15%
30.01.2020
.22253e-06| 12.67%| for k in range(0, d_num):
239| 37425000| 177.055| 4.73093e-06| 10.65%| rij[k] = pos[k,i] - pos[k,j]
240| 0| 0
15%
09.12.2019
to check follows
a, b = 1,2
c = a + b
# Code to check ends
end_time = time.time()
time_taken = (end_time- start_time)
print(" Time taken in seconds: {0} s").format(time_taken_in_micro)
If a section of code
15%
05.11.2018
Name=slurm-node-0[0-1] Gres=gpu:2 CPUs=10 Sockets=1 CoresPerSocket=10 \
ThreadsPerCore=1 RealMemory=30000 State=UNKNOWN
PartitionName=compute Nodes=ALL Default=YES MaxTime=48:00:00 DefaultTime=04:00:00 \
Max
15%
02.02.2021
.05). Once you do the math (1/0.05=20), that is the maximum possible speedup under ideal conditions (i.e., the absolute limit with infinite resources thrown at the problem).
Amdahl's Law
IBM's Gene Amdahl
15%
04.11.2011
$if | bzip2 ‑9 > $if.bz2;
04 done
05 real 0m27.005s
06 user 0m11.745s
07 sys 0m14.623s
08
09 $ time find . ‑name "*.gz" ‑print | parallel ‑j +0 'zcat {} | bzip2 ‑9 > {.}bz2'
11
12 real 0m
15%
02.02.2021
.sin(periods * 2 * np.pi * t)
12 return max(value, 0.0)
13 else:
14 value = np.sin(periods * 2 * np.pi * t)
15 return max(value, 0.0)
16
17 # building the data vector
18 my_data = []
19 i = 0
20 while
15%
28.11.2023
:
13 name: statuspage-demo
14 external: true
Listing 2
config.json
01 {
02 "version": "2.0",
03 "columns": 2,
04 "tiles": [
05 { "type": "PING", "params": {"hostname
15%
11.06.2014
_system_release = '12.04'
17 $eth0_mac = '08:00:27:c4:a1:d8'
18 $VirtInfo = {
19 virtualization_role => 'guest'
20 virtualization_type => 'virtualbox'
21 }
22 $memory_shared = '0'
23 $Network = {
24 networkdevices
15%
30.11.2020
):
11
12 s = 0.0
13 s += h * f(a)
14 for i in range(1, n):
15 s += 2.0 * h * f(a + i*h)
16 # end for
17 s += h * f(b)
18 return (s/2.)
19 # end def
20
21
22 # Main section
23 comm = MPI
15%
07.06.2019
_web latest c100b674c0b5 13 months ago 19MB
nginx alpine bf85f2b6bf52 13 months ago 15.5MB
With the image ID in hand, you can inspect the image manifest:
docker inspect bf85f2b6bf52
