\KwOut{NewNb: array containing random numbers in global memory}
\If{threadId is concerned} {
- retrieve data from InternalVarXorLikeArray[threadId] in local variables\;
+ retrieve data from InternalVarXorLikeArray[threadId] in local variables including shared memory\;
offset = threadIdx\%permutation\_size\;
o1 = threadIdx-offset+tab1[offset]\;
o2 = threadIdx-offset+tab2[offset]\;
\For{i=1 to n} {
t=xor-like()\;
- shared\_mem[threadId]=(unsigned int)t\;
- x = x $\oplus$ (unsigned int) t\;
- x = x $\oplus$ (unsigned int) (t>>32)\;
- x = x $\oplus$ shared[o1]\;
- x = x $\oplus$ shared[o2]\;
+ t=t$\oplus$shmem[o1]$\oplus$shmem[o2]\;
+ shared\_mem[threadId]=t\;
+ x = x $\oplus$ t\;
store the new PRNG in NewNb[NumThreads*threadId+i]\;
}
\subsection{Theoretical Evaluation of the Improved Version}
-A run of Algorithm~\ref{algo:gpu_kernel2} consists in four operations having
+A run of Algorithm~\ref{algo:gpu_kernel2} consists in three operations having
the form of Equation~\ref{equation Oplus}, which is equivalent to the iterative
-system of Eq.~\ref{eq:generalIC}. That is, four iterations of the general chaotic
+system of Eq.~\ref{eq:generalIC}. That is, three iterations of the general chaotic
iterations are realized between two stored values of the PRNG.
To be certain that we are in the framework of Theorem~\ref{t:chaos des general},
we must guarantee that this dynamical system iterates on the space
cards have 240 cores.
In Figure~\ref{fig:time_gpu} we compare the number of random numbers generated
-per second. In order to obtain the optimal performance we remove the storage of
-random numbers in the GPU memory. This step is time consumming and slows down
-the random number generation. Moreover, if you are interested by applications
-that consome random numbers directly when they are generated, their storage is
-completely useless. In this figure we can see that when the number of threads is
-greater than approximately 30,000 upto 5 millions the number of random numbers
-generated per second is almost constant. With the naive version, it is between
-2.5 and 3GSample/s. With the optimized version, it is approximately equals to
+per second. The xor-like prng is a xor64 described in~\cite{Marsaglia2003}. In
+order to obtain the optimal performance we remove the storage of random numbers
+in the GPU memory. This step is time consumming and slows down the random number
+generation. Moreover, if you are interested by applications that consome random
+numbers directly when they are generated, their storage is completely
+useless. In this figure we can see that when the number of threads is greater
+than approximately 30,000 upto 5 millions the number of random numbers generated
+per second is almost constant. With the naive version, it is between 2.5 and
+3GSample/s. With the optimized version, it is approximately equals to
20GSample/s. Finally we can remark that both GPU cards are quite similar. In
practice, the Tesla C1060 has more memory than the GTX 280 and this memory
should be of better quality.
