17%
13.12.2018
disk reads: 1306 MB in 3.00 seconds = 434.77 MB/sec
federico@cybertron:~$ sudo hdparm -W /dev/sdb
/dev/sdb:
write-caching = 1 (on)
federico@cybertron:~$ sudo hdparm -W 0 /dev/sdb
/dev/sdb:
write
17%
18.07.2013
100
100
000
Old_age
Always
–
2456
12
Power_Cycle_Count
0x0032
100
100
000
Old_age
Always
17%
14.11.2013
Controller
login2$ ls -s /sys/devices/system/edac/mc/mc0
total 0
0 ce_count 0 csrow1 0 csrow4 0 csrow7 0 reset_counters 0 size_mb
0 ce_noinfo_count 0 csrow2 0 csrow5 0 device 0 sdram
17%
30.01.2020
]
test: (groupid=0, jobs=1): err= 0: pid=1225: Sat Oct 12 19:20:18 2019
write: IOPS=168k, BW=655MiB/s (687MB/s)(10.0GiB/15634msec); 0 zone resets
[ ... ]
Run status group 0 (all jobs):
WRITE: bw=655Mi
17%
19.11.2019
Jobs: 1 (f=1): [w(1)][100.0%][w=654MiB/s][w=167k IOPS][eta 00m:00s]
test: (groupid=0, jobs=1): err= 0: pid=1225: Sat Oct 12 19:20:18 2019
write: IOPS=168k, BW=655MiB/s (687MB/s)(10.0GiB/15634msec); 0
16%
30.11.2025
); i+= 4096) newblock[i] = 'Y';
12 printf("Allocated %d MB\n", allocation);
13 }
14 }
Things are more interesting when memory is being used. Uncommenting line 11 does just that. The OOM
16%
16.10.2012
:1500 Metric:1
RX packets:495050 errors:0 dropped:0 overruns:0 frame:0
TX packets:26284 errors:0 dropped:0 overruns:0 carrier:0
collisions:0 txqueuelen:1000
RX
16%
02.02.2021
section.
Listing 1
Creating a Time Series
01 import numpy as np
02 import plotly.graph_objects as go
03
04 step = 1 / 1000 t = np.arange(0, 1, step) # time vector
05 periods = 30 # number
16%
24.02.2022
.255.255.255 broadcast 0.0.0.0
inet6 fe80::bfd3:1a4b:f76b:872a prefixlen 64 scopeid 0x20
ether 42:01:0a:80:00:02 txqueuelen 1000 (Ethernet)
RX packets 11919 bytes 61663030 (58.8 Mi
16%
07.04.2022
,BROADCAST,RUNNING,MULTICAST> mtu 1460
inet 10.0.0.2 netmask 255.255.255.255 broadcast 0.0.0.0
inet6 fe80::bfd3:1a4b:f76b:872a prefixlen 64 scopeid 0x20
ether 42:01:0a:80:00:02 txqueuelen 1000
