X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/11f93a2e8880680f6b192298e5ce0697d2596a31..1874c46934f4ba7e8c2013d3829f65309456d292:/BookGPU/Chapters/chapter6/PartieAsync.tex

diff --git a/BookGPU/Chapters/chapter6/PartieAsync.tex b/BookGPU/Chapters/chapter6/PartieAsync.tex
index 3365b41..0253c9c 100644
--- a/BookGPU/Chapters/chapter6/PartieAsync.tex
+++ b/BookGPU/Chapters/chapter6/PartieAsync.tex
@@ -6,7 +6,7 @@ In the previous section, we have seen how to efficiently implement overlap of
 computations (CPU and GPU) with communications (GPU transfers and internode
 communications).  However, we have previously shown that for some parallel
 iterative algorithms, it is sometimes even more efficient to use an asynchronous
-scheme of iterations\index{iterations!asynchronous} \cite{HPCS2002,ParCo05,Para10}.  In that case, the nodes do
+scheme of iterations\index{iterations asynchronous} \cite{HPCS2002,ParCo05,Para10}.  In that case, the nodes do
 not wait for each other but they perform their iterations using the last
 external data they have received from the other nodes, even if this
 data was produced \emph{before} the previous iteration on the other nodes.
@@ -887,7 +887,7 @@ the CPU may vary depending on the application. For example, when processing data
 streams (pipelines), pre-processing of the next data item and/or post-processing
 of the previous result can be done on the CPU while the GPU is processing the current
 data item.  In other cases, the CPU can perform \emph{auxiliary}
-computations\index{computation!auxiliary}
+computations\index{computation auxiliary}
 that are not absolutely required to obtain the result but that may accelerate
 the entire iterative process.  Another possibility would be to distribute the
 main computations between the GPU and CPU. However, this
