0.0010       0.01    0.1    0.999 
+-------------------------------------------
+s 30   |  18.498       18.497 16.814 18.434
+i 33   |  75.999       44.205 16.987 19.993
+z 66   | 180.208       77.364 24.675 24.234
+e 100  | 269.628      107.28  26.316 26.163
+  300  | 634.321      175.732 20.052 22.059
+  1000 |   1.238E+003 201.066 18.932 18.935
+
+Performance of DoubleMatrix.zMult(B,C) [Mflops/sec]
+type=sparse
+       | density
+       | 0.0010  0.01   0.1     0.999  
+---------------------------------------
+s 30   |   1.344  1.306   1.274   1.345
+i 33   |  13.965  3.411   1.094   1.101
+z 66   |  43.325  5.637   1.057   1.123
+e 100  |  62.718  9.634 NaN     NaN    
+  300  | 167.183 32.423 NaN     NaN    
+  1000 | 392.696 75.25  NaN     NaN    
+
+Speedup of dense over sparse
+       | density
+       | 0.0010 0.01   0.1     0.999  
+--------------------------------------
+s 30   | 13.762 14.163  13.201  13.709
+i 33   |  5.442 12.961  15.53   18.162
+z 66   |  4.159 13.724  23.345  21.58 
+e 100  |  4.299 11.136 NaN     NaN    
+  300  |  3.794  5.42  NaN     NaN    
+  1000 |  3.153  2.672 NaN     NaN    
+Run took a total of Time=391.957 secs. End of run.
+
+@x....x....x....x....x....x....
+@x....x....x....x....x....x....*
+Performance of DoubleMatrix Elementwise mult [Mflops/sec]
+type=dense
+       | density
+       | 0.0010 0.01   0.1    0.999 
+------------------------------------
+s 30   | 11.469 11.025 10.981 11.229
+i 33   | 11     11.015 11.256 11.643
+z 66   | 11.947 11.82  11.874 11.874
+e 100  | 12.112 11.981 11.828 11.952
+  300  |  7.753  6.403  6.547  6.471
+  1000 |  7.874  6.605  6.605  6.609
+
+Performance of DoubleMatrix Elementwise mult [Mflops/sec]
+type=sparse
+       | density
+       | 0.0010 0.01  0.1     0.999  
+-------------------------------------
+s 30   | 0.609  0.608   0.6     0.618
+i 33   | 0.607  0.601   0.565   0.57 
+z 66   | 0.603  0.604   0.589   0.565
+e 100  | 0.606  0.605 NaN     NaN    
+  300  | 0.606  0.591 NaN     NaN    
+  1000 | 0.611  0.577 NaN     NaN    
+
+Speedup of dense over sparse
+       | density
+       | 0.0010 0.01   0.1     0.999  
+--------------------------------------
+s 30   | 18.844 18.127  18.317  18.183
+i 33   | 18.107 18.319  19.908  20.417
+z 66   | 19.815 19.573  20.161  21.021
+e 100  | 19.985 19.793 NaN     NaN    
+  300  | 12.787 10.84  NaN     NaN    
+  1000 | 12.89  11.44  NaN     NaN    
+Run took a total of Time=171.221 secs. End of run.
+
+@x....x....x....x....x....x....
