34%
11.04.2016
/s wMB/s avgrq-sz ...
sdb 0.00 28.00 1.00 259.00 0.00 119.29 939.69 ...
Parallelism
Multiple computers can access enterprise storage, and multiple threads can access
34%
02.08.2021
B/s (1725kB/s-1725kB/s), io=98.9MiB (104MB), run=60118-60118msec
Disk stats (read/write):
sdf: ios=51/25253, merge=0/0, ticks=7/1913272, in_queue=1862556, util=99.90%
Listing 5
RAM Random
34%
26.02.2014
of time writing: 0 ms
sdd1 :
Number of reads: 1,544 Number of bytes: 77.75 M Read Rate: 0.00 B/s
Amount of time reading: 12,477 ms
Number of writes: 18,263 Number of bytes: 148.16 M
34%
17.02.2015
$179.00/EUR119
http://www.hardkernel.com/
Gizmo 2
Linux, Windows Embedded 8
AMD G-series GX210HA
Dual x86 @1GHz (1MB shared L2) for 85GFLOPS
AMD Radeon HD 8210E
33%
25.03.2020
---------------- -------------------- ---------------------------------------- --------- -------------------------- ---------------- --------
/dev/nvme0n1 152e778212a62015 Linux 1 21.00 TB / 21.00 TB 4 KiB + 0 B 5.4.12-0
You are now able to read and write from and to /dev
33%
04.08.2020
-slim[build]: info=image id=sha256:231d40e811cd970168fb0c4770f2161aa30b9ba6fe8e68527504df69643aa145 size.bytes=126323486 size.human=126 MB
docker-slim[build]: info=image.stack index=0 name='nginx:latest' id='sha256
33%
21.01.2020
SN Model Namespace Usage Format FW Rev
---------------- -------------------- ---------------------------------------- --------- -------------------------- ---------------- --------
/dev/nvme0n1 152e778212a62015 Linux 1 21.00 TB / 21.00 TB 4 KiB + 0 B 5.4.12-0
You are now able to read and write from and to /dev/nvme0n1
33%
29.09.2020
-amd64.tar.gz.sha256sum
[...snip]
e6be589df85076108c33e12e60cfb85dcd82c5d756a6f6ebc8de0ee505c9fd4c helm-v3.1.2-linux-amd64.tar.gz
$ sha256sum helm-v3.1.2-linux-amd64.tar.gz
e6be589df85076108c33e12e60cfb85
32%
12.09.2013
, integrated
AMD Radeon HD 6320, integrated
Integrated/SoC
Graphics memory
Up to 512MB
128MB
256MB
384MB
256MB
RAM
1GB
1GB
2GB
32%
11.05.2021
sizes (Listing 1). The second script (Listing 2) is the same as Listing 1, but uses double precision.
Listing 1: Single-Precision Square Matrix Multiply
# Example SGEMM
for N = [2, 4, 8, 16, 32, 64, 128, 256
