62%
20.06.2012
:root
bin:x:1:root,bin,daemon
daemon:x:2:root,bin,daemon
sys:x:3:root,bin,adm
adm:x:4:root,adm,daemon
tty:x:5:
disk:x:6:root
lp:x:7:daemon,lp
mem:x:8:
kmem:x:9:
wheel:x:10:root
mail:x:12:mail
uucp:x:14 ... Warewulf Cluster Manager – Part 2
62%
30.11.2020
runtime is surrounded by confusion and ambiguity. The CRI-O runtime [3] is an Open Container Initiative (OCI)-compliant container runtime. Both runC and Kata Containers are currently supported
62%
28.11.2023
36 [notify]
37 startup_notification = true
38 reminder_interval = 300
39
40 [notify.webhook]
41 hook_url = "https://webhook.site/4406e2a4-13cd-4c99-975c-d3456a148b26"
42
43 [probe]
44 [[probe
62%
24.02.2022
machines; (2) the Metadata Service (MDS), which contains Metadata Targets (MDTs); (3) Object Storage Services (OSS), which store file data on one or more Object Storage Targets (OSTs); and (4) the clients
62%
21.01.2021
This first article of a series looks at the forces that have driven desktop supercomputing, beginning with the history of PC and supercomputing processors through the 1990s into the early 2000s.
...
May 1988
AMD K6-2
MMX and 3DNOW! SIMD, 200–570MHz; 64KiB L1 cache
Jun 1998
Pentium II Xeon
SIMD; L2 cache from 512KB to 2MB
Feb 1999
Pentium III
9 ...
This first article of a series looks at the forces that have driven desktop supercomputing, beginning with the history of PC and supercomputing processors through the 1990s into the early 2000s.
62%
05.11.2018
One way to share HPC systems among several users is to use a software tool called a resource manager. Slurm, probably the most common job scheduler in use today, is open source, scalable, and easy ... infinite 4/9/3/16 node[212-213,215-218,220-229]
This example lists the status, time limit, node information, and node list of the p100 partition.
sbatch
To submit a batch serial job to Slurm, use the ...
One way to share HPC systems among several users is to use a software tool called a resource manager. Slurm, probably the most common job scheduler in use today, is open source, scalable, and easy
62%
03.01.2013
data on the Device (GPU)
dA = gpuSetData(A);
dC = gpuSetData(C);
d1 = gpuMult(A,B);
d2 = gpuMult(dA,dC);
d3 = gpuMult(d1,d2);
result = gpuGetData(d3); // Get result on host
// Free device memory
dA
62%
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
62%
16.05.2013
,1000);
06
07 // Set host data on the Device (GPU)
08 dA = gpuSetData(A);
09 dC = gpuSetData(C);
10
11 d1 = gpuMult(A,B);
12 d2 = gpuMult(dA,dC);
13 d3 = gpuMult(d1,d2);
14 result = gpuGetData(d3); // Get
62%
27.09.2024
Design Document with Views
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"_rev": "6-856a5c52b1a9f33e136b7f044b14a8e6",
"language": "javascript",
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