14%
03.02.2022
,32
1,33
2,34
3,35
4,36
5,37
6,38
7,39
8,40
9,41
10,42
11,43
12,44
13,45
14,46
15,47
16,48
17,49
18,50
19,51
20,52
21,53
22,54
23,55
24,56
25,57
26,58
27,59
28,60
29,61
30,62
31,63
The lstopo tool
14%
12.05.2021
30.85 72.31 13.16 20.40 0.26 70.44 83.89 1.97 3.52
nvme0n1 58.80 12.22 17720.47 48.71 230.91 0.01 79.70 0.08 0.42 0.03 0.00 301.34 3
14%
14.09.2021
$(find /sys/devices/system/cpu -regex ".*cpu[0-9]+/topology/thread_siblings_list") | sort -n | uniq
0,32
1,33
2,34
3,35
4,36
5,37
6,38
7,39
8,40
9,41
10,42
11,43
12,44
13,45
14,46
15,47
16,48
17,49
18,50
19,51
20,52
21,53
22,54
23,55
24,56
25
14%
06.10.2022
in practice, because Rancher comes with its own Kubernetes core distribution in tow in the form of K3s, which provides all the features you need.
In this article, I guide you through the implementation
14%
18.06.2014
]: 13502 ( 3.48%) ( 87.59% cumulative)
[ 64- 128 KB]: 12083 ( 3.11%) ( 90.70% cumulative)
[ 128- 256 KB]: 8623 ( 2.22%) ( 92.93% cumulative)
[ 256- 512 KB]: 13437 ( 3
14%
07.06.2019
for clarity.)
Listing 2
Viewing the Log
$ docker logs 0f8a0
[INFO] Syncing group took 5.293130159378052 sec
[INFO] Processing group: alpine:3.4
"172.20.0.3" "POST /v1/queues
14%
04.08.2020
in the standard Python built-ins [2] and in the NumPy library [3].
Figure 1: IPython session comparing two implementations of a round routine.
Unless you
14%
25.03.2020
1
16 8 80 39078144 sdf
17 8 48 6836191232 sdd
18 8 49 6836189184 sdd1
19 8 32 6836191232 sdc
20 8 33 6836189184 sdc1
21 11 0 1048575 sr0
14%
17.05.2017
, ALLOCATABLE, TARGET :: DATA(:,:) ! Data to write
18 INTEGER :: RANK = 2 ! Dataset rank
19
20 CHARACTER(MPI_MAX_PROCESSOR_NAME) HOSTNAME
21 CHARACTER(LEN=100) :: FILENAME ! File name
22 CHARACTER(LEN=3) :: C
14%
30.01.2020
without profiling).
Listing 3
pprofile Output
Command line: md_002.py
Total duration: 1662.48s
File: md_002.py
File duration: 1661.74s (99.96%)
Line #| Hits| Time| Time per hit