+@x....x....x....x....x....x....*
+Performance of LUQuick.decompose [Mflops/sec]
+type=dense
+       | density
+       | 0.0010  0.01    0.1    0.999 
+--------------------------------------
+s 30   |  18.982  17.223  8.871 10.844
+i 33   |  21.144  19.34  11.536 11.954
+z 66   |  46.86   41.798 20.639 20.001
+e 100  |  72.479  62.436 29.51  25.456
+  300  | 200.101 148.991 43.647 34.466
+  1000 | 559.754 279.877 32.319 29.631
+
+Performance of LUQuick.decompose [Mflops/sec]
+type=sparse
+       | density
+       | 0.0010  0.01   0.1     0.999  
+---------------------------------------
+s 30   |   5.005  4.139   1.356   0.4  
+i 33   |   5.339  4.535   1.282   0.659
+z 66   |  11.359  8.552   1.556   0.322
+e 100  |  16.029 12.015 NaN     NaN    
+  300  |  45.169 25.912 NaN     NaN    
+  1000 | 122.752 37.729 NaN     NaN    
+
+Speedup of dense over sparse
+       | density
+       | 0.0010 0.01  0.1     0.999  
+-------------------------------------
+s 30   | 3.792  4.162   6.544  27.083
+i 33   | 3.96   4.265   9      18.148
+z 66   | 4.126  4.887  13.264  62.116
+e 100  | 4.522  5.197 NaN     NaN    
+  300  | 4.43   5.75  NaN     NaN    
+  1000 | 4.56   7.418 NaN     NaN    
+Run took a total of Time=208.389 secs. End of run.
+
+@x....x....x....x....x....x....
+@x....x....x....x....x....x....*
+Performance of LUQuick.solve [Mflops/sec]
+type=dense
+       | density
+       | 0.0010       0.01    0.1    0.999 
+-------------------------------------------
+s 30   |  77.259       75.015 36.129 13.482
+i 33   |  88.404       85.518 38.265 13.852
+z 66   | 194.44       189.424 27.088 15.565
+e 100  | 297.059      288.72  25.301 16.252
+  300  | 830.455      462.612 17.97  15.271
+  1000 |   2.592E+003  51.529 14.909 14.416
+
+Performance of LUQuick.solve [Mflops/sec]
+type=sparse
+       | density
+       | 0.0010  0.01   0.1     0.999  
+---------------------------------------
+s 30   |  17.994 17.379   3.66    1.446
+i 33   |  19.73  19.331   3.703   1.463
+z 66   |  40.044 36.5     2.477   1.474
+e 100  |  60.849 54.269 NaN     NaN    
+  300  | 179.829 34.356 NaN     NaN    
+  1000 | 596.899  4.884 NaN     NaN    
+
+Speedup of dense over sparse
+       | density
+       | 0.0010 0.01   0.1     0.999  
+--------------------------------------
+s 30   | 4.294   4.316   9.871   9.325
+i 33   | 4.481   4.424  10.335   9.465
+z 66   | 4.856   5.19   10.937  10.56 
+e 100  | 4.882   5.32  NaN     NaN    
+  300  | 4.618  13.465 NaN     NaN    
+  1000 | 4.343  10.55  NaN     NaN    
+Run took a total of Time=955.159 secs. End of run.
+
+@x....x....x....x....x....x....
+@x....x....x....x....x....x....*
+Performance of SOR [Mflops/sec]
+type=dense
+       | density
+       | 0.0010 0.01   0.1    0.999 
+------------------------------------
+s 30   | 13.27  13.236 13.301 13.264
+i 33   | 13.301 13.226 13.112 13.099
+z 66   | 12.518 12.526 12.526 12.477
+e 100  | 12.208 12.22  12.208 12.227
+  300  | 10.9   10.942 10.954 10.942
+  1000 | 10.676 10.7   10.7   10.71 
+
+Performance of SOR [Mflops/sec]
+type=sparse
+       | density
+       | 0.0010 0.01  0.1     0.999  
+-------------------------------------
+s 30   | 2.055  1.972   1.881   2.186
+i 33   | 2.042  2.021   1.854   2.066
+z 66   | 1.924  1.911   1.657   1.979
+e 100  | 1.895  1.807 NaN     NaN    
+  300  | 1.848  1.769 NaN     NaN    
+  1000 | 1.843  1.752 NaN     NaN    
+
+Speedup of dense over sparse
+       | density
+       | 0.0010 0.01  0.1     0.999  
+-------------------------------------
+s 30   | 6.458  6.712   7.071   6.069
+i 33   | 6.512  6.543   7.073   6.34 
+z 66   | 6.505  6.553   7.557   6.304
+e 100  | 6.441  6.764 NaN     NaN    
+  300  | 5.9    6.186 NaN     NaN    
+  1000 | 5.792  6.108 NaN     NaN    
+Run took a total of Time=165.514 secs. End of run.
