From 45058aa2e4a1b5f9ef8367a2605dc55fd36ff23d Mon Sep 17 00:00:00 2001 From: couturie Date: Sun, 13 Nov 2011 19:52:00 +0100 Subject: [PATCH] more --- prng_gpu.tex | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/prng_gpu.tex b/prng_gpu.tex index c48aeda..23fc776 100644 --- a/prng_gpu.tex +++ b/prng_gpu.tex @@ -956,18 +956,22 @@ Devaney's formulation of a chaotic behavior. Different experiments have been performed in order to measure the generation speed. We have used a computer equiped with Tesla C1060 NVidia GPU card and an -Intel Xeon E5530 cadenced at 2.40 GHz for our experiments. +Intel Xeon E5530 cadenced at 2.40 GHz for our experiments and we have used +another one equipped with a less performant CPU and a GeForce GTX 280. Both +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 number we remove the storage of +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 number directly when they are generated, their storage is +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 almost equals to -20GSample/s. +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. \begin{figure}[htbp] \begin{center} -- 2.39.5