29%
02.04.2013
.com’s “Starter Edition” is completely free of charge. Larger packages start from US$ 3,000 (EUR 2,000) per year, but include 500 users and 10,000 authentication operations (Premium), or 10,000 users and 250,000
29%
20.03.2014
-node cluster with support on weekdays costs no less than 9,000 Euros, while the same cluster with 24x7 support is priced at 14,000 Euros. Realistically, however, no one will operate cloud storage with only two
29%
05.11.2013
decided to look for an intermediate path, introducing the Xeon Phi accelerator at the beginning of this year. The Xeon Phi, which is based on x86 technology, has received more attention in recent months
29%
25.09.2013
the code states should be good enough for caches up to 20MB. The Stream FAQ recommends you use a problem size such that each array is four times the sum of the caches (L1, L2, and L3). You can either change
29%
09.01.2013
http://opennebula.org/repo/ openSUSE/12.3/stable/x86_64 opennebula
zypper refresh
zypper install opennebula
zypper install opennebula-sunstone
For Debian and Ubuntu, a tarball is available with several
29%
10.06.2024
in gigaflops per watt over time. The first Green500 list was in June 2013. The number 1 system used GPUs even then (NVIDIA K20 with QDR InfiniBand). The energy efficiency was 3,208.8Mflops/W (0.32Gflops
29%
17.09.2013
–17 (seven orders of magnitude difference). The lower number is just about one error per gigabit of memory per hour. The upper number indicates roughly one error every 1,000 years per gigabit of memory
29%
21.12.2011
clock time = 46.156027 seconds
cpu clock time = 46.160000 seconds
Iterations = 7
Final Relative Residual Norm = 3.535135e-07
[openss]: Converting raw data from /home/jeg/chaos_4_x86_64_ib
29%
05.12.2014
(colour = guide_legend(override.aes = list(size=3)))
+ theme(axis.text.x
= element_text(face = "bold", color="black"), axis.text.y
= element_text(face = "bold", color="black"), axis.title.x
= element
29%
08.06.2021
numpy as np
nx = 100
ny = 100
a = np.random.rand(nx,ny)
b = np.random.rand(ny)
x = np.linalg.solve(a, b)
Array a
and the second part of the tuple, b
,
are created by a random number generator with random
