X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/prng_gpu.git/blobdiff_plain/0264ec3e2a52ec399fb117c69cd7d8ba6fc1c16a..558affb9cf9a30a05a5e35a9f4413ee24d66fa5b:/prng_gpu.tex?ds=inline

diff --git a/prng_gpu.tex b/prng_gpu.tex
index 24d5fc6..3a677e2 100644
--- a/prng_gpu.tex
+++ b/prng_gpu.tex
@@ -182,13 +182,13 @@ based on  Lagged Fibonacci or Hybrid  Taus.  They have  used these
 PRNGs   for  Langevin   simulations   of  biomolecules   fully  implemented   on
 GPU. Performance of  the GPU versions are far better than  those obtained with a
 CPU, and these PRNGs succeed to pass the {\it BigCrush} battery of TestU01. 
-However the evaluations of the proposed PRNGs are only statistical.
+However the evaluations of the proposed PRNGs are only statistical ones.
 
 
 Authors of~\cite{conf/fpga/ThomasHL09}  have studied the  implementation of some
 PRNGs on  diferrent computing architectures: CPU,  field-programmable gate array
 (FPGA), GPU and massively parallel  processor. This study is interesting because
-it  shows the  performance  of the  same  PRNGs on  different architeture.   For
+it  shows the  performance  of the  same  PRNGs on  different architectures.   For
 example,  the FPGA  is globally  the  fastest architecture  and it  is also  the
 efficient one because it provides the fastest number of generated random numbers
 per joule. Concerning the GPU,  authors can generate betweend 11 and 16GSample/s