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Building a virtual NVMe drive
25.03.2020
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Lead Image © lassedesignen, 123RF
7 1 56008 loop1 06 7 2 56184 loop2 07 7 3 91264 loop3 08 259 0 244198584 nvme0n1 09 8 0 488386584 sda 10 8 1 1024 sda1 11
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Linux device mapper writecache
30.01.2020
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Lead Image © lightwise, 123R.com
Conversion $ sudo lvs -a vg-cache -o devices,segtype,lvattr,name,vgname,origin Devices Type Attr LV VG Origin /dev/nvme0n1(0) linear Cwi-aoC--- [fast
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Unleashing Accelerated Speeds with RAM Drives
02.08.2021
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Lead Image © Vladimir Nenov, 123RF.com
nvmet-rd0 -a 10.0.0.185 -s 4420 and verify that the NVMe subsystem sees the NVMe target (Listing 9). Listing 9 Verify the NVMe Target Is Seen $ sudo nvme list Node SN
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Processor and Memory Metrics
12.02.2014
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(total), "=" * 33)) >         sys.stdout.write("%sB \n" % human(total)); 449c450 <     print_header() --- >     #print_header() 475a477 > You can apply this patch to the file as patch ps_mem.py where
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Monitoring events with the Audit daemon
17.02.2015
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Lead Image © alphaspirit , 123RF.com
e syscall=2 success=yes exit=3 a0=7fff67b1e9fc a1=0 a2=1fffffffffff0000 a3=3109e85ad0 items=1 ppid=7144 pid=11992 auid=1000 uid=1000 gid=1000 euid=1000 suid=1000 fsuid=1000 egid=1000 sgid=1000
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Have a Bash with the Zing network utility
25.09.2023
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Photo by Micah Tindell on Unsplash
for Internal Network zing.bash -c 4 -op 2 -p 80,443 10.0.0.23 ZING:80/ 10.0.0.23 / Warning: Error: on 80 is: Active. Continue. Port: 80: op 1.1. 10.0.0.23 80 Time: 28 ms. Port: 80: op 1.2. 10.0.0.23 80 Time
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Securing the container environment
01.06.2024
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Lead Image © zeferli, 123RF.com
Running kube-proxy 1 5bf2de2a3af3c kube-proxy-b65c9 The mitigations for the above technique could be used to ensure that no containers mount docker
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Pandas: Data analysis with Python
20.03.2014
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Lead Image © yang chao, 123RF.com
, 2) Out: array([3, 5, 7, 9]) to generate a sequence from 3 to 10 with a step size of 2. Basic Arithmetic Operations NumPy allows many operations applied against all elements of an array without
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Data Analysis with Panda
12.03.2014
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]}) An optional index list determines the indices, as for a Series. In: DataFrame({'a': [1, 2], 'b': [3, 4]}, columns=['a', 'c'], index=['top', 'bottom']) Out: a c top 1 NaN bottom 2 NaN
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Tuning loops – from loop unrolling to Duff's device
28.07.2025
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[i] + b[i]; } When the number of cycles is known at compile time, a loop can be fully unrolled: c[0] = a[0] + b[0]; c[1] = a[1] + b[1]; c[2] = a[2] + b[2]; c[3] = a[3] + b[3]; However, it remains

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