+
+@x....x....x....x....x....x....
+@x....x....x....x....x....x....*
+Performance of Correlation [Mflops/sec]
+type=dense
+       | density
+       | 0.0010 0.01   0.1    0.999 
+------------------------------------
+s 30   | 11.324  9.106  5.681 10.675
+i 33   | 12.262  9.522  6.291 11.842
+z 66   | 18.938 13.537 10.745 18.629
+e 100  | 23.126 16.525 14.326 22.83 
+  300  | 25.533 21.681 20.007 27.345
+  1000 | 21.805 24.525 24.658 25.25 
+
+Performance of Correlation [Mflops/sec]
+type=sparse
+       | density
+       | 0.0010 0.01  0.1     0.999  
+-------------------------------------
+s 30   | 0.998  0.975   0.91    1.007
+i 33   | 1.008  0.975   0.917   1.011
+z 66   | 1.036  1.013   0.976   1.047
+e 100  | 1.046  1.023 NaN     NaN    
+  300  | 1.063  1.033 NaN     NaN    
+  1000 | 1.078  1.052 NaN     NaN    
+
+Speedup of dense over sparse
+       | density
+       | 0.0010 0.01   0.1     0.999  
+--------------------------------------
+s 30   | 11.347  9.344   6.241  10.599
+i 33   | 12.163  9.768   6.857  11.717
+z 66   | 18.282 13.365  11.013  17.785
+e 100  | 22.103 16.154 NaN     NaN    
+  300  | 24.029 20.986 NaN     NaN    
+  1000 | 20.235 23.322 NaN     NaN    
+Run took a total of Time=2228.253 secs. End of run.
+
+Program execution took a total of 77.4098 minutes.
+Good bye.

Propchange: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown122RC3.txt
------------------------------------------------------------------------------
    svn:eol-style = native

Added: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown12pre2.txt
URL: http://svn.apache.org/viewvc/lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown12pre2.txt?rev=883365&view=auto
==============================================================================
--- lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown12pre2.txt (added)
+++ lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown12pre2.txt Mon Nov 23 15:14:26 2009
@@ -0,0 +1,330 @@
+Colt Matrix benchmark running on
+
+java.vm.vendor  Sun Microsystems Inc.
+java.vm.version 1.2                  
+java.vm.name    Classic VM           
+os.name         Linux                
+os.version      2.2.12-20            
+os.arch         i386                 
+java.version    1.2                  
+java.vendor     Sun Microsystems Inc.
+java.vendor.url http://java.sun.com/ 
+
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of DoubleMatrix2D assign [Mops/sec]
+type=dense
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  | 94.226 82.951 83.753 87.891
+i 33  | 59.244 41.933 62.03  58.52 
+z 66  | 41.457 38.795 39.744 39.11 
+e 100 | 39.656 39.464 39.96  39.626
+  300 | 18.506 17.738 17.907 17.804
+
+Performance of DoubleMatrix2D assign [Mops/sec]
+type=sparse
+      | density
+      | 0.0010       0.01    0.1    0.99 
+-----------------------------------------
+s 30  |  54.517       30.869 13.099 2.564
+i 33  | 113.38        37.577 16.068 1.737
+z 66  | 298.229       92.842 21.464 2.121
+e 100 | 307.347      147.517 16.044 2.676
+  300 |   1.289E+003 259.31  23.216 1.174
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01  0.1   0.99  
+---------------------------------
+s 30  | 1.728  2.687 6.394 34.273
+i 33  | 0.523  1.116 3.86  33.693
+z 66  | 0.139  0.418 1.852 18.44 
+e 100 | 0.129  0.268 2.491 14.808
+  300 | 0.014  0.068 0.771 15.171
+Run took a total of Time=181.15 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of DoubleMatrix2D assignGetSetQuick [Mops/sec]
+type=dense
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 4.242  4.264 4.269 4.279
