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[book_gpu.git] / BookGPU / Chapters / chapter2 / ch2.tex

diff --git a/BookGPU/Chapters/chapter2/ch2.tex b/BookGPU/Chapters/chapter2/ch2.tex
index 501e34e1948ea29d293260a0de7bf69a09d4f7cf..c33ac50dbe4b52ba7443a8d3294c2aa3816e06ca 100755 (executable)
--- a/BookGPU/Chapters/chapter2/ch2.tex
+++ b/BookGPU/Chapters/chapter2/ch2.tex
@@ -137,6 +137,8 @@ consider  we  have  a  squared  matrix  of size  \texttt{size}.  So  with  a  1D
 array, \texttt{A[i*size+j]} allows  us to access to the  element of the $i^{th}$
 row and of the $j^{th}$ column.
 
 array, \texttt{A[i*size+j]} allows  us to access to the  element of the $i^{th}$
 row and of the $j^{th}$ column.
 

+In sequential the matrix multiplication is performed using three loops. Supposing that $A$, $B$ represent two square matrices, the result of the multiplication of $A \times B$ is 
+

 On C2070M Tesla card, this code take 37.68ms to perform the multiplication. On a
 Intel Xeon E31245 at 3.30GHz, it takes 2465ms without any parallelization (using
 only one  core). Consequently the  speed up between  the CPU and GPU  version is
 On C2070M Tesla card, this code take 37.68ms to perform the multiplication. On a
 Intel Xeon E31245 at 3.30GHz, it takes 2465ms without any parallelization (using
 only one  core). Consequently the  speed up between  the CPU and GPU  version is