6%
14.08.2023
-h
option because it produces human-readable output.
A second way to check that the NFS filesystem is mounted is to use the mount
command and specify the filesystem type:
$ mount -t nfs4
With this command
6%
04.12.2012
these designs.
The increase in frequency or “megahertz march,” as it was called, started to create problems when processors approached 4GHz (109Hz, or cycles per second). In simple terms, processors were getting
6%
03.07.2013
. The speed-up increases from 1.00 with 1 process to 4.71 with 64 processes. However, also notice that the wall clock time for the serial portion of the application does not change. It stays at 200 seconds
6%
12.11.2011
processing capability. So, typically, you want – it varies obviously – but you want 4 to 8 drives per node in a Hadoop cluster, and that’s typically not something you would do on an Altix ICE type of system
6%
05.12.2011
enhancements. Compiler support is being rapidly released immediately by various vendors.
Short term, we are driving to release 4.0 – tentatively, next year with content which will probably include accelerator
6%
21.12.2017
card or SIMD directives for vectorization defined in OpenMP 4.5) to create a GPU-enabled or vectorized application on many-core processors. Efficiently implementing these OpenMP concepts (or alternative
6%
18.09.2017
-02 0.622010
3 0.123543E-01 0.113207E-02 0.621986
4 0.127060E-01 0.904517E-03 0.621966
5 0.130230E-01 0.761265E-03 0.621948
6
6%
25.02.2016
). Version 3.3.1 using OpenMP was used for the example. Only the FT test (discrete 3D fast Fourier transform, all-to-all communication) was run and the Class B “size” standard test (4x size increase going from
6%
08.04.2024
pigz
for maximum compression:
$ pigz -9 file.out
By default, pigz
uses all the cores on the system. To limit the number of cores to four, use the -p
option:
$ pigz -9 -p4 file.out
To uncompress, add
6%
06.05.2014
is a development of MapReduce version 2 (MRv2). YARN is based directly on HDFS and assumes the role of a distributed operating system for resource management for big data applications (Figure 4
