17%
04.04.2023
specification, denoted by the letter V. In this case, V10 once again delivers 10MBps, with the higher V60 or V90 (90MBps) intended for high-rate or high-definition video capture.
Bus specification could
17%
16.01.2023
report, "Network & Application Anomalies" was the second highest incident type at 19 percent, followed by "System Anomalies" at 11.5 percent.
The report also states that "large" organizations (>10
17%
27.09.2021
this option to ask for "one ping only, please" (Listing 1).
Listing 1
One Ping Only
$ ping -o 52.90.56.122; sleep 2; ssh ubuntu@52.90.56.122
PING 52.90.56.122 (52.90.56.122): 56 data bytes
17%
18.10.2017
laytonjb@laytonjb-Lenovo-G50-45 ~]$ pgf90 test1.f90 -o test1
[laytonjb@laytonjb-Lenovo-G50-45 ~]$ ldd test1
linux-vdso.so.1 => (0x00007fff11dc8000)
libpgf90rtl.so => /opt/pgi/linux86-64/16.10
17%
25.01.2017
Fortran 90 took Fortran 77 from the dark ages by giving it new features that developers had wanted for many years and by deprecating old features – but this was only the start. Fortran 95 added new
17%
18.06.2014
- 28 days]: 7379 ( 1.90%) ( 2.30% cumulative)
[ 28- 56 days]: 10655 ( 2.75%) ( 5.05% cumulative)
[ 56- 112 days]: 12079 ( 3.11%) ( 8.16% cumulative)
[ 112- 168 days]: 27551 ( 7.10
17%
22.08.2017
or a subset of nodes (user controllable). To achieve this, I use F2PY to build the module.
F2PY
The tool named f2py
, Fortran-to-Python, creates a connection between the two languages that calls Fortran 77/90
17%
21.12.2011
-n 256 ./smg2000 -n 90 90 90
then to run with Open|SpeedShop, one adds the convenience command and quotes around the command normally used to execute the application outside of Open
17%
12.05.2020
$(id -u):$(id -g) -e HOME=$HOME \
-e USER=$USER -v $HOME:$HOME \
--rm --read-only nvidia/cuda:10.1-base-ubuntu18.04
When running a read-only container, it likely will need to access local files (i.e., files in the container, like
17%
21.03.2017
# ===================
09 #
10 if __name__ == '__main__':
11
12 f = h5py.File("mytestfile.hdf5", "w")
13
14 dset = f.create_dataset("mydataset", (100,), dtype='i')
15
16 dset[...] = np.arange(100)
17
