14%
04.08.2020
// (c) 2020 by Federico Lucifredi
04
05 #include
06 #include
07 #include
08 #include
09 #include
10 #include
11 #include
12
13
14%
31.10.2025
18 months. And, among those reporting incidents, 18 percent put their losses at more than US$ 500,000 while another 8 percent saw losses in excess of US$1 million.
Of the respondents who experienced
14%
01.08.2019
total
x = numpy.arange(10_000_000);
%time sum(x)
CPU times: user 1.63 s, sys: 0 ns, total: 1.63 s
Wall time: 1.63 s
Next, add Numba into the code (Listing 2) so the @jit decorator can be used. (Don
14%
01.06.2024
* argv[])
08 {
09 long niter = 1000000000;
10 int myid; //holds process's rank id
11 double x,y; //x,y value for the random coordinate
12 int i;
13
14%
16.08.2018
of nodes using a parallel shell tool. However, for those that might be asking if they can use parallel shells on their 50,000-node clusters, the answer is that you can, but the time skew in the results
14%
07.10.2014
/lib/sheepdog
root 582 581 0 13:13 ? 12:00:00 AM sheep -p 7000 /var/lib/sheepdog
# grep sheep /proc/mounts
/dev/sdb1 /var/lib/sheepdog ext4 rw,relatime,data=ordered 0 0
# grep sheep /etc/fstab
/dev
14%
05.12.2014
(plyr)
06 library(ggplot2)
07
08 setwd("~/R/RFI")
09 rfi <- read.csv("rfi-extract-July2011.log",header=TRUE, sep=",")
10
11 ## read in data to frame
12 data <- data.table(rfi)
13 ct <- count
14%
15.08.2016
via 192.168.1.1 dev eth1
13 $ ip netns exec ns1 ping -c2 8.8.8.8
14 PING 8.8.8.8 (8.8.8.8) 56(84) bytes of data.
15 64 bytes from 8.8.8.8: icmp_seq=1 ttl=51 time=22.1 ms
16 64 bytes from 8.8.8.8: icmp
14%
30.11.2025
the Buffer
01 [global]
02 ioengine=libaio
03 direct=1
04 filename=testfile
05 size=2g
06 bs=4m
07
08 refill_buffers=1
09
10 [write]
11 rw=write
12 write_bw_log
13
14 [read]
15 stonewall
16 rw=read
17 write
14%
30.11.2025
c/s virtual
12
13 Benchmarking: OpenBSD Blowfish (x32) [32/64 X2]... DONE
14 Raw: 723 c/s real, 723 c/s virtual
15
16 Benchmarking: Kerberos AFS DES [48/64 4K]... DONE
17 Short: 378501 c/s real