+i 33  | 4.252  4.22  4.242 4.217
+z 66  | 4.16   4.309 4.216 4.218
+e 100 | 4.232  4.217 4.272 4.281
+  300 | 3.746  3.706 3.602 3.582
+
+Performance of DoubleMatrix2D assignGetSetQuick [Mops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 0.904  0.869 0.73  0.391
+i 33  | 0.908  0.86  0.769 0.32 
+z 66  | 0.899  0.867 0.787 0.356
+e 100 | 0.907  0.888 0.722 0.391
+  300 | 0.898  0.877 0.76  0.31 
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01  0.1   0.99  
+---------------------------------
+s 30  | 4.694  4.905 5.85  10.955
+i 33  | 4.682  4.907 5.516 13.182
+z 66  | 4.626  4.968 5.356 11.851
+e 100 | 4.667  4.748 5.915 10.946
+  300 | 4.172  4.225 4.739 11.571
+Run took a total of Time=160.328 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of DoubleMatrix2D assignGetSet [Mops/sec]
+type=dense
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 2.667  2.657 2.676 2.672
+i 33  | 2.645  2.645 2.645 2.645
+z 66  | 2.655  2.65  2.654 2.653
+e 100 | 2.647  2.634 2.644 2.63 
+  300 | 2.409  2.385 2.409 2.38 
+
+Performance of DoubleMatrix2D assignGetSet [Mops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 0.839  0.79  0.637 0.379
+i 33  | 0.837  0.789 0.703 0.315
+z 66  | 0.806  0.795 0.682 0.351
+e 100 | 0.814  0.796 0.693 0.386
+  300 | 0.807  0.785 0.692 0.307
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 3.18   3.365 4.2   7.042
+i 33  | 3.159  3.351 3.76  8.393
+z 66  | 3.294  3.332 3.893 7.567
+e 100 | 3.251  3.308 3.812 6.822
+  300 | 2.985  3.037 3.479 7.74 
+Run took a total of Time=156.659 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of DoubleMatrix.zMult(B,C) [Mflops/sec]
+type=dense
+      | density
+      | 0.0010  0.01    0.1    0.99  
+-------------------------------------
+s 30  |  23.649  23.584 23.632 23.617
+i 33  |  71.912  49.372 27.959 22.081
+z 66  | 172.052  94.519 33.433 33.772
+e 100 | 264.948 126.199 37.792 39.168
+  300 | 654.718 216.108 24.977 24.324
+
+Performance of DoubleMatrix.zMult(B,C) [Mflops/sec]
+type=sparse
+      | density
+      | 0.0010  0.01   0.1   0.99 
+----------------------------------
+s 30  |   1.84   1.769 1.696 1.793
+i 33  |  14.957  4.684 1.585 1.636
+z 66  |  39.289  7.778 1.56  1.674
+e 100 |  57.171 12.72  1.561 1.692
+  300 | 151.331 36.193 1.472 1.598
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  | 12.855 13.334 13.935 13.173
+i 33  |  4.808 10.54  17.635 13.498
+z 66  |  4.379 12.153 21.436 20.175
+e 100 |  4.634  9.921 24.206 23.148
+  300 |  4.326  5.971 16.965 15.224
+Run took a total of Time=208.171 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of DoubleMatrix Elementwise mult [Mflops/sec]
+type=dense
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  | 16.224 16.965 16.075 16.154
+i 33  | 15.962 15.211 16.787 17.103
+z 66  | 16.869 14.916 14.929 14.919
+e 100 | 14.73  14.709 14.713 14.601
+  300 |  6.952  6.664  6.814  9.229
+
+Performance of DoubleMatrix Elementwise mult [Mflops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 0.558  0.555 0.576 0.53 
+i 33  | 0.555  0.55  0.542 0.531
+z 66  | 0.545  0.554 0.538 0.52 
+e 100 | 0.556  0.551 0.549 0.501
+  300 | 0.555  0.54  0.52  0.486
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  | 29.102 30.563 27.902 30.486
+i 33  | 28.779 27.643 30.96  32.179
+z 66  | 30.965 26.935 27.741 28.674
+e 100 | 26.487 26.704 26.81  29.118
+  300 | 12.52  12.348 13.112 19.005
+Run took a total of Time=164.533 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of LUQuick.decompose [Mflops/sec]
+type=dense
+      | density
