30%
05.02.2019
Containers, this was 1.6 seconds. The classic runC run time needs 0.6 seconds for the same task, and this value is stable even if the number of passes that can be configured in the script varies by using the N
30%
17.06.2011
,200, comprising 55 different commands, were issued. The system, a server with 768MB RAM and a Pentium 3 CPU, took a total of 22 seconds to answer them, the longest response took 32 milliseconds, the shortest
30%
10.09.2012
to logfiles, and it’s pretty simple to use:
[laytonjb@test1 ~]$ logger "This is a test"
...
[root@test1 ~]# tail -n 2 /var/log/messages
Aug 22 15:54:47 test1 avahi-daemon[1398]: Invalid query packet.
Aug 22 17
30%
07.10.2014
Amount of virtual memory used by the process
1405m
1,405MB
RES
Amount of physical memory used by the process
1.0g
1.0GB
SHR
Shared memory used
30%
18.02.2018
. The required modifications are explained in the Huginn wiki [3].
The software is based on Ruby on Rails [4] and therefore requires an installed Ruby environment; in fact, it needs version 2.2 or 2
30%
02.10.2012
port 22) to port 2222, for example, to stop port scans filling up your logs. Without TCP Wrappers enabled, scans might run dictionary attacks on your server where password combinations are guessed by one
30%
25.09.2023
: 55 ms.
Port: 80: op 2.1. 10.0.0.23 80 Time: 26 ms.
Port: 80: op 2.2. 10.0.0.23 80 Time: 56 ms.
Port: 80: op 3.1. 10.0.0.23 80 Time: 25 ms.
Port: 80: op 3.2. 10.0.0.23 80 Time: 48 ms.
Port: 80: op 4
30%
06.05.2024
module with an Intel N100, 8GB of low-power double data rate (LPDDR) memory, up to 64GB of eMMC flash storage, up to nine PCIe 3.0 lanes, up to four USB 3.2 ports, eight USB 2.0 ports, 64 GPIO pins
30%
02.02.2021
i < 1000:
21 my_data.append(data_w_weekend(i / 1000))
22 i += 1
23
24 # add some noise
25 random_gain = 0.1 # factor for the noise
26 i = 0
27 while i < 1000:
28 my_data[i] += np
30%
08.06.2021
samples from a uniform distribution over [0,1). The equation is then solved by the solve
routine.
NumPy on GPUs
NumPy functions are all single threaded unless the underlying NumPy code is multithreaded
