9%
11.04.2016
.40 0.00 0.54 1.66 0.00 96.39
Device: rrqm/s wrqm/s r/s w/s rMB/s wMB/s avgrq-sz avgqu-sz await r_await w_await svctm %util
sda 393.19 2.17 137.48 2
9%
28.03.2012
--------><----------Disks-----------><----------Network---------->
#cpu sys inter ctxsw KBRead Reads KBWrit Writes KBIn PktIn KBOut PktOut
3 1 1421 2168 0 0 41000 90 0 2 0 0
3 2 1509 2198 64 2 49712
9%
11.02.2016
amount of available RAM: 117,080MB in this case.
Mem used
: Amount of RAM used by the applications: 48,810MB in this case.
Mem free
: Potentially free RAM: 68,270MB in this case.
Mem cached
: RAM
9%
05.08.2024
; i < size; i++ {
10 for j := 0; j < size; j++ {
11 array[j][i]++
12 }
13 }
14
15 }
Running the same test produces the results in Figure 3 – there indeed
9%
17.02.2015
://developer.nvidia.com/jetson-tk1
Odroid-XU3
Android 4.4, Linux
Samsung Exynos5422
Quad ARM Cortex-A15 @2.0GHz (32KB/32KB/2MB), Quad ARM Cortex-A7 @1.4GHz (32KB/32KB/512KB)
Mali-T628 MP6
9%
29.09.2020
-line operations.
To install Dockly [3], you can choose one of two routes: with npm (see the "Installation by npm" box for that route) and in a Docker container. For context, on my laptop, about 43MB of file space
9%
05.11.2013
, mainly because it is inside the world’s fastest supercomputer – the Tianhe-2; in fact, the 48,000 Xeon Phi cards built in to the Tianhe-2 help it deliver nearly twice the raw performance of the second
9%
09.04.2019
in mv
ubuntu@aws:~/slow-mv$ strace -t mv 3GB.copy 3GB
19:00:09 execve("/bin/mv", ["mv", "3GB.copy", "3GB"], 0x7ffd0e7dddf8 /* 21 vars */) = 0
19:00:09 brk(NULL) = 0x55cd7d1ce000
9%
12.05.2020
TAG IMAGE ID CREATED SIZE
nvidia/cuda 10.1-base-ubuntu18.04 3b55548ae91f 4 months ago 106MB
hello-world latest fce289e99eb9 16 months ago 1.84kB
Running the nvidia
9%
13.12.2022
-export-libs-9.11.36-3.el8_6.1.x86_64.rpm 579 kB/s | 1.1 MB 00:02
(6/6): warewulf-4.3.0-1.git_235c23c.el8.x86_64.rpm 746 kB/s | 8.3 MB 00