+      | 0.0010  0.01    0.1    0.99  
+-------------------------------------
+s 30  |  15.702  15.505  8.841  8.008
+i 33  |  18.937  17.512  9.324  8.889
+z 66  |  42.264  37.863 15.569 14.158
+e 100 |  65.513  56.832 22.099 17.438
+  300 | 181.178 134.128 32.36  21.924
+
+Performance of LUQuick.decompose [Mflops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01   0.1   0.99 
+---------------------------------
+s 30  |  4.635  3.152 1.377 0.664
+i 33  |  4.989  4.257 1.315 0.844
+z 66  | 10.5    8.077 1.622 0.647
+e 100 | 15.092 11.311 2.141 0.458
+  300 | 42.293 24.468 2.732 0.279
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01  0.1    0.99  
+----------------------------------
+s 30  | 3.388  4.919  6.421 12.066
+i 33  | 3.796  4.114  7.09  10.532
+z 66  | 4.025  4.688  9.6   21.886
+e 100 | 4.341  5.025 10.324 38.071
+  300 | 4.284  5.482 11.844 78.715
+Run took a total of Time=207.526 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of LUQuick.solve [Mflops/sec]
+type=dense
+      | density
+      | 0.0010  0.01    0.1    0.99  
+-------------------------------------
+s 30  |  77.868  76.473 30.938 12.804
+i 33  |  88.397  86.286 32.596 13.265
+z 66  | 200.525 193.055 26.704 15.917
+e 100 | 315.2   292.3   26.566 17.068
+  300 | 838.431 397.797 18.536 15.907
+
+Performance of LUQuick.solve [Mflops/sec]
+type=sparse
+      | density
+      | 0.0010  0.01   0.1   0.99 
+----------------------------------
+s 30  |  16.704 16.471 3.681 1.349
+i 33  |  18.365 18.141 3.518 1.354
+z 66  |  37.499 36.153 2.334 1.364
+e 100 |  56.859 54.296 2.185 1.375
+  300 | 169.086 33.579 1.521 1.351
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  | 4.662   4.643  8.404  9.492
+i 33  | 4.813   4.756  9.266  9.794
+z 66  | 5.347   5.34  11.443 11.665
+e 100 | 5.544   5.383 12.156 12.416
+  300 | 4.959  11.847 12.189 11.773
+Run took a total of Time=238.383 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of SOR [Mflops/sec]
+type=dense
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  | 18.375 18.597 18.528 18.66 
+i 33  | 18.537 18.575 18.372 18.407
+z 66  | 17.574 17.672 17.691 17.526
+e 100 | 17.23  17.032 16.358 17.239
+  300 | 14.786 15     14.992 15.051
+
+Performance of SOR [Mflops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 1.833  1.821 1.596 1.783
+i 33  | 1.823  1.776 1.595 1.842
+z 66  | 1.713  1.701 1.456 1.786
+e 100 | 1.687  1.656 1.478 1.602
+  300 | 1.646  1.525 1.43  1.5  
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  | 10.022 10.213 11.608 10.463
+i 33  | 10.17  10.458 11.521  9.994
+z 66  | 10.259 10.392 12.154  9.814
+e 100 | 10.212 10.285 11.068 10.758
+  300 |  8.981  9.838 10.48  10.034
+Run took a total of Time=158.007 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of Correlation [Mflops/sec]
+type=dense
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  |  8.828  6.926  4.014  6.762
+i 33  |  9.918  7.498  4.473  6.923
+z 66  | 15.391  9.958  7.727 10.371
+e 100 | 20.08  12.249 10.495 13.467
+  300 | 21.247 17.642 17.489 17.255
+
+Performance of Correlation [Mflops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 1.277  1.223 1.086 1.217
+i 33  | 1.254  1.178 1.1   1.217
+z 66  | 1.394  1.393 1.251 1.389
+e 100 | 1.487  1.474 1.433 1.47 
+  300 | 1.349  1.473 1.404 1.507
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  |  6.913  5.663  3.694  5.557
+i 33  |  7.91   6.364  4.065  5.688
+z 66  | 11.038  7.148  6.178  7.466
+e 100 | 13.499  8.311  7.322  9.16 
+  300 | 15.75  11.977 12.457 11.449
+Run took a total of Time=202.475 secs. End of run.
