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[prng_gpu.git] / prng_gpu.tex

diff --git a/prng_gpu.tex b/prng_gpu.tex
index 9de2d15de29484c0c8eb1457cef58847bc4c70f9..3a677e2c2928640219232d2d3a4b926895dfb196 100644 (file)
--- a/prng_gpu.tex
+++ b/prng_gpu.tex
@@ -160,30 +160,35 @@ summarized and intended future work is presented.
 
 \section{Related works on GPU based PRNGs}
 \label{section:related works}
 
 \section{Related works on GPU based PRNGs}
 \label{section:related works}

-In the litterature many authors have work on defining GPU based PRNGs. We do not
-want to be exhaustive and we just give the most significant works from our point
-of view. When authors mention the  number of random numbers generated per second
-we mention  it. We  consider that  a million numbers  per second  corresponds to
-1MSample/s and than a billion numbers per second corresponds to 1GSample/s.
-
-In \cite{Pang:2008:cec},  the authors define  a PRNG based on  cellular automata
-which  does   not  require  high  precision  integer   arithmetics  nor  bitwise
-operations. There is no mention of statistical tests nor proof that this PRNG is
-chaotic.  Concerning   the  speed  of   generation,  they  can   generate  about
-3.2MSample/s on a GeForce 7800 GTX GPU (which is quite old now).
+
+Numerous research works on defining GPU based PRNGs have yet been proposed  in the
+literature, so that completeness is impossible.
+This is why authors of this document only give reference to the most significant attempts 
+in this domain, from their subjective point of view. 
+The  quantity of pseudorandom numbers generated per second is mentioned here 
+only when the information is given in the related work. 
+A million numbers  per second will be simply written as
+1MSample/s whereas a billion numbers per second is 1GSample/s.
+
+In \cite{Pang:2008:cec}  a PRNG based on  cellular automata is defined
+with no  requirement to an high  precision  integer   arithmetic  or to any bitwise
+operations. Authors can   generate  about
+3.2MSample/s on a GeForce 7800 GTX GPU, which is quite an old card now.
+However, there is neither a mention of statistical tests nor any proof of
+chaos or cryptography in this document.

 
 In \cite{ZRKB10}, the authors propose  different versions of efficient GPU PRNGs
 
 In \cite{ZRKB10}, the authors propose  different versions of efficient GPU PRNGs

-based on  Lagged Fibonacci, Hybrid  Taus or Hybrid  Taus.  They have  used these
+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
 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} test of TestU01. There is
-no mention that their PRNGs have chaos mathematical properties.
+CPU, and these PRNGs succeed to pass the {\it BigCrush} battery of TestU01. 
+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
 
 
 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
 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