new

diff --git a/biblio.bib b/biblio.bib
index 55c4a932db5b6579d0abfd30c66f040e635f46ca..e45f5d6dde0823081b05ba1b56beaede7a6f99b6 100644 (file)
--- a/biblio.bib
+++ b/biblio.bib
@@ -77,7 +77,14 @@
 @TechReport{prace-multi,
        author =                         {Nick Brown and J. Mark Bull and Iain Bethune},
        title =                          {Solving Large Sparse Linear Systems using Asynchronous Multisplitting},
 @TechReport{prace-multi,
        author =                         {Nick Brown and J. Mark Bull and Iain Bethune},
        title =                          {Solving Large Sparse Linear Systems using Asynchronous Multisplitting},

-       institution =  {PRACE White paper n°WP84},
+       institution =  {PRACE White paper number WP84},

        year =                           {2013},
 }
 
        year =                           {2013},
 }
 

+@Book{S96,
+        author =         {Y. Saad},
+        title =          {Iterative Methods for Sparse Linear Systems},
+        publisher =      {PWS Publishing},
+        year =           {1996},
+        address =        {New York},
+}

diff --git a/krylov_multi.tex b/krylov_multi.tex
index 40380d0f50678f162ca474373e64278fbf915124..91e47458d85749235dbe5d7e5106fecee13efcf2 100644 (file)
--- a/krylov_multi.tex
+++ b/krylov_multi.tex
@@ -38,9 +38,14 @@ classical GMRES both in terms of number of iterations and execution times.
 
 Iterative methods are used to solve  large sparse linear systems of equations of
 the form  $Ax=b$ because they are  easier to parallelize than  direct ones. Many
 
 Iterative methods are used to solve  large sparse linear systems of equations of
 the form  $Ax=b$ because they are  easier to parallelize than  direct ones. Many

-iterative  methods have  been proposed  and  adapted by  many researchers.  When
-solving large  linear systems  with many cores,  iterative methods  often suffer
-from  scalability  problems.    This  is  due  to  their   need  for  collective
+iterative  methods have  been proposed  and  adapted by  many researchers.   For
+example, the GMRES method and the  Conjugate Gradient method are very well known
+and  used by  many researchers  ~\cite{S96}. Both  the method  are based  on the
+Krylov subspace which consists in forming  a basis of the sequence of successive
+matrix powers times the initial residual.
+
+When  solving large  linear systems  with  many cores,  iterative methods  often
+suffer  from scalability problems.   This is  due to  their need  for collective

 communications  to  perform  matrix-vector  products and  reduction  operations.
 Preconditionners can be  used in order to increase  the convergence of iterative
 solvers.   However, most  of the  good preconditionners  are not  sclalable when
 communications  to  perform  matrix-vector  products and  reduction  operations.
 Preconditionners can be  used in order to increase  the convergence of iterative
 solvers.   However, most  of the  good preconditionners  are not  sclalable when