+
+Program execution took a total of 27.962118 minutes.
+Good bye.

Propchange: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown12pre2.txt
------------------------------------------------------------------------------
    svn:eol-style = native

Added: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown12pre2with350Mhz.txt
URL: http://svn.apache.org/viewvc/lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown12pre2with350Mhz.txt?rev=883365&view=auto
==============================================================================
--- lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown12pre2with350Mhz.txt (added)
+++ lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown12pre2with350Mhz.txt Mon Nov 23 15:14:26 2009
@@ -0,0 +1,329 @@
+Matrix benchmark running on
+
+java.vm.vendor  Sun Microsystems Inc.
+java.vm.version 1.2                  
+java.vm.name    Classic VM           
+os.name         Linux                
+os.version      2.0.35               
+os.arch         i386                 
+java.version    1.2                  
+java.vendor.url http://java.sun.com/ 
+
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of DoubleMatrix2D assign [Mops/sec]
+type=dense
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  | 37.772 40.493 37.596 39.108
+i 33  | 35.931 31.273 29.131 28.822
+z 66  | 25.905 26.243 26.078 26.423
+e 100 | 19.681 26.375 25.497 24.463
+  300 | 10.355  9.897 10.529 10.279
+
+Performance of DoubleMatrix2D assign [Mops/sec]
+type=sparse
+      | density
+      | 0.0010  0.01    0.1    0.99 
+------------------------------------
+s 30  |  34.238  19.227  7.641 1.648
+i 33  |  68.982  19.067  9.239 1.144
+z 66  | 124.51   51.374 13.159 1.351
+e 100 | 185.214  80.052 10.547 1.568
+  300 | 784.392 163.194 14.102 0.759
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01  0.1   0.99  
+---------------------------------
+s 30  | 1.103  2.106 4.92  23.727
+i 33  | 0.521  1.64  3.153 25.204
+z 66  | 0.208  0.511 1.982 19.556
+e 100 | 0.106  0.329 2.418 15.6  
+  300 | 0.013  0.061 0.747 13.545
+Run took a total of Time=174.361 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of DoubleMatrix2D assignGetSetQuick [Mops/sec]
+type=dense
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 2.532  2.521 2.523 2.526
+i 33  | 2.473  2.475 2.483 2.465
+z 66  | 2.483  2.462 2.486 2.363
+e 100 | 2.489  2.454 2.493 2.49 
+  300 | 2.227  2.255 2.246 2.217
+
+Performance of DoubleMatrix2D assignGetSetQuick [Mops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 0.503  0.477 0.415 0.227
+i 33  | 0.501  0.474 0.421 0.188
+z 66  | 0.485  0.479 0.429 0.209
+e 100 | 0.489  0.479 0.414 0.228
+  300 | 0.487  0.473 0.425 0.176
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01  0.1   0.99  
+---------------------------------
+s 30  | 5.033  5.289 6.085 11.107
+i 33  | 4.932  5.22  5.901 13.082
+z 66  | 5.115  5.144 5.789 11.32 
+e 100 | 5.09   5.126 6.021 10.909
+  300 | 4.571  4.763 5.291 12.632
+Run took a total of Time=158.716 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of DoubleMatrix2D assignGetSet [Mops/sec]
+type=dense
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 1.496  1.46  1.491 1.489
+i 33  | 1.473  1.473 1.469 1.471
+z 66  | 1.465  1.478 1.475 1.476
+e 100 | 1.469  1.479 1.483 1.481
