6%
02.03.2024
/diskstats | grep 'sda ' | awk '{print $4+$8}')
sleep 1
curr=$(cat /proc/diskstats | grep 'sda ' | awk '{print $4+$8}')
iops=$((curr-prev))
echo "IOPS: $iops"
done
The admin tools you can code in Bash is really
6%
18.08.2021
).
Figure 3: Job summary tables from darshan-job-summary.pl output PDF.
Another group of useful tables are shown in Figure 4. The top table presents the cumulative time spent in reads and writes
6%
08.08.2022
commands.
For example, a user on a Linux system starts tty-share
(Figure 4). The URL is shared with the Windows user who then pastes it in their browser. Figure 5 is a screenshot of the Brave browser
6%
04.11.2011
Recent trends in computing are toward more cores doing more tasks at once. These days, you are likely to have a dual- or quad-core CPU in your laptop, and perhaps 4, 6, 12, or 16 cores in your
6%
07.11.2011
to the created subprocesses in a few different ways. To see these concepts in action, run the code shown in Listing 4 (miscellaneous.py
).
Listing 4: miscellaneous.py
01 #! /usr/bin/env python2.7
02
03 import
6%
24.09.2015
. The speed-up increases from 1.00 with one process to 4.71 using 64 processes. Of course, the wall clock time for the serial portion of the application does not change; it stays at 200 seconds regardless
6%
03.04.2019
that executed on the GPUs and produced massive speedups for many applications.
Starting with OpenMP 4.0, the OpenMP standard added directives that targeted GPUs and has expanded on this capability
6%
04.11.2011
Enterprise Linux Installation Guide [3] and the section on Kickstart installations [4] are important reading.
The basic steps for getting your HPC cluster up and running are as follows:
Create the admin
6%
13.06.2019
your code with auto-vectorization (so you don’t have to vectorize by hand).
Starting in OpenMP 4.0, the directive omp simd
was introduced. It basically gives a hint to the compiler to look for places
6%
19.09.2019
add_ufunc(x, y):
return x + y
The decorator line defines the data types (i.e., int64
here) and the target for the decorator cuda
. A simple test for the add_ufunc
Numba function is:
a = np.array([1, 2, 3, 4])
b