+  300 | 1.275  1.377 1.353 1.375
+
+Performance of DoubleMatrix2D assignGetSet [Mops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 0.443  0.419 0.371 0.214
+i 33  | 0.442  0.418 0.376 0.169
+z 66  | 0.426  0.421 0.382 0.19 
+e 100 | 0.43   0.422 0.372 0.216
+  300 | 0.428  0.417 0.377 0.173
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 3.375  3.485 4.014 6.959
+i 33  | 3.336  3.526 3.909 8.695
+z 66  | 3.436  3.515 3.858 7.782
+e 100 | 3.42   3.505 3.991 6.858
+  300 | 2.98   3.298 3.594 7.964
+Run took a total of Time=157.747 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of DoubleMatrix.zMult(B,C) [Mflops/sec]
+type=dense
+      | density
+      | 0.0010  0.01    0.1    0.99  
+-------------------------------------
+s 30  |  10.028  10.018 10.028 10.018
+i 33  |  36.256  24.784 13.77  13.385
+z 66  |  85.718  44.97  16.937 17.048
+e 100 | 132.518  65.163 19.305 19.675
+  300 | 340.157 118.317 16.206 13.957
+
+Performance of DoubleMatrix.zMult(B,C) [Mflops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01   0.1   0.99 
+---------------------------------
+s 30  |  0.887  0.858 0.828 0.668
+i 33  |  8.227  2.699 0.875 0.937
+z 66  | 21.19   4.479 0.908 0.975
+e 100 | 30.907  7.303 0.908 0.985
+  300 | 82.537 20.493 0.853 0.947
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  | 11.305 11.676 12.107 14.987
+i 33  |  4.407  9.182 15.74  14.283
+z 66  |  4.045 10.04  18.646 17.485
+e 100 |  4.288  8.923 21.264 19.98 
+  300 |  4.121  5.773 19.001 14.738
+Run took a total of Time=253.718 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of DoubleMatrix Elementwise mult [Mflops/sec]
+type=dense
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 6.684  6.63  6.589 6.751
+i 33  | 6.677  6.713 6.757 6.862
+z 66  | 6.982  7.058 7.068 7    
+e 100 | 7.274  6.383 6.856 7.334
+  300 | 4.238  4.105 4.142 4.128
+
+Performance of DoubleMatrix Elementwise mult [Mflops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 0.311  0.311 0.296 0.317
+i 33  | 0.304  0.304 0.303 0.3  
+z 66  | 0.308  0.309 0.3   0.277
+e 100 | 0.309  0.308 0.308 0.281
+  300 | 0.309  0.301 0.284 0.205
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  | 21.489 21.333 22.24  21.295
+i 33  | 22.001 22.089 22.279 22.855
+z 66  | 22.701 22.811 23.522 25.308
+e 100 | 23.511 20.745 22.288 26.128
+  300 | 13.71  13.638 14.583 20.089
+Run took a total of Time=162.957 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of LUQuick.decompose [Mflops/sec]
+type=dense
+      | density
+      | 0.0010  0.01   0.1    0.99  
+------------------------------------
+s 30  |   9.552  8.783  5.388  5.136
+i 33  |  10.741  9.897  5.81   5.782
+z 66  |  24.068 21.47  10.347  9.467
+e 100 |  37.491 32.28  14.422 11.832
+  300 | 105.263 77.847 21.713 16.158
+
+Performance of LUQuick.decompose [Mflops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01   0.1   0.99 
+---------------------------------
+s 30  |  2.575  2.133 0.702 0.353
+i 33  |  2.747  2.342 0.663 0.424
+z 66  |  5.774  4.442 0.802 0.319
+e 100 |  8.222  6.143 1.062 0.237
+  300 | 23.377 13.319 1.318 0.145
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01  0.1    0.99   
+-----------------------------------
+s 30  | 3.709  4.119  7.672  14.538
+i 33  | 3.91   4.226  8.765  13.647
+z 66  | 4.169  4.834 12.909  29.649
+e 100 | 4.56   5.255 13.58   50.015
+  300 | 4.503  5.845 16.474 111.603
+Run took a total of Time=275.21 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of LUQuick.solve [Mflops/sec]
+type=dense
+      | density
+      | 0.0010  0.01    0.1    0.99  
+-------------------------------------
+s 30  |  45.625  37.152 20.652  9.721
+i 33  |  52.179  51.074 22.264 10.256
+z 66  | 118.886 112.129 21.499 13.246
+e 100 | 187.056 176.012 22.422 14.846
+  300 | 508.467 278.274 16.231 14.433
+
+Performance of LUQuick.solve [Mflops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01   0.1   0.99 
+---------------------------------
+s 30  |  9.399  9.253 1.757 0.611
+i 33  |  9.867 10.165 1.8   0.61 
+z 66  | 21.034 20.108 1.073 0.566
+e 100 | 31.889 30.134 0.993 0.617
+  300 | 94.803 16.99  0.68  0.61 
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  | 4.854   4.015 11.751 15.909
+i 33  | 5.289   5.025 12.37  16.823
+z 66  | 5.652   5.576 20.036 23.397
+e 100 | 5.866   5.841 22.588 24.047
+  300 | 5.363  16.379 23.852 23.654
+Run took a total of Time=350.137 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of SOR [Mflops/sec]
+type=dense
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 6.733  6.914 6.899 6.894
+i 33  | 6.881  6.873 6.713 6.787
+z 66  | 6.473  6.394 6.393 6.397
+e 100 | 6.387  6.332 6.344 6.347
+  300 | 5.543  5.552 5.838 5.819
+
+Performance of SOR [Mflops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 1.056  1.065 0.875 1.163
+i 33  | 1.056  1.043 0.981 1.081
+z 66  | 0.993  0.988 0.882 1.063
+e 100 | 0.967  0.924 0.83  0.953
+  300 | 0.953  0.913 0.838 0.958
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 6.375  6.493 7.889 5.929
+i 33  | 6.518  6.589 6.841 6.278
+z 66  | 6.517  6.47  7.252 6.018
+e 100 | 6.606  6.853 7.64  6.661
+  300 | 5.815  6.079 6.965 6.074
+Run took a total of Time=151.213 secs. End of run.
+
+@x....x....x....x....x....
+@x....x....x....x....x....*
+Performance of Correlation [Mflops/sec]
+type=dense
+      | density
+      | 0.0010 0.01  0.1    0.99 
+---------------------------------
+s 30  |  5.384 3.978  2.221 3.9  
+i 33  |  5.831 3.993  2.471 4.151
+z 66  |  8.94  5.459  3.998 5.825
+e 100 | 10.052 6.737  5.752 7.301
+  300 | 12.724 9.938 10.131 9.637
+
+Performance of Correlation [Mflops/sec]
+type=sparse
+      | density
+      | 0.0010 0.01  0.1   0.99 
+--------------------------------
+s 30  | 0.757  0.719 0.63  0.689
+i 33  | 0.761  0.683 0.638 0.69 
+z 66  | 0.799  0.778 0.745 0.819
+e 100 | 0.867  0.862 0.831 0.889
+  300 | 0.772  0.855 0.813 0.896
+
+Speedup of dense over sparse
+      | density
+      | 0.0010 0.01   0.1    0.99  
+-----------------------------------
+s 30  |  7.115  5.533  3.527  5.664
+i 33  |  7.664  5.842  3.872  6.017
+z 66  | 11.196  7.018  5.365  7.116
+e 100 | 11.595  7.817  6.92   8.212
+  300 | 16.472 11.624 12.463 10.76 
+Run took a total of Time=254.412 secs. End of run.
+
+Program execution took a total of 32.3259 minutes.
+Good bye.

Propchange: lucene/mahout/trunk/matrix/src/main/java/org/apache/mahout/matrix/matrix/doc-files/perfBlackdown12pre2with350Mhz.txt
------------------------------------------------------------------------------
    svn:eol-style = native